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Full text of "Robot Arms"


Edited by Satoru Goto 


Robot Arms 

Edited by Satoru Goto 

Published by InTech 

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Copyright © 2011 InTech 

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Preface IX 

Part 1 Model and Control 1 

Chapter 1 Modeling Identification of the Nonlinear 

Robot Arm System Using MISO NARX Fuzzy 
Model and Genetic Algorithm 3 

Ho Pham Huy Anh, Kyoung Kwan Ahn 
and Nguyen Thanh Nam 

Chapter 2 Kinematics of AdeptThree Robot Arm 21 

Adelhard Beni Rehiara 

Chapter 3 Solution to a System of Second Order Robot Arm 

by Parallel Runge-Kutta Arithmetic Mean Algorithm 39 

S. Senthilkumar and Abd Rahni Mt Piah 

Chapter 4 Knowledge-Based Control for Robot Arm 51 

Aboubekeur Hamdi-Cherif 

Chapter 5 Distributed Nonlinear Filtering Under 
Packet Drops and Variable Delays for 
Robotic Visual Servoing 77 

Gerasimos G. Rigatos 

Chapter 6 Cartesian Controllers for Tracking of Robot 

Manipulators under Parametric Uncertainties 109 

R. Garcfa-Rodrfguez and P. Zegers 

Chapter 7 Robotic Grasping of Unknown Objects 123 

Mario Richtsfeld and Markus Vincze 

VI Contents 

Chapter 8 Object-Handling Tasks Based on 

Active Tactile and Slippage Sensations 137 

Masahiro Ohka, Hanafiah Bin Yussof 
and Sukarnur Che Abdullah 

Part 2 Applications 157 

Chapter 9 3D Terrain Sensing System using Laser 

Range Finder with Arm-Type Movable Unit 159 

Toyomi Fujita and Yuya Kondo 

Chapter 10 Design of a Bio-Inspired 3D Orientation 
Coordinate System and Application in 
Robotised Tele-Sonography 175 

Courreges Fabien 

Chapter 1 1 Object Location in Closed Environments 

for Robots Using an Iconographic Base 201 

M. Pefia-Cabrera, I. Lopez-Juarez, R. Rios-Cabrera 
M. Castelan and K. Ordaz-Hernandez 

Chapter 1 2 From Robot Arm to Intentional Agent: 
The Articulated Head 215 

Christian Kroos, Damith C. Herath and Stelarc 

Chapter 13 Robot Arm-Child Interactions: A Novel Application 
Using Bio-Inspired Motion Control 241 

Tanya N. Beran and Alejandro Ramirez-Serrano 


Robot arms have been developing since 1960's, and those are widely used in industrial 
factories such as welding, painting, assembly, transportation, etc. Nowadays, the robot 
arms are indispensable for automation of factories. Moreover, applications of the robot 
arms are not limited to the industrial factory but expanded to living space or outer 
space. The robot arm is an integrated technology, and its technological elements are 
actuators, sensors, mechanism, control and system, etc. 

Hot topics related to the robot arms are widely treated in this book such as model 
construction and control strategy of robot arms, robotic grasping and object handling, 
applications to sensing system and tele-sonography and human-robot interaction in a 
social setting. 

I hope that the reader will be able to strengthen his/her research interests in robot arms 
by reading this book. 

I would like to thank all the authors for their contribution and I am also grateful to the 
InTech staff for their support to complete this book. 

Satoru Goto 

Saga University 

Model and Control 


Modeling Identification of the Nonlinear 

Robot Arm System Using MISO NARX 

Fuzzy Model and Genetic Algorithm 

Ho Pham Huy Anh 1 , Kyoung Kwan Ahn 2 and Nguyen Thanh Nam 3 

2 Ho Chi Minh City University of Technology, Ho Chi Minh City 

2 FPMI Lab, Ulsan University, S. Korea 

3 DCSELAB, Viet Nam National University 

Ho Chi Minh City (VNU-HCM) 

Viet Nam 

1. Introduction 

The PAM robot arm is belonged to highly nonlinear systems where perfect knowledge of 
their parameters is unattainable by conventional modeling techniques because of the time- 
varying inertia, hysteresis and other joint friction model uncertainties. To guarantee a good 
tracking performance, robust-adaptive control approaches combining conventional methods 
with new learning techniques are required. Thanks to their universal approximation 
capabilities, neural networks provide the implementation tool for modeling the complex 
input-output relations of the multiple n DOF PAM robot arm dynamics being able to solve 
problems like variable-coupling complexity and state-dependency. During the last decade 
several neural network models and learning schemes have been applied to on-line learning 
of manipulator dynamics (Karakasoglu et al., 1993), (Katie et al., 1995). (Ahn and Anh, 2006a) 
have optimized successfully a pseudo-linear ARX model of the PAM robot arm using 
genetic algorithm. These authors in (Ahn and Anh, 2007) have identified the PAM 
manipulator based on recurrent neural networks. The drawback of all these results is 
considered the n-DOF robot arm as n independent decoupling joints. Consequently, all 
intrinsic coupling features of the n-DOF robot arm have not represented in its recurrent NN 
model respectively. 

To overcome this disadvantage, in this study, a new approach of intelligent dynamic model, 
namely MISO NARX Fuzzy model, firstly utilized in simultaneous modeling and 
identification both joints of the prototype 2-axes pneumatic artificial muscle (PAM) robot 
arm system. This novel model concept is also applied to (Ahn and Anh, 2009) by authors. 
The rest of chapter is organized as follows. Section 2 describes concisely the genetic 
algorithm for identifying the nonlinear NARX Fuzzy model. Section 3 is dedicated to the 
modeling and identification of the 2-axes PAM robot arm based on the MISO NAR Fuzzy 
model. Section 4 presents the experimental set-up configuration for MISO NARX Fuzzy 
model-based identification. The results from the MISO NARX Fuzzy model-based 
identification of the 2-axes PAM robot arm are presented in Section 5. Finally, in Section 6 a 
conclusion remark is made for this paper. 

Robot Arms 

2. Genetic algorithm for NARX Fuzzy Model identification 

The classic GA involves three basic operations: reproduction, crossover and mutation. As to 

derive a solution to a near optimal problem, GA creates a sequence of populations which 

corresponds to numerical values of a particular variable. Each population represents a 

potential solution of the problem in question. Selection is the process by which chromosomes 

in population containing better fitness value having greater probability of reproducing. In this 

paper, the roulette-wheel selection scheme is used. Through selection, chromosomes encoded 

with better fitness are chosen for recombination to yield off-springs for successive generations. 

Then natural evolution (including Crossover and Mutation) of the population will be 

continued until a desired termination or error criterion achieved. Resulting in a final 

generation contained of highly fitted chromosomes represent the optimal solution to the 

searching problems. Fig. 1 shows the procedure of conventional GA optimization. 

It needs to tune following parameters before running the GA algorithm: 

D: number of chromosomes chosen for mating as parents 

N : number of chromosomes in each generation 

Lt. number of generations tolerated for no improvement on the value of the fitness before 

MGA terminated 

L e : number of generations tolerated for no improvement on the value of the fitness before 

the extinction operator is applied. It need to pay attention that L e {{L t . 

p : portion of chosen parents permitted to be survived into the next generation 

q: percentage of chromosomes are survived according to their fitness values in the extinction 


The steps of MGA-based NARX Fuzzy model identification procedure are summarized as: 

Step 1. Implement tuning parameters described as above. Encode estimated parameters 

into genes and chromosomes as a string of binary digits. Considering that 

parameters lie in several bounded region rjk 

IwJ <?j t for k-l,...,h. (1) 

The length of chromosome needed to encode Wk is based on ijk and the desired accuracy 8k. 
Set i=k=m=0. 

Step 2. Generate randomly the initial generation of N chromosomes. Set i-i+1. 
Step 3. Decode the chromosomes then calculate the fitness value for every chromosome of 
population in the generation. Consider F ax the maximum fitness value in the i lh 

Step 4. Apply the Elitist strategies to guarantee the survival of the best chromosome in 
each generation. Then apply the G-bit strategy to this chromosome for improving 
the efficiency of MGA in local search. 
Step 5. 

1. Reproduction: In this paper, reproduction is set as a linear search through roulette wheel 
values weighted proportional to the fitness value of the individual chromosome. Each 
chromosome is reproduced with the probability of 


Modeling Identification of the Nonlinear 

Robot Arm System Using MISO NARX Fuzzy Model and Genetic Algorithm 

Fig. 1. The flow chart of conventional GA optimization procedure. 

Robot Arms 

Fig. 2. The flow chart of the modified GA optimization procedure. 

Modeling Identification of the Nonlinear 

Robot Arm System Using MISO NARX Fuzzy Model and Genetic Algorithm 7 

with j being the index of the chromosome (j=l,...,N). Furthermore, in order to prevent some 
strings possess relatively high fitness values which would lead to premature parameter 
convergence, in practice, linear fitness scaling will be applied. 

2. Crossover: Choose D chromosomes possessing maximum fitness value among N 
chromosomes of the present gene pool for mating and then some of them, called p best 
chromosomes, are allowed to survive into the next generation. The process of mating D 
parents with the crossover rate p c will generate (N-p) children. Pay attention that, in the 
identification process, it is focused the mating on parameter level rather than on 
chromosome level. 

3. Mutation: Mutate a bit of string ( 1 ) with the mutation rate P m . 
Step 6. Compare if F ax = F^, then k-k+1, m=m+l ; otherwise, k=0 and m=0. 

Step 7. Compare if k=L e , then apply the extinction strategy with k-0. 

Step 8. Compare if m=L t/ then terminate the MGA algorithm; otherwise go to Step 3. 

Fig. 2 shows the procedure of modified genetic algorithm (MGA) optimization. 

3. Identification of the 2 -Axes PAM robot arm based on MISO NARX fuzzy 

3.1 Assumptions and constraints 

Firstly, it is assumed that symmetrical membership functions about the y-axis will 
provide a valid fuzzy model. A symmetrical rule-base is also assumed. Other constraints 
are also introduced to the design of the MISO NARX Fuzzy Model (MNFM). 

• All universes of discourses are normalized to lie between -1 and 1 with scaling 
factors external to the DNFM used to give appropriate values to the input and output 

• It is assumed that the first and last membership functions have their apexes at -1 and 1 
respectively. This can be justified by the fact that changing the external scaling 
would have similar effect to changing these positions. 

• Only triangular membership functions are to be used. 

• The number of fuzzy sets is constrained to be an odd integer greater than unity. In 
combination with the symmetry requirement, this means that the central membership 
function for all variables will have its apex at zero. 

• The base vertices of membership functions are coincident with the apex of the 
adjacent membership functions. This ensures the value of any input variable is a 
member of at most two fuzzy sets, which is an intuitively sensible situation. It also 
ensures that when a variable's membership of any set is certain, i.e. unity, it is a 
member of no other sets. 

Using these constraints the design of the DNFM input and output membership 
functions can be described using two parameters which include the number of 
membership functions and the positioning of the triangle apexes. 

3.2 Spacing parameter 

The second parameter specifies how the centers are spaced out across the universe of 
discourse. A value of one indicates even spacing, while a value larger than unity 
indicates that the membership functions are closer together in the center of the range and 
more spaced out at the extremes as shown in Fig.3. The position of each center is 


Robot Arms 

calculated by taking the position the centre would be if the spacing were even and by 
raising this to the power of the spacing parameter. For example, in the case where there 
are five sets, with even spacing (p =1) the center of one set would be at 0.5. If p is modified 
to two, the position of this center moves to 0.25. If the spacing parameter is set to 0.5, 
this center moves to (0.5) 05 = 0.707 in the normalized universe of discourse. Fig. 3 presents 
Triangle input membership function with spacing factor = 0.5. 

Input variable with 
Number of MF=7 & Scaling Factor=0.5 



' . i 






1 \/ 

— i - -A- - 






I : \ll_1 


1 1 1 
1 1 1 

1 1 1 





-0.8 -0.6 -0.4 -0.2 0.2 0.4 0.6 0.£ 

Input discourse 

Fig. 3. Triangle input membership function with spacing factor = 0.5. 

3.3 Designing the rule base 

As well as specifying the membership functions, the rule-base also needs to be 
designed. Again idea presented by Cheong in was applied. In specifying a rule base, 
characteristic spacing parameters for each variable and characteristic angle for each output 
variable are used to construct the rules. 

Certain characteristics of the rule-base are assumed in using the proposed construction 

• Extreme outputs more usually occur when the inputs have extreme values while mid- 
range outputs generally are generated when the input values are mid-range. 

• Similar combinations of input linguistic values lead to similar output values. 

Using these assumptions the output space is partitioned into different regions 
corresponding to different output linguistic values. How the space is partitioned is 
determined by the characteristic spacing parameters and the characteristic angle. The angle 
determines the slope of a line through the origin on which seed points are placed. 
The positioning of the seed points is determined by a similar spacing method as was used to 
determine the center of the membership function. 

Grid points are also placed in the output space representing each possible combination 
of input linguistic values. These are spaced in the same way as before. The rule-base is 
determined by calculating which seed-point is closest to each grid point. The output 
linguistic value representing the seed-point is set as the consequent of the antecedent 
represented by the grid point. This is illustrated in Fig. 4a, which is a graph showing 
seed points (blue circles) and grid-points (red circles). Fig. 4b shows the derived rule 
base. The lines on the graph delineate the different regions corresponding to different 
consequents. The parameters for this example are 0.9 for both input spacing parameters, 1 
for the output spacing parameter and 45° for the angle theta parameter. 

Modeling Identification of the Nonlinear 

Robot Arm System Using MISO NARX Fuzzy Model and Genetic Algorithm 



Fig. 4a. The Seed Points and the Grid Points for Rule-Base Construction 

N. e 

de \ 




































Fig. 4b. Derived Rule Base. 

3.4 Parameter encoding 

To run a MGA, a suitable encoding for each of the parameters and bounds for each of them 
needs to be carefully decided. For this task the parameters given in Table 1 are used 
with the shown ranges and precisions. Binary encoding is used as it is felt that this allows 
the MGA more flexible to search the solution space more thoroughly. The numbers of 
membership functions are limited to the odd integers inclusive between (3 - 9) in case 
MGA-based PAM robot arm Inverse and Forward TS fuzzy model and between (3-5) in case 
MGA-based PAM robot arm Inverse and Forward NARX Fuzzy model identification. 
Experimentally, this was considered to be a reasonable constraint to apply. The advantage 
of doing this is that this parameter can be captured in just one to two bits per variable. 
For the spacing parameters, two separate parameters are used. The first, within the range 
[0.1- 1.0], determines the magnitude and the second, which takes only the values -1 or 
1, is the power by which the magnitude is to be raised. This determines whether the 
membership functions compress in the center or at the extremes. Consequently, each 
spacing parameter obtains the range [0.1 - 10]. The precision required for the magnitude is 
0.01, meaning that 8 bits are used in total for each spacing parameter. The scaling for the 


Robot Arms 

input variables is allowed to vary in the range [0 - 100], while that of the output variable is 
given the range [0 - 1000]. 




No. of Bits 

Number of Membership Functions 




Membership Function Spacing 




Membership Function 




Rule-Base Scaling 




Rule-Base Spacing 




Input Scaling 




Output Scaling 

- 1000 



Rule-Base Angle 




Table 1. MGA-based Inverse and Forward NARX Fuzzy Model Parameters used for 

3.5 Inverse and forward MISO NARX fuzzy models of the 2-Axes PAM robot arm 

The newly proposed Inverse and Forward MISO NARX Fuzzy model of the PAM robot arm 
presented in this paper is improved by combining the extraordinary predictive and adaptive 
features of the Nonlinear Auto-Regressive with eXogenous input (NARX) model structure. 
The resulting model established a nonlinear relation between the past inputs and outputs 
and the predicted output, the system prediction output is combination of system output 
produced by real inputs and system historical behaviors. It can be expressed as: 

y{k) = f{y{k-'L),-,y{k-n l ),u(k-n a ),...,u(k-n i -n i )) 


Here, n a and n& are the maximum lag considered for the output, and input terms, 

respectively, n& is the discrete dead time, and /represents the mapping of fuzzy model. 

The structure of the newly proposed MISO NARX TS fuzzy model is that this MISO NARX 

TS fuzzy model interpolates between local linear, time-invariant (LTI) ARX models as 


Rulej: if Zi(k) is Ay and ... and z„(k) is A„j then 

y(fc)=Z«M fc -0 + Z l 'M fc - J '->O +c ' 


where the element of z(k) "scheduling vector" are usually a subset of the x(k) regressors that 
contains the variables relevant to the nonlinear behaviors of the system, 

Z(k)e{y(k-l),...,y(k-n,),u(k-n d ),...,u(k -«„-«,)} 
while the fj(q(k)) consequent function contains all the regressor q(k)=[X(k) 1], 


/,(#)) = X«M*-0 + Hb>u(k- i-n,)- 


Modeling Identification of the Nonlinear 

Robot Arm System Using MISO NARX Fuzzy Model and Genetic Algorithm 







2-Axes PAM 
Robot Arm 

v Ul(k) 















\ TU2(k) 

Fig. 5. Block diagrams of The MGA-based 2- Axes PAM robot arm Inverse MISO Fuzzy 
Model Identification. 


Robot Arms 

Fig. 6. Block diagrams of The MGA-based 2- Axes PAM robot arm Forward MISO Fuzzy 
Model Identification. 

In the simplest case, the NARX type zero-order TS fuzzy model (singleton or Sugeno fuzzy 
model which is not applied in this paper) is formulated by simple rules consequents as: 
Rulej: if Zi(k) is Ay and ... and z„(k) is A n ,j then 

Modeling Identification of the Nonlinear 

Robot Arm System Using MISO NARX Fuzzy Model and Genetic Algorithm 


y(fc) = c' 

with the z(k) contains all inputs of the NARX model: 

Z{k) = X(k) = {y(k-l),...,y(k-n),u(k-n l ),...,u{k-n i -n J )} 



Thus the difference between NARX fuzzy model and Fuzzy TS model method is that the 
output from Inverse TS fuzzy model is linear and constant, and the output from Inverse 
NARX fuzzy model is NARX function. But they have same fuzzy inference structure (FIS). 
The block diagrams presented in Fig. 5a and Fig. 5b illustrate the difference between the 
MGA-based PAM robot arm Inverse MISO TS Fuzzy model and the MGA-based PAM robot 
arm Inverse MISO NARX Fuzzy model identification. Forwardly, the block diagrams 
presented in Fig. 5b and Fig.5c illustrate the difference between the MGA-based PAM robot 
arm Inverse MISO NARX11 Fuzzy model identification and Inverse MISO NARX22 Fuzzy 
model identification. 

Likewise, the block diagrams presented in Fig. 6a and Fig. 6b illustrate the difference 
between the MGA-based PAM robot arm Forward MISO TS Fuzzy model and the MGA- 
based PAM robot arm Forward MISO NARX Fuzzy model identification. Forwardly, the 
block diagrams presented in Fig. 6b and Fig. 6c illustrate the difference between the MGA- 
based PAM robot arm Forward MISO NARX11 Fuzzy model identification and Forward 
MISO NARX22 Fuzzy model identification. 

4. Identification of inverse and forward MISO NARX fuzzy models 

The schematic diagram of the prototype 2-Axes PAM robot arm and the block diagram of 
the experimental apparatus are shown in Fig.7 and Fig. 8. 

Wonder Box 
Controller Kit 

Fig. 7. General configuration of 2- axes PAM robot arm 


Robot Arms 

Fig. 8. Working principle of the 2-axes PAM robot arm. 

In general, the procedure which must be executed when attempting to identify a dynamical 
system consists of four basic steps (Fig. 9). 

To realize Step 1, Fig. 10 presents the PRBS input applied simultaneously to the 2 joints of 
the tested 2-axes PAM robot arm and the responding joint angle outputs collected from both 
of them. This experimental PRBS input-output data is used for training and validating not 
only the Forward MISO NARX Fuzzy model (see Fig. 10a) but also for training and 
validating the Inverse MISO NARX Fuzzy model (see Fig. 10b) of the whole dynamic two- 
joint structure of the 2-axes PAM robot arm. 

PRBS input and Joint Angle output from (40-80)[s] will be used for training, while PRBS 
input and Joint Angle output from (0-40)[s] will be used for validation purpose. The range 
(4.3 - 5.7) [V] and the shape of PRBS voltage input applied to the 1 st joint as well as the range 
(4.5 - 5.5) [V] and the shape of PRBS voltage input applied to rotate the 2 nd joint of the 2-axes 
PAM robot arm is chosen carefully from practical experience based on the hardware set-up 
using proportional valve to control rotating joint angle of both of PAM antagonistic pair. 
The experiment results of 2-axes PAM robot arm position control prove that experimental 
control voltages uj(t) and ui(t) applied to both of PAM antagonistic pairs of the 2-axes PAM 
robot arm is to function well in these ranges. Furthermore, the chosen frequency of PRBS 
signal is also chosen carefully based on the working frequency of the 2-axes PAM robot arm 
will be used as an elbow and wrist rehabilitation device in the range of (0.025 - 0.2) [Hz]. 

Modeling Identification of the Nonlinear 

Robot Arm System Using MISO NARX Fuzzy Model and Genetic Algorithm 


5. Experiment results 

Three different identification models were carried out, which include MGA-based 2-axes 
PAM robot arm's MISO UUdot fuzzy model identification, MGA-based 2-axes PAM robot 
arm's MISO NARX11 fuzzy model identification, and MGA-based PAM 2-axes robot arm's 
MISO NARX22 fuzzy model identification, respectively. 

getting training data) 






Not Accepted 

Fig. 9. MISO NARX Fuzzy Model Identification procedure 






\ i. ! i 


- lJ L p ■_! !j U ■ 1 L ! 


nput " 

■ f ! j | p 

PRBS input T 

2 5 

.] [j L_i 

iJ 1^ 

Li Li i_J 

Li ! — 1 L — ! iJ _ 

20 30 

20 30 

t [sec] 

10 20 30 40 50 GO 70 8C 

- Yref OUTPUT I 


J v_ 

10 20 30 40 50 60 

t [sec] 



10 20 30 40 10 20 30 4( 

t [sec] t [sec] 

Fig. 10a. Forward MISO NARX Fuzzy Model Training data obtained by experiment. 


Robot Arms 

10 20 30 40 10 20 30 4C 

t [sec] 

t [sec] 

Fig. 10b. Inverse MISO NARX Fuzzy Model Training data obtained by experiment. 

5.1 MGA-based 2-axes PAM robot arm forward MISO NARX fuzzy model identification 

The identification procedure bases on the experimental input-output data values measured 
from the 2-axes PAM robot arm. Table 1 tabulates fuzzy model parameters used for 
encoding as optimized input values of MGA optimization algorithm. The range (3-5) 
permits the variable of number of membership functions obtaining 2 different odd values 
would be chosen by MGA (3 and 5). Block diagrams in Fig. 5a, Fig.5b and Fig.5c illustrate the 
MGA-Based 2-axes PAM robot arm's forward MISO Fuzzy model identification. 
The fitness value of MGA-based optimization calculated based on Eq. (8) is presented in Fig. 
11 (with population = 40 and generation = 150). 

F,=^-{-Y.(y( k )-y,( k )fr 


This Figure represents the fitness convergence values of both Forward Fuzzy models of both 
joints of the 2-axes PAM robot arm corresponding to three identification methods. This Figure 
shows that the fitness value of Forward MISO UUdot fuzzy model falls early at 10 th 
generation into a local optimal trap equal 1050 with joint 1 and 1250 with joint 2. The reason is 
that UUdot fuzzy model can't cover nonlinear features of the 2-axes PAM robot arm implied 
in input signals U [v] and Udot [v/s]. On the contrary, the fitness value of Forward MISO 
NARX fuzzy model obtains excellently the global optimal value (equal 2350 with joint 1 and 
12600 with joint 2 in case of Forward MISO NARX11 fuzzy model and equal 9350 with joint 1 
and 10400 with joint 2 in case of Forward MISO NARX22 fuzzy model). The cause is due to 
novel Forward MISO NARX fuzzy model combines the extraordinary approximating capacity 
of fuzzy system with powerful predictive and adaptive potentiality of the nonlinear NARX 
structure implied in Forward NARX Fuzzy Model. Consequently, resulting Forward MISO 
NARX11 and Forward MISO NARX22 fuzzy model as well cover excellently most of nonlinear 
features of the 2-axes PAM robot arm implied in input signals L7(zJ[v] and Y(z-l) [deg]. 
Consequently, the validating result of the MGA-based identified 2-axes PAM robot arm's 
Forward MISO NARX fuzzy model presented in Fig. 12 also shows a very good range of 
error (< [ ±5" ] with joint 1 and < [ +1" ] with joint 2 in case of Forward MISO NARX11 fuzzy 

Modeling Identification of the Nonlinear 

Robot Arm System Using MISO NARX Fuzzy Model and Genetic Algorithm 


■ Forward UUdot Fuzzy Model 

- Forward NARX11 Fuzzy Model 

■ Forward NARX22 Fuzzy Model 

1000 £•/—■ 

■ Forward UUdot Fuzzy Model 
• Forward NARX1 1 Fuzzy Model 
- Forward NARX22 Fuzzy Model 



Fig. 11. Fitness Convergence of MGA-based Forward MISO Fuzzy Model optimization of 
the 2-axes PAM robot arm 





S 5 




=1 PRBS U1(z) INPUT h 



D 5 10 







i I 








"I I ' 'I 







T~ 1 

. ! T ! 

20 25 30 35 40 

-■-Joint A 

Fig. 12. Validation of MGA-based Forward MISO Fuzzy Model of the 2-axes PAM robot arm 

model and <[ ±3" ] with joint 1 and <[ ±2.5" ] with joint 2 in case of Forward MISO NARX22 
fuzzy model). These results are very impressive in comparison with Forward MISO UUdot 
fuzzy model (error > [ ±10" ] for both joints. 

These results assert the outstanding potentiality of the novel proposed MISO NARX fuzzy 
model not only in modeling and identification but also in advanced control application 
as well. 


Robot Arms 

5.2 MGA-based 2 -axes PAM robot arm Inverse MISO NARX fuzzy model identification 

The identification procedure bases on the experimental input-output data values measured 
from the 2-axes PAM robot arm. Table 1 tabulates fuzzy model parameters used for 
encoding as optimized input values of MGA optimization algorithm. The range (3-5) 
permits the variable of number of membership functions obtaining 2 different odd values 
would be chosen by MGA (3 and 5). Block diagrams in Fig. 6a, Fig. 6b and Fig. 6c illustrate the 
MGA-Based 2-axes PAM robot arm's Inverse MISO Fuzzy model identification. 



_ Inverse NARX11 Fuzzy Model 

Inverse NARX22 Fuzzy Model 



i \j i i i j~r~- 
i IT 1 if - -i 1 

I j_ /-' .... 

«*+7-r.7.7Z I I I. I. I. I. I. 

e UUdol Fuzzy Model 
s NARX11 Fuzzy Model 
e NARX22 Fuzzy Model 

"I r ~ 

- I j — 

,— 'i 

70 80 90 100 

Fig. 13. Fitness Convergence of MGA-based Inverse MISO Fuzzy Model optimization of the 
2-axes PAM robot arm 

The fitness value of MGA-based optimization calculated based on equation (8) is presented 
in Fig. 13 (with population = 40 and generation = 100). This Figure represents the fitness 
convergence values of both Inverse Fuzzy models of both joints of the 2-axes PAM robot 
arm corresponding to three different identification methods. This Figure shows that the 
fitness value of Inverse MISO UUdot fuzzy model falls early (at 8 th generation with joint-1 
and 48 th generation with joint-2) into a local optimal trap equal 5250 with joint 1 and 7480 
with joint 2. The reason is that UUdot fuzzy model seems impossible to learn nonlinear 
features of the 2-axes PAM robot arm implied in input signals U [deg] and Udot [deg/s]. On 
the contrary, the fitness value of Inverse MISO NARX fuzzy model obtains excellently the 
global optimal value (equal 485000 with joint 1 and 676000 with joint 2 in case of Inverse 
MISO NARX11 fuzzy model and equal 235000 with joint 1 and 98400 with joint 2 in case of 
Inverse MISO NARX22 fuzzy model). The cause is due to proposed Inverse MISO NARX 
fuzzy model combines the extraordinary approximating capacity of fuzzy system with 
powerful predictive and adaptive potentiality of the nonlinear NARX structure implied in 
Inverse NARX Fuzzy Model. Consequently, MGA-based Inverse MISO NARX11 and 
Inverse MISO NARX22 fuzzy model as well cover excellently all of nonlinear features of the 
2-axes PAM robot arm implied in input signals 17 (z) [deg] and Y(z-l) [V]. 

Consequently, the validating result of the MGA-based identified 2-axes PAM robot arm's 
Inverse MISO NARX fuzzy model presented in Fig. 14 also shows a very good range of error 
(< [ +0.1[V] ] with joint 1 and < [ +0.05[V] ] with joint 2 in case of Inverse MISO NARX11 
fuzzy model and < [ ±0.15[V] ] with joint 1 and < [ +0.3[V] ] with joint 2 in case of Inverse 
MISO NARX22 fuzzy model). These results are very impressive in comparison with Inverse 
MISO UUdot fuzzy model (error > [+1[V]] with joint 1 and > [+0.5[V]] with joint 2 

Modeling Identification of the Nonlinear 

Robot Arm System Using MISO NARX Fuzzy Model and Genetic Algorithm 




A A t: !■ A 

-Udot1(z) input 

I ° 

i/'i rV 

V "ii » 


r- •V"~"' v *~y 


J y j V*^y 


E 5 

PRBS Y1(z-1) input N 

U U U-< 

- PRBS Y2(z-1) input 

U U L 

15 20 


— — - Inverse UUdot Fuzzy Model 
NARX11 Fuzzy Model 
- Inverse NARX22 Fuzzy Model 

4 Myf^- -WfVi- -flfe 


Inverse UUdot Fuzzy Model 
Inverse NARX11 Fuzzy Model 
Inverse NARX22 Fuzzy Model 

,^^w^.p M 4vi» j ^^% = 


UUdot Fuzzy Model 
NARX11 Fuzzy Model 
Inverse NARX22 Fuzzy Model 


■ Inverse UUdot Fuzzy Model 
- Inverse NARX11 Fuzzy Model 
Inverse NARX22 Fuzzy Model 

^te? — n^fe-H"- 

Fig. 14. Validation of MGA-based Inverse MISO Fuzzy Model of the 2-axes PAM robot arm 

These results assert the outstanding potentiality of the novel proposed Forward and Inverse 
MISO NARX fuzzy model not only in modeling and identification of the 2-axes PAM robot 
arm but also in advanced control application of nonlinear MIMO systems as well. 

6. Conclusion 

In this study, a new approach of MISO NARX Fuzzy model firstly utilized in modeling and 
identification of the prototype 2-axes pneumatic artificial muscle (PAM) robot arm system 
which has overcome successfully the nonlinear characteristic of the prototype 2-axes PAM 
robot arm and resulting Forward and Inverse MISO NARX Fuzzy model surely enhance the 
control performance of the 2-axes PAM robot arm, due to the extraordinary capacity in 
learning nonlinear characteristics and coupled effects as well of MISO NARX Fuzzy model. 
Results of training and testing on the complex dynamic systems such as PAM robot arm show 
that the newly proposed MISO NARX Fuzzy model which is trained and optimized by 
modified genetic algorithm presented in this study can be used in online control with better 
dynamic property and strong robustness. This resulting MISO NARX Fuzzy model is quite 
suitable to be applied for the modeling, identification and control of various plants, including 
linear and nonlinear process without regard greatly changing external environments. 

7. Acknowledgements 

This research was supported by the DCSELAB - Viet Nam National University Ho Chi Minh 
City (VNU-HCM) and the NAFOSTED, Viet Nam. 

20 Robot Arms 

8. References 

Ahn K.K., Anh H.P.H, 2006. System modeling and identification of the two-link pneumatic 

artificial muscle (PAM) manipulator optimized with genetic algorithm. In: 

Proceedings of the 2006 IEEE-ICASE Int. Conf., Busan, Korea, pp. 356-61. 
Ahn K.K., Anh H.P.H, 2007. A new approach of modeling and identification of the pneumatic 

artificial muscle (PAM) manipulator based on recurrent neural network. In Proc. 

IMechE, Part I: Journal of Systems and Control Engineering, 2007, 221(18), 1101-1122. 
Ahn K.K., Anh H.P.H., 2009. Identification of the pneumatic artificial muscle manipulators 

by MGA-based nonlinear NARX fuzzy model. In MECHATRONICS, 2009, Volume 

19, Issue 1, pp. 106-133. 
Ahn K.K., Thanh T.D.C., 2005. Nonlinear PID control to improve the control performance of 

PAM manipulators using neural network. In KSME, Int., Jour., 19(l):pp.l06~15. 
Balasubramanian K, Rattan K.S., 2003a. Fuzzy logic control of a pneumatic muscle system 

using a linearizing control scheme. In: Proceedings of Int. Conf., North American 

Fuzzy Information Processing Society, pp. 432-6. 
Chan S.W., Lilly J., Berlin J.E., May 2003. Fuzzy PD+I learning control for a pneumatic muscle. 

In: Proceedings of IEEE Int. Conf. Fuzzy Systems, St. Louis, MO, pp. 278-83. 
Hesselroth T, Sarkar K, Van der Smagt P, Schulten K., 1994. Neural network control of a 

pneumatic robot arm. IEEE Trans. System Man Cybernetics 24(1): pp. 28-38. 
Karakasoglu,A., Sudharsanan, S.I. and Sundareshan,M.K., "Identification and decentralized 

adaptive control using dynamical neural networks with application to robotic 

manipulators," IEEE Trans, on neural networks, Vol. 4, No. 6, (Nov. 1993), pp. 919-930. 
Katie, D.M. and Vukobratovic, M.K., "Highly efficient robot dynamics learning by 

decomposed connectionist feed-forward control structure," IEEE Trans, on syst. man 

and cybern., Vol. 25, No. 1, (1995), pp. 145-158. 
Lilly J., Sep. 2003Adaptive tracking for pneumatic muscle actuators in bicep and tricep 

configurations. IEEE Trans. Neural Syst. Rehabil. Eng. ll(3):pp.333-9. 
Lilly J.H., Chang X., Sep. 2003. Tracking control of a pneumatic muscle by an evolutionary 

fuzzy controller. In IEEE Intell. Automat. Soft Comput., 9(3):pp. 227-44. 
Medrano-Cerda G.A., Bowler C.J., Caldwell D.G., Aug. 1995. Adaptive position control of 

antagonistic pneumatic muscle actuators. In: Proceedings of IEEE Int. Conf. 

Intelligent Robots and Systems, Pittsburgh, PA, pp. 378-83. 
Nelles O., Nonlinear system identification, Springer, 2000. 
Repperger D.W., Johnson K.R., Phillips C.A., 1998. VSC position tracking system involving a 

large scale pneumatic muscle actuator. In: Proceedings of IEEE Conf. Decision 

Control, Tampa, FL, Dec. pp. 4302-7. 
Repperger D.W., Phillips C.A., Krier M., Aug. 1999. Controller design involving gain 

scheduling for a large scale pneumatic muscle actuator," In: Proceedings of IEEE 

Conf. Control Applications, Kohala Coast, HI, pp. 285-90. 
Reynolds D.B., Repperger D.W., Phillips C.A., Bandry G., 2003. Dynamic characteristics of 

pneumatic muscle. In IEEE Ann. Biomed. Eng., 31(3):pp. 310-7. 
Tsagarakis N, Darwin G.C., 2000. Improved modeling and assessment of pneumatic muscle 

actuators. In: Proceedings of IEEE Int. Conf. Robotics and Automation, San 

Francisco, CA, pp. 3641-6. 

Kinematics of 
AdeptThree Robot Arm 

Adelhard Beni Rehiara 

University of Papua 

1. Introduction 

Robots are very powerful elements of today's industry. They are capable of performing 
many different tasks and operations precisely and do not require common safety and 
comfort elements humans need. However, it takes much effort and many resources to make 
a robot function properly. Most companies that made industrial robots can be found in the 
market such as Adept Robotics, Staubli Robotics and Fanuc Robotics. As a result, there are 
many thousands of robots in industry. 

An AdeptThree robot arm is a selectively compliant assembly robot arm (SCARA) 
manufactured by the Adept Company. In general, traditional SCARA's are 4-axis robot arms 
within their work envelope. They have the jointed two-link arm layout similar to our human 
arms and commonly used in pick-and-place, assembly, and packaging applications. As a 
SCARA robot, an AdeptThree robot has 4 joints which denote that it has 4 degree of 
freedom (DOF). The robot has been designed with completed components including 
operating system and programming language namely V+ (Rehiara and Smit, 2010). 
In robotic, there are two important studies which are kinematics and dynamics studies. 
Robot kinematics is the study of robot motion without regards to the forces that result it. On 
the other hand, the relationship between motion, and the associated forces and torques is 
studied in robot dynamics. In this chapter, kinematics problem for an AdeptThree robot will 
be explained in detail. 

2. AdeptThree robot system 

An AdeptThree robot is a 4-axis SCARA robot which is designed for assembling and part- 
handling tasks. The body of the robot is too big compared to the most SCARAs but it has 
strength and rigidity to carry a load about 25 kg (55 lb) as its maximum payload. For the 
working envelope, it has a 1067 mm maximum radial that can make more than two meters 
in diameter and also it has 305 mm Z-axis stroke. Fig. 1 shows the physical system of an 
AdepthThree robot arm. All of the figures in this section are provided by Adept company 

As manufactured by Adept Company, AdeptThree robot is designed to be compatible with 
the other Adept products either the Adept MC or the Adept CC controller interface. All of 
the control and operation of the AdeptThree robot are programmed through the selected 
controller. In this case, the robot is using the Adept MC controller. 


Robot Arms 

Joint 2 Lif* 



Joint 1 


Joint 3 and 
Joint 4 



User Flange 

Base Enclosure 

(a) (b) 

Fig. 1. (a) Physics of an AdeptThree Robot Arm and (b) Joints and links names 

2.1 Joints motion 

An AdeptThree robot has 4 joints which are linked to the robot. Joint 3 is a translational joint 
which can move along Z-axis while joint 1, 2, and 4 are rotational joints. Working envelope 
of the robot is shown in fig.2 (b). 

First joint is the base joint and it is also called"the shoulder" as its function looks like a 
human shoulder. In this joint, the rotational movement of the inner link and the column will 
be provided. The joint has a maximum movement of about 300° that can be separated in 150° 
to the left and 150° to the right as in fig.2 (a). 

Fig. 2. (a) I s ' joint motion and (b) working envelope 

Second joint is called "the elbow" as its function looks like a human elbow. In this joint, both 
the outer link and inner link are linked. Furthermore this joint is similar to 1 st joint, the 
maximum movement of the joint is also about 300°. 

Kinematics of AdeptThree Robot Arm 




Joint 3 


Joint 4 

Fig. 3. (a) 2 nd joint, (b) 3 rd and 4 th joint movements 


Figure 3(a) shows the movement of the 2 nd joint. In order to avoid any ambiguity to 
program the robot, the robot can be programmed to move like a human left or right arm by 
using the syntax "LEFTY" or "RIGHTY". 

Third joint is placed at the end of the outer link. It has a maximum stroke of about 12 inches 
or 30.5 cm. Fig. 3(b) shows the 3 rd joint and also 4 th joint. 

Fourth joint is also called the "the wrist". The joint can be moved over a range of 540°. Its 
function is similar to a human wrist and it can be rotated as a human hand to tighten a bolt 
or unscrew a screw. 

Although the AdeptThree robot has the widest working envelope, it still has a limitation. 
The limitation is about the travelling of each joint and it was built to avoid the damage of 
the robot. The maximum joint travel is confined by soft-stop and hard-stop. Soft-stop and 
hard stop occur when the joint is expected to pass the limit angle. While both stops happen, 
robot power will be turned off. 

Soft-stop can be a programmed cancellation and it requires the robot arm to be moved 
manually into its working envelope. After the arm into the working envelope, the robot arm 
can be used directly without any other setting. On the other hand, the action of the hard- 
stop is to cancel all of the robot operations and it requires to move the robot manually by 
using the manual control pendant (MCP) to its working area. 

2.2 Operating system 

The AdeptThree robot has its own operating system called V+ that also can recognize some 
syntaxes in programming the robot. As an operating system, the V+ can handle all of the 
system operations. The programming language in the robot operating system is a high level 
programming language. It is similar to C or Pascal programming and it can transfer 
syntaxes to machine language. 

The V+ real-time and multi-tasking operating system manages all system level operations, 
such as input/ output (I/O), program execution, task management, memory management 
and disk file operations. As a programming language, V+ has a rich history and has evolved 
into the most powerful, safe and predictable, robot programming language available today. 
V+ is the only language to provide an integrated solution to all of the programming needs in 
a robotic work cell, including safety, robot motion, vision operations, force sensing and I/O. 


Robot Arms 

In general, the syntaxes using by V+ can be categorized into 4 parts: 

• Monitor command, it can be used directly by typing it one by one. 

• Program command, it will be run if it is used in a program lines. 

• Real-time command, it only can be run in a program. 

• String command, it is used to handle all operations with string variable. It can be used 
in monitor and program command. 

2.3 Robot setup 

Before using the robot, it is needed to be booted by using its operation system V+. The 
booting screen of the Adept + is placed in fig. 4. Dot (.) command in the last line means that 
the robot is ready to be commanded by applying the V+ syntaxes. 

Adept V+ 

Copyright (c) 1984, 1985, 1986, 1987 
By Adept Technology, Inc. All rights 

1988, 19B9, 






Adept External Encoder Module. 
Adept SCAR?. Robot Control Module. 

Software : 10.4 1917-301 2960 
Controller : 310-2960-3 2048 Kb 
Robot 1 : 640-319-10 1 

Fig. 4. Adept V+ booting screen 

As shown in fig.4, the robot consists of some modules which are software (V+ version 10.4), 
controller module and a robot arm. Unlike most computers, the controller does not have 
BIOS (basic input output system) memory; therefore the robot time needs to be changed 
with the actual time every time after tuned on. 

3. Kinematics 

Kinematics in robotics is a statement form about geometrical description of a robot 
structure. From the geometrical equation we can get relationship among joints spatial 
geometry concept on a robot with ordinary co-ordinate concept which is used to determine 
the position of an object. In other word, kinematics is the relationships between the 
positions, velocities, and accelerations of the links of a robot arm. The aim of kinematics is to 
define position relative of a frame to its original coordinates. 

Using kinematics model, a programmer can determine the configuration of input reference 
that should be fed to every actuator so that the robot can do coincide movements of all joint 
to reach the desired position. On the other hand, with information of position that is shown 
by every joint while robot is doing a movement, the programmer by means of kinematics 
analysis can determine where is arm tip position or which parts of the robot should be 
moved in spatial coordinate. Kinematics problem consists of forward and inverse kinematics 
and each type of the kinematics has its own function as illustrated in fig. 5. 
From fig. 5, forward kinematics is used for transferring joint variable to get end-effector 
position. On the other hand, inverse kinematics will be applied to find joint variable from 
end-effector position. 

Kinematics of AdeptThree Robot Arm 


Joint Variable 

Forward Kinematics 

Inverse Kinematics 

(x, y, z) 

Fig. 5. Forward and inverse kinematics diagram 

3.1 Forward kinematics 

Forward kinematics problem is deal with finding the position and orientation of a robot 
end-effector as a function of its joint angles. Forward kinematics problem is relatively simple 
and it is easy to be implemented. There are two methods for building forward kinematics 
provided in this section. 

3.1.1 Graphical method 

A simple forward kinematics can be derived from its space using graphical solution. With a 
three link planar robot in fig. 6, the graphical method for solving forward kinematics will be 
described in this section. 





Is / 

I f 

h / 

/ e. 




— *- 

Fig. 6. Geometric of three link planar robot 

Using the vector algebra solution to analyse the graph, the coordinate of the robot end- 
effector can be solved as follows. 

X = Zj cos(0,) + U cos(9 l +0 7 ) + l 3 cos(#, +6 2 +6 i ) 
y = \ sin(<?, ) + /, sin(0, + 2 ) + 1 3 sin(6, + 2 + 3 ) 


Maple is mathematical software which is widely used in computation, modelling and 
simulation. In each section of the kinematics and Jacobean, the script of the software is 

26 Robot Arms 

provided. The Maple script for building forward kinematics using the graphical method is 
listed as follows. 

> restart: 

>n:=3 :y :=0 :c:= 


> for i from 1 




> for j from i 






> end do ; 



(c) :c:=0: 

> end do ; 

3.1.2 D-H convention 

The steps to get the position in using D-H convention are finding the Denavid-Hartenberg 
(D-H) parameters, building A matrices, and calculating T matrix with the coordinate 
position which is desired. 

D-H Parameters 

D-H notation is a method of assigning coordinate frames to the different joints of a robotic 
manipulator. The method involves determining four parameters to build a complete 
homogeneous transformation matrix. These parameters are the twist angle ai , link length ai, 
link offset di , and joint angle di (Jaydev, 2005). Based on the manipulator geometry, two of 
the parameters which are ai and ai have constant values, while the di and 0i parameters can 
be variable depending on whether the joint is prismatic or revolute. 

Jaydev (2005) has provided 10 steps to denote the systematic derivation of the D-H 
parameters as : 

1. Label each axis in the manipulator with a number starting from 1 as the base to n as the 
end-effector. Every joint must have an axis assigned to it. 

2. Set up a coordinate frame for each joint. Starting with the base joint, set up a right 
handed coordinate frame for each joint. For a rotational joint, the axis of rotation for 
axis i is always along Zi-i. If the joint is a prismatic joint, Z i-i should point in the 
direction of translation. 

3. The Xi axis should always point away from the Zi-iaxis. 

4. Yj should be directed such that a right-handed orthonormal coordinate frame is created. 

5. For the next joint, if it is not the end-effector frame, steps 2-4 should be repeated. 

6. For the end-effector, the Z n axis should point in the direction of the end-effector 

7. Joint angle 0i is the rotation about Zi-i to make X i-i parallel to Xi. 

8. Twist angle ai is the rotation about Xi axis to make Zi-iparallel to Zi. 

9. Link length Hi is the perpendicular distance between axis i and axis i + 1, 

10. Link offset di is the offset along the Zj-i axis. 

A Matrix 

The A matrix is a homogenous 4x4 transformation matrix which describe the position of a 
point on an object and the orientation of the object in a three dimensional space. The 
homogeneous transformation matrix from one frame to the next frame can be derived by the 
determining D-H parameters. The homogenous rotation matrix along an axis is given by 

Kinematics of AdeptThree Robot Arm 


Rot : 


-cos a sin# 

sin a sin# 


cos a cos# 

-sin a sin# 

sin a 

cos a 



and the homogeneous translation matrix transforming coordinates from a frame to the next 
frame is given by 









Where the four quantities Q\, flj, &y ai are the names joint angle, link length, link offset, and 
twist angle respectively. These names derive from specific aspects of the geometric 
relationship between two coordinate frames. The four parameters are associated with link i 
and joint i. 

In Denavit-Hartenberg convention, each homogeneous transformation matrix A, is 
represented as a product of four basic transformations as follows. 

A ; = Rot (z, 0) Trans (z,&^)Trans (x,a i )Rot{x,a l ) 


or in completed form as 

cos(6>) -sin(6>.) 0"|[1 
sin(6>) cos(<9) 


a i~\ 







1 d. 


cos(a.) -sin(a f ) 
sin(«.) cos(a ; ) 

By simplifying equation 5, the matrix A, which is known as D-H convention matrix is given 
in equation 6. 


cos ; 

-cosa ; sin6>. 

sin a ; sin 


a, cos j 

sin 6 i 

cos a, cos 


-sin a t sin 6 i 

a, sin ; 

sin a, 

cos a, 




In the matrix A,, about three of the four quantities are constant for a given link. While the 
other parameter which is di for a revolute joint and di for a prismatic joint is variable for a 


Robot Arms 

joint. The A; matrix contains a 3x3 rotation matrix, a 3x1 translation vector, a 1x3 perspective 
vector and a scaling factor. The A, matrix can be simplified as follows. 




T Matrix 

The T matrix is a kinematics chain of transformation. The matrix can be used to obtain 
coordinates of an end-effector in terms of the base link. The matrix can be built from 2 or 
more A matrices depending on the number of manipulator joint(s). The T matrix can be 
formulated as 

T = T n =A 1 A 2 ,...,A }i 


Inside the T matrix, the direct kinematics can be found in the translation matrix P, while the 
X, Y and Z positions are Pi, P2 and P3 respectively. 

Solution for the robot 

An AdeptThree robot arm with four joints is figured in fig. 7. The AdeptThree robot joint 

motions are revolution, revolution, prismatic and revolution (RRPR) respectively from joint 

1 to 4. So the robot has four degrees of freedom. 

From fig. 7, joints 1, 2, and 4 are revolute joints; then the values of 0i are variable. Since there 

is no rotation about prismatic joint in joint 3, the di values for joint 3 is zero while d\ is 



e 2 ^J 


1 *V\ 

— ya 






n~ — 1 

J 3 d t 

1 i_ 

Fig. 7. Links and joints parameters of an AdeptThree robot arm 

Kinematics of AdeptThree Robot Arm 


Each axis of the AdeptThree robot was numbered from 1 to 4 based on the algorithm 
explained before. After established coordinate frames, the next step is to determine the D- 
H parameters by first determining ai. The a\ is the rotation about Xi to make Zi-i parallel 
with Zj. Starting from axis 1, ai is because Zo and Zi are parallel. For axis 2, the ai is n or 
180° because Z2 is opposite of Zo which is pointing down along the translation of the 
prismatic joint. 013 and a.4 values are zero because Z3 is parallel with Z2 and Z4 is also 
parallel with Z3. 

The next step is to determine a\ and di. For axis 1, there is an offset di between axes 1 and 2 in 
the Zo direction. There is also a distance CL\ between both axes. For axis 2, there is a distance 
02 between axes 2 and 3 away from the Zi axis. No offset is found in this axis so ^2 is zero. In 
axis 3, due to prismatic joint, the offset di is variable. Between axes 3 and 4, there is an offset 
di which is equal to this distance, while 03 and 04 are zero. The completed D-H parameters 
are listed in table 1. 


joint Angle 


Link Offset 















d 3 




Table 1. D-H Parameters of an AdeptThree Robot 

The transformation matrix A; can now be computed. Using the expression in equation 6 the 
A matrices of each joint can be build as 


-8, l A 





l 2 C 2 

c 2 s 2 

s 2 -c 2 













Robot Arms 

c 4 -s, 

s. c t 

1 di 



T matrix is created by multiplying each A matrix defined using equation 9 to 12 and the 
result is as follows. 

S 4?U2 "•" C 4 C *l+2 S A C U2 + C 4' S 'l+2 " '2 C l+2 ~™1 C 1 

,? 4 C l+2 " l " C 4' S l+2 

Vlt! +C 4 C l+2 

Ml+2 + Vi 


—d 4 -<f 3 +d x 



Where a and Si are the cosines and sinus of 8i, ci+2and si+2are cos(8i+62) and sin(0i+62), li is 
the length of link i and d; is the offset of link i. 

By using the T matrix, it is possible to calculate the values of (Px, Py, P z ) with respect to the 
fixed coordinate system. Then the Px, P y , P z which are obtained with direct kinematics are 
equations which are listed below: 


■- Z 2 s 1+2 + Z,s, 


P = -d. -d, + d, 

Where constant parameters h=559 mm, 12=508 mm, and di=876.3 mm. The direct kinematics 
can be used to find the end-effector coordinate of the robot movement by substituting the 
constant parameter values to the above equation. 
Maple script for the D-H convention of forward kinematics is listed as follows. 

> res 

tart : 

> DH: 

=Matrix (<<theta [ 1 ] , theta[2] , , tr 



, 0, 







0>|<0,pi, 0, 0>>) : 

> for 

i from 1 to 4 do 

> A[i 

] :=Matrix (<<cos (DH [i, 1 ] ) , sin (DH [ 

i, 1] ) , 0, 0>|<- 




♦sin (DH[ 

i,l]) , 


(DH[i,4] ) *cos (DH[i, 1] ) 

sin (DH[i, 


, 0>|<sin (DH[i, 4] ) 




, - 


(DH[i, 4] ) *sin (DH[i, 1] ) 

cos (DH [i, 


, 0>|<DH[i, 

3] *cos (DH[i, 


, DH[i 

,3] *si 

n(DH[i, 1] ) ,DH[i,2] , 1») ; 

> end 


>T: = 

simplify (Al .A2 .A3 .A4) ; 

3.2 Inverse kinematics 

Inverse kinematics deals with the problem of finding the appropriated joint angles to get a 
certain desired position and orientation of the end-effector. Finding the inverse kinematics 
solution for a general manipulator can be a very tricky task. In general, inverse kinematics 
solutions are non linear. To find those equations can be complicated and sometimes there is 
no solution for the problem. Geometric and algebraic methods are provided in this section 
for solving inverse kinematics of a robot arm. 

Kinematics of AdeptThree Robot Arm 


3.2.1 Geometric method 

One of the simple ways to solve the inverse kinematics problem is by using geometric 
solution. With this method, cosines law can be used. A two planar manipulator will be used 
to review this kinematics problem as in following figure. 






/ \ 















Fig. 8. Geometric of two link planar robot 
With cosines law, we get 

(x 2 +y 2 ) = l;+ l 2 2 -2\J 2 cos(im-6 2 ) 

Since cos(180-62) = -cos(02) then the equation 15 will become 

{x 2 +y 2 ) = l 2 1 +l;+2l 1 l 1 cos{0 1 ) 

By solving the equation 16 for getting the cos(62), 

x 2 + y 2 -/ 2 -/ 2 



Therefore the 62 will be determined by taking inverse cosines as 

ft, = arccos | 
Again looking the fig. 8, we get 

( x 2 +y 2 -l\-l; " 
' 2H 

t(y?)_ sin(y) 

k ^ 



a = arctan — 






Where sin(y) = sin(180-92) = sin(92). By replacing sin(y) with sin(62), the equation 19 will 


Robot Arms 

fl = arcsin 
Since 81 = P + a, the 9i can be solved as 

? 2 sin(6> 2 ) 

V* 2+ y\ 

; 2 sin(a,) 

- arctan 


Maple script for the geometric method of inverse kinematics is listed as follows. 



> restart : 

>beta:=solve (sin (beta) /12 = sin (theta2) /sqrt (x~2+y~2 ) , 

beta) : 

> alpha : =arctan (y, x) : 

> thetal : =beta+alpha; 

> theta2 :=solve (y"2+x"2=l 1"2+12"2+ (2*11*12* cos (theta) 

) , theta) ; 

3.2.2 Algebraic method 

The other simple ways to solve the inverse kinematics problem is by using algebraic 
solution. This method is used to make an invert of forward kinematics. Rewriting the end- 
effector coordinate from forward kinematics: 

V = k s i + ^Si- 
Using the square of the coordinate, we get 



x 1 +V 1 =l]c\ +%(c U2 f + 2/ I / 2 c 1 (c 1+2 ) + 
>XH 2 (s M ) 2 +2/M(s M ) 
Since cos(a) 2 +sin(a) 2 = 1 and also cos(a+b) 2 +sin(a+b) 2 = 1, the equation 23 can be simplify as 

Note that 

+r=/ 1 2 + / 2 2 +2/ 1 / 2 [ C] ( Cl+2 ) + s 1 (s 1+2 )] 

cos(a ±b) = cos(a)cos(b) + sin(a)sin(b) 
sm(a±b) = cos(a)sin(b) + sin(fl)cos(b) 



By simplifying the formulation inside the parenthesis in equation 24 with the rule in 
equation 25, the only left parameter is cos(02); so the equation 24 will become 

x 2 + y 1 = l\ + \\ + 21J. 2 C 2 
Now the 62 can be formulated as the function of inverse cosines 


Kinematics of AdeptThree Robot Arm 33 

^=aircos| * 2+ y- g | 127) 

Using the rule of sinus and cosines in equation 25, the end-effector coordinate can be 
rewritten as 

x = l 1 c 1 +l 2 c 1 c 2 -l 2 s 1 s 2 
y = l 1 s l +l 2 s 1 c 2 +l 2 c 1 s 2 

There are two unknown parameters inside the equation which are cos(9i) and sin(9i). The 
cos(8i) can be defined from the rewritten x as 

x + l^s, 
\+l 2 C 2 

The sin(6i) is still a missing parameter and it is need to be solved. Substituting ci to y in 
equation 28, we get 

y = ^f^(l 2 s 1 ) + l 1 s 1 + l 2 s 1 c 2 (30) 

The equation 28 will become, 

_ xi 2 s 2 + t 2 s i s 2 t 1 s 1 + l 1 l 2 s 1 c 2 
I + Lc, I + he. 

lJ^c 2 +l 2 s J c 2 
l x + l 2 c 2 

Simplifying the equation 31 we get 

xl 2 s, +SJK +12+21^0,) n r,^ 

y = ^— '- W 

l,+l 2 c 2 

The parenthesis in equation 32 can be replaced using cosines law with x 2 + y 2 . Therefore the 
sinus of 9i can derived from the above equation as 

xis 2 +s, (x 2 +y 2 ) 

y= , — — (33) 

/j + l 2 c 2 
Now the 9i will be got as the function of inverse sinus as 

. f y(I 1+ l 2 c 2 )-xl 2 (34) 

= arcsin | 

Until now we had defined both 9i and 02 of a two planar robot that is similar to the 
AdeptThree robot. The joint angles can be used by applying link length of the robot to the 
equation of those angles. 

34 Robot Arms 

Maple script for the algebraic method of inverse kinematics is listed below. 

> restart: 

> theta2 :=solve (x A 2+y A 2=ll A 2+12 A 2+ (2*ll*12*cos (theta2) ) , theta2) ; 

> restart: 

> cos (thetal) :=solve (x=ll*cos (thetal) +12*cos (thetal) *cos (theta2) - 
12*sin(thetal)*sin(theta2) , cos (thetal) ) : 

> thetal : =simplify (solve (y=ll*sin (thetal) +12*sin (thetal) *cos (theta2) + 
12*cos (thetal) *sin (theta2) , thetal) ) ; 

4. Jacobean 

The Jacobean defines the transformation between the robot hand velocity and the joint 
velocity. Knowing the joint velocity, the joint angles and the parameters of the arm, the 
Jacobean can be computed and the hand velocity calculated in terms of the hand Cartesian 
coordinates. The Jacobean is an important component in many robot control algorithms. 
Normally, a control system receives sensory information about the robot's environment, 
most naturally implemented using Cartesian coordinates, yet robots operate in the joint or 
world coordinates. Transforms are needed between Cartesian coordinates and joint 
coordinates and vice versa. The transformation between the velocity of the arm, in terms of 
its joint speeds, and the velocity of the arm in Cartesian coordinates, in a particular frame of 
reference, is very important. Solving the inverse kinematics can provide a transform, but 
this would be a difficult task to perform in real-time and in most cases no unique solutions 
exist for the inverse kinematics. An alternative is to use the Jacobean (Zomaya et al. , 1999). 
Many ways to design a Jacobean matrix of a robot arm were provided. Zomaya et al. (1999) 
had presented three kinds of algorithms to perform a Jacobean matrix. First algorithm is the 
simple way. Without using matrix calculation, the Jacobean can be built from T matrix. 
Second algorithm was found to perform very well using a sequential processing method. 
Third algorithm is also provided to sequential machine, but it would be interesting to study 
how well it maps onto the mesh with multiple buses. The other algorithm was provided by 
Manjunath (2007) and Frank (2006). It uses tool configuration vector to perform the 
Jacobean. The last algorithm will be used and explained in this paper (Rehiara, 2011). 
Given joint variable coordinate of the end effectors: 

<7 = [<7 1 <? 2 -<?J T ( 35 ) 

Where q\ = 8i for a rotary joint and qi = di for a prismatic joint. Nonlinear transformation from 
joint variable q(t) to y(t) is defined as y=h(q), then the velocities of joint axes is given by 

2/ = ^4 = J4 ( 36 ) 


Where J is the Jacobean of manipulator. Inverse of the Jacobean J- 1 relates the change in the 
end-effector to the change in axis displacements, 

q = ry (37) 

Kinematics of AdeptThree Robot Arm 


The Jacobean is not always invertible, in certain positions it will happen. These positions are 
called geometric singularities of the mechanism. 

A rotation matrix in a T matrix is formed by three 3x1 vector. In simple, the T matrix can be 
rewriting as 

n o a p 


Where a is the approach vector of the end-effector, o is the orientation vector which is the 
direction specifying the orientation of the hand, from fingertip to fingertip while n is the 
normal vector which is chosen to complete the definition of a right-handed coordinate 
system (Frank, 2006). 

The T matrix can be used to design the Jacobean by first defining the tool configuration 
vector w as follows. 


Rewriting p and a vector from equation 13, we get the tool configuration vector as 

^1+2 + kC, 



; 2 s 1+2 +/ lSl 

-d i -d 3 + d 1 



Then the Jacobean matrix is the differential of the tool configuration vector a> as 


/(<?) = 

dq t 


By taking a differentiation of the eq. 40, the Jacobean for the AdeptThree robot is defines as 


•Is -Is 

-I s 

i 2 J l+2 

A + ' 2 C l+2 

'2 C l + 2 






The first 3x3 matrix in the Jacobean is also called direct Jacobean. Because the Jacobean in eq. 
42 is not a square matrix, it is not invertible. In this condition, the direct Jacobean can be 
useful since it is a square and invertible matrix. 


Robot Arms 

Maple script for forming the Jacobean is listed below. 

> restart: with (LinearAlgeb 


> q:=vector (4 , [phil ,phi2 ,d3 

, phi 

4]) : 

> J:=matrix (6, 4) : 

>w[l] :=ll*cos (phil)+12*cos 



2) : 

> w[2] :=ll*sin(phil)+12*sin 

(phil+phi2) : 

> w[3] :=-d4-d3+dl; 

> w[4] :=0; 

> w[5] :=0; 

>w[6] :=exp(q[4]/pi) ; 

> for i from 1 to 4 do 

> for j from 1 to 6 do 

>J[j,i] :=diff (w[j],q[i]); 

> end do ; 

> end do ; 

> print (J) ; 

5. Kinematics simulation 

A Virtual Instrumentation (VI) was built to the section of kinematics simulation for 
supporting the manual calculation of a four DOF SCARA robot. The VI is a product of 

File Edit View Project Operate Tools Window Help 



. : ■■■■ ■■ ■'.:;■ : ■ : 

Joint 1 ((leg) 

■50 I 50 
-100- -100 

-150 150 

Joint 2 (deg) 

-50 I 50 

-100- J -too 

-ISO'' "l5C 

Joint 3 (mm) 

■ I > 

100 200 305 

J Dint 4 (deg) 


Inverse Knr- 

x (mm)^fi 




Simultaneous moving 


X 1-596.916 

0.3 10 

Run forward kinematics 

Y" 1-577.936 

Sequence moving 

Z |5D0.S7 

Fig. 9. SCARA robot simulation 

Kinematics of AdeptThree Robot Arm 37 

graphical programming in Lab View which is produced by National Instrumentation. The 
designed VI can simulate visual movement of the SCARA robot. The advantage of utilizing 
Lab View is that the graphical programming language is easy and simple to be used. A user 
only needs to set each property to program the VI. 

As shown in fig. 9, the VI can be used to move the robot by applying the method of forward 
and inverse kinematics. To support the visual joint tracking, the VI is provided with 
simultaneous moving and sequence moving buttons. In simultaneous moving mode, each 
joint move together in same time. On the other hand sequence moving mode provides the 
motion of each joint one by one. Started from 1 st joint to 4 th joint, each joint will move after 
the other finished its task. The position of the end-effector is given in X, Y and Z boxes, 
while the joint variables are shown in qi, q2 and q3 boxes. 

6. Conclusion 

This paper formulates and solves the kinematics problem for an AdeptThree robot arm. The 
forward kinematics of an AdeptThree robot was explained utilizing D-H convention while 
inverse kinematics of the robot was design using the principal cosines. Jacobean for the 
robot was design by using tool configuration vectors and direct Jacobean. Some script to 
design forward and inverse kinematics and also Jacobean matrix were provided using 
Maple. A graphical solution for simulating and calculating the robot kinematics was 
implemented in a virtual instrumentation (VI) of LabView. Using the VI, forward 
kinematics for a four dof SCARA robot can be simulated. Inverse kinematics for the robot 
can also be calculated with this VI. 

7. References 

[1] Jaydev P. Desai (2005). D-H Convention, Robot and Automation Handbook, CRC Press, 

USA, ISBN 0-8493-1804-1. 
[2]Zomaya A.Y., Smitha H., Olariub S., Computing robot Jacobians on meshes with multiple 

buses, Microprocessors and Microsystems, no. 23, (1999), pp 309-324. 
[3] Frank L.Lewis, Darren M.Dawson, Chaouki T.Abdallah (2006), Robot Manipulators 

Control, Marcel Dekker, Inc., New York. 
[4] Bulent Ozkan, Kemal Ozgoren, Invalid Joint Arrangements and Actuator Related 

Singular Configuration of a System of two Cooperating SCARA Manipulator, 

Journal of Mechatronics, Vol.11, (2001), pp 491-507. 
[5] Taylan Das M., L. Canan Dulger, Mathematical Modeling, Simulation and Experimental 

Verification of a SCARA Robot, Journal of Simulation Modelling Practice and Theory, 

Vol.13, (2005), pp 257-271. 
[6] Mete Kalyoncu, Mustafa Tinkir (2006), Mathematical Modeling for Simulation and 

Control of Nonlinear Vibration of a Single Flexible Link, Procedings of Intelligent 

Manufacturing Systems Symposium, Sakarya University Turkey, May 29-31, 2006. 
[7] Mustafa Nil, Ugur Yuzgec, Murat Sonmez, Bekir Cakir (2006),, Fuzzy Neural Network 

Based Intelligent Controller for 3-DOF Robot Manipulator Procedings of Intelligent 

Manufacturing Systems Symposium, Sakarya University Turkey, May 29-31, 2006. 
[8] Rasit Koker, Cemil Oz, Tarik Cakar, Huseyin Ekiz, A Study of Neural Network Based 

Inverse Kinematics Solution for a Three-Joint Robot, Journal of Robotics and 

Autonomous System, Vol.49, (2004), pp 227-234 

38 Robot Arms 

[9] Adept, (1991), AdeptThree Robot: User's Guide, Adept Technology, USA. 

[10] Manjunath T.C., Ardil C, Development of a Jacobean Model for 4-Axes indigenously 

developed SCARA System, International Journal of Computer and Information Science 

and Engineering, Vol. 1 No 3, (2007), pp 152-158. 
[11] John Faber Archila Diaz, Max Suell Dutra, Claudia Johana Diaz (2007), Design and 

Construction of a Manipulator Type Scara, Implementing a Control System, 

Proceedings of COBEM, 19th International Congress of Mechanical Engineering, 

November 5-9, 2007, Brasilia. 
[12] Rehiara Adelhard Beni, Smit Wim (2010), Controller Design of a Modeled AdeptThree 

Robot Arm, Proceedings of the 2010 International Conference on Modelling, Identification 

and Control, Japan, July 17-19, 2010, pp 854-858. 
[13] Rehiara Adelhard Beni, System Identification Solution for Developing an AdeptThree 

Robot Arm Model, Journal of Selected Areas in Robotics and Control, February Edition, 

(2011), pp. 1-5 available at 

Solution to a System of Second Order 

Robot Arm by Parallel Runge-Kutta 

Arithmetic Mean Algorithm 

S. Senthilkumar and Abd Rahni Mt Piah 

Universiti Sains Malaysia, School of Mathematical Sciences, 

Pulau Pinang 

1. Introduction 

Enormous amount of real time robot arm research work is still being carried out in different 
aspects, especially on dynamics of robotic motion and their governing equations. Taha [5] 
discussed the dynamics of robot arm problems. Research in this field is still on-going and 
its applications are massive. This is due to its nature of extending accuracy in order to 
determine approximate solutions and its flexibility. Many studies [4-8] have reported 
different aspects of linear and non-linear systems. Robust control of a general class of 
uncertian non-linear systems are investigated by zhihua [10] . 

Most of the initial value problems (lVPs) are solved using Runge-Kutta (RK) methods which 
in turn are employed in order to calculate numerical solutions for different problems, which 
are modelled in terms of differential equations, as in Alexander and Coyle [11], Evans [12 ], 
Shampine and Watts [14], Shampine and Gordan [18] codes for the Runge-Kutta fourth 
order method. Runge-Kutta formula of fifth order has been developed by Butcher [15-17]. 
Numerical solution of robot arm control problem has been described in detail by Gopal et 
al.[19]. The applications of non-linear differential-algebraic control systems to constrained 
robot systems have been discussed by Krishnan and Mcclamroch [22]. Asymptotic observer 
design for constrained robot systems have been analyzed by Huang and Tseng [21]. Using 
fourth order Runge-Kutta method based on Heronian mean (RKHeM) an attempt has been 
made to study the parameters concerning the control of a robot arm modelled along with 
the single term Walsh series (STWS) method [24]. Hung [23] discussed on the dissipitivity of 
Runge-Kutta methods for dynamical systems with delays. Ponalagusamy and Senthilkumar 
[25,26] discussed on the implementations and investigations of higher order techniques and 
algorithms for the robot arm problem. Evans and Sanugi [9] developed parallel integration 
techniques of Runge-Kutta form for the step by step solution of ordinary differential 

This paper is organized as follows. Section 2 describes the basics of robot arm model 
problem with variable structure control and controller design. A brief outline on parallel 
Runge-Kutta integration techniques is given in section 3. Finally, the results and conclusion 
on the overall notion of parallel 2-stage 3-order arithmetic mean Runge-Kutta algorithm and 
obtains almost accurate solution for a given robot arm problem are given in section 4. 


Robot Arms 

2. Statement of the robot arm model problem and essential variable 

2.1 Model of a robot arm 

It is well known that both non-linearity and coupled characteristics are involved in 
designing a robot control system and its dynamic behavior. A set of coupled non-linear 
second order differential equations in the form of gravitational torques, coriolis and 
centrifugal represents dynamics of the robot. It is inevitable that the significance of the 
above three forces are dependent on the two physical parameters of the robot namely the 
load it carries and the speed at which the robot operates. The design of the control system 
becomes more complex when the end user needs more accuracy based on the variations of 
the parameters mentioned above. Keeping the objective of solving the robot dynamic 
equations in real time calculation in view, an efficient parallel numerical method is needed. 
Taha [5] discussed dynamics of robot arm problem represented by as 

T=A(Q)Q + B(Q,Q)+C(Q) 


where A(Q) represents the coupled inertia matrix, B(Q,Q) is the matrix of coriolis and 

centrifugal forces. C(Q) is the gravity matrix, T denotes the input torques applied at various 


For a robot with two degrees of freedom, by considering lumped equivalent massless links, 

i.e. it means point load or in this case the mass is concentrated at the end of the links, the 

dynamics are represented by 


T t =D n q\+D n q 21 + D U2 (q 2 f + D 112 {q 1 q 2 ) + D l , 
T 2 = D 21 q\+D 22 q 2 + D 211 {q l ) 1 +D 2 , 

D„ =(M, + M 2 )d; + 2M 2 djd 2 cos(g 2 ) , 
D n =D 21 =M 2 d\+M 2 d 1 d 2 cos(q 2 ) , 




D s =M 2 d\, 

D n2 =-2M 2 d 1 d 2 sin(q 2 ) , 

D, =[(M 1 +M 2 )d 1 sm(q l ) + M 1 d 2 sin(q 1 +q 2 )]g 

D 2 =[M 2 d 2 sm(q 1+ q 2 )]g. 

Solution to a System of Second Order Robot Arm 

by Parallel Runge-Kutta Arithmetic Mean Algorithm 41 

The values of the robot parameters used are Mi= 2kg, M2 = 5kg, d\ = At = 1. For problem of 
set point regulation, the state vectors are represented as 

X = (X 4 ,X 2 ,X 3 X 4 )' = fo -q^q^-q^qj , (3) 


q 1 and q 2 are the angles at joints 1 and 2 respectively, and q ld and q 2J are constants. 

Hence, equation (2) may be expressed in state space representation as 


* 2 =%D ]22 X 2 2 + D n2 X 2 X 4 + D, +T 1 )-^(D 21 X +D 2 +T 2 ) 

_Dil (D 122 X 2 J + D 112 X 2 X 4 + D, + r t ) - %D 211 X 4 2 + D 2 + T 2 ) 

r (4) 

4 1 \ 122 2 112 2 4 1 II , \ 211 4 2 2/ 

a d J 

Here, the robot is simply a double inverted pendulum and the Lagrangian approach is used 
to develop the equations. 

In [5] it is found that by selecting suitable parameters, the non-linear equation (3) of the two- 
link robot-arm model may be reduced to the following system of linear equations: 


V (5) 


%l ~^*20^2 _ ^21-^4 _ "^20^3 ' 

where one can attain the system of second order linear equations: 

X l =— A n X 4 — A 10 X 4 + o w l 1 / 

y -- A 2 y - A 1 y + R 2 T 

~3 ^*-21 A 3 ^20^1 ' ^210 J 2' 

with the parameters concerning joint-1 are given by 

Aw = 0.1730, A n = -0.2140, Bio = 0.00265, 

and the parameters of joint-2 are given by 

A 20 = 0.0438, A 2 i = 0.3610, B 20 = 0.0967 

If we choose Ti - 1 (constant) and T2 = A (constant), it is now possible to find the 

complementary functions of equation (4) because the nature of the roots of auxiliary 

equations (A. Es) of (4) is unpredictable. Due to this reason and for the sake of simplicity, 

we take T\ = Ti = 1. 


Robot Arms 

considering q 1 = q 2 = 0, q u = q Id = 1 and q l = q 2 = 0, the initial conditions are given by 
62(0) = £3(0) = -1 and £2(0)= 64(0) = and the corresponding exact solutions are, 

ei (f)=e 0M7 '[-1.15317919cos(0.401934074i) + 
0.306991074 sin(0.401934074f)] + 0.15317919 ' 

e 2 (f)=e° M7 '[0.463502009sin(0.401934074f) + 0.123390173cos(0.401934074f)] + 
e° lu7 '[-1.15317919cos(0.401934074f) + 0.306991074sin(0.401934074£)] 

e 3 (f)= 1.029908976 e un31 ° 4116 ' - 6.904124484 e^" 1691683 " + 4.874215508 , 

e 4 (f)=-0.116795962e-" 1,34M16 ' +0.116795962e-"° M163S " . 

> (6) 

3. A brief sketch on parallel Runge-Kutta numerical integration techniques 

The system of second order linear differential equations originates from mathematical 
formulation of problems in mechanics, electronic circuits, chemical process and electrical 
networks, etc. Hence, the concept of solving a second order equation is extended using 
parallel Runge-Kutta numerical integration algorithm to find the numerical solution of the 
system of second order equations as given below. It is important to mention that one has to 
determine the upper limit of the step-size (Si) in order to have a stable numerical solution of 
the given ordinary differential equation with IVP. We thus consider the system of second 
order initial value problems, 

yi=fM,yi,y t ),i =1,2,. 


with y,M=y l0 

y,( x °)=y,» 

for all; = 1,2,. 

3.1 Parallel Runge-Kutta 2-stage 3-order arithmetic mean algorithm 

A parallel 2-stage 3-order arithmetic mean Runge-Kutta technique is one of the simplest 
technique to solve ordinary differential equations. It is an explicit formula which adapts the 
Taylor's series expansion in order to calculate the approximation. A parallel Runge-Kutta 2- 
stage 3-order arithmetic mean formula is of the form, 

K=hf(x n ,y n ) 

K=m*»+\,y„+\) = K 

k 3 = hf(x n +k 1 ,y n +k t ) = k" 3 . 

Hence, the final integration is a weighted sum of three calculated derivatives per time step is 
given by, 

Solution to a System of Second Order Robot Arm 
by Parallel Runge-Kutta Arithmetic Mean Algorithm 



Parallel 2-stage 3-order arithmetic mean Runge-Kutta algorithm to determine yj and 
y ,,j =1,2,3, ....m is given by, 



y. = i>. h — fu 1 . + 4w, , + M,.l 


hu 1 . 
k J .=y . h '-= fc, 

2/ Jin r\ 2] 


h,=y)„+ hu i, = K, 

u ll =f(x,,y i „y^),^i=l,2,3....,Tn 


h hk,, hk„ hk, . hu,, . hu„ . hu, . 

u 2i =f(x n + —,y,„ + — -,y 2 , + — —,-,y + — — ,y,„ + — -,y 2 „ + — —,-,y m „ + — ) 

u,.= f(x + h,y, +hk„,y. +hk„,...,y + hk, ,y, + hu„,y, + hu„,...,y + hu, ). 

3 1 J \ n 'Jin 11 ' U 2H 12' ' J inn lm ' J In 11 ' J 2n 12 ' 'J mn lm / 

The corresponding parallel 2-stage 3-order arithmetic mean Runge-Kutta algorithm array to 
represent equation (9) takes the form 

14 1 

Therefore, the final integration is a weighted sum of three calculated derivatives per time 
step given by, 

y, +l =Vn + -7[K + 4=K+k 3 ] 



44 Robot Arms 

3.2 Parallel Runge-Kutta 2-stage 3-order geometric mean algorithm of type-l 

The parallel 2-stage 3-order geometric mean Runge-Kutta formula of type-I is of the form, 

K=hf(x n ,y„), 

2 2k 

k, = hf(x + — ,y + — L ) = k, , 

k 3 = h f( x „+K,y„+K) = K , 

Hence, the final integration is a weighted sum of three calculated derivates per time step 
which is given by, 

y„ +1 = y. +hk/%% . 

Parallel 2-stage 3-order geometric mean Runge-Kutta algorithm of type-I to determine yj 
and y, ,j =1,2,3, ....m is given by, 

y^^y^hk/%%, (12) 

y^ = y in +hkJ%K, 

k, = ii , 

k=y, + — ii = v , 
K,=y*+tov m K, > ( 13 ) 

^rf{x,„y p „y,„), Vj = l,2,3....,m (14) 

2h 2WL 2Hk„ 2Wc, 

2hu„ . 2hu„ . 2hu, . 
■"" 3 3 3 

u 3 ,=f( x „ + h >y,„ + h K<y2„ + hk i2>->y,„, + h K,„>yi„ + hu n>y2„ + hu n>->y,„„ + HJ- 

parallel Runge-Kutta 2-stage 3-order geometric mean of type-I array represent equation (13) 
takes the form 

Hence, the final integration is a weighted sum of three calculated derivatives per time step 
and the parallel Runge-Kutta 2-stage 3-order geometric mean of type-I formula is given by, 

y„ 1 = y„+hk 1 \ % . (is) 

Solution to a System of Second Order Robot Arm 
by Parallel Runge-Kutta Arithmetic Mean Algorithm 


■wi -lA 

3.3 Parallel 2-stage 3-order geometric mean runge-kutta formula of type-ll 

The parallel 2-stage 3-order geometric mean Runge-Kutta formula of type-II is of the 

K=hf(x n ,y n ), 

6 6 

Hence, the final integration is a weighted sum of three calculated derivates per time step 
given by, 

y» + i = y„ + h K%" 3 ■ 

Parallel 2-stage 3-order geometric Mean Runge-Kutta algorithm of type-II to determine yj 
and if ,j =1,2,3,. ...m is given by, 


y^=y^ + fc[^^] 

y,^=y, n + h [ulj + u^] 


K =y„, . 

k, =y 

3 j V ]n 


u i,=f( x „>y p ,>y,„)> Vj = l,2,3....,m 


,. h hk„ hk„ hk, 

u,. = f(x — ,y, — —,y, + — —,..., V + — — 

3 1 J \ it s ' Z) \n *■ ' Zf 2fl s ' J mn y 

6 6 6 6 

hu„ . hu„ . hu, , 

J/i, + 'Vm + -i-iVm, + — )• 

•J In S -J Ln S *s inn -* / 

6 6 6 


Robot Arms 

The corresponding parallel Runge-Kutta 2-stage 3-order geometric mean algorithm of type- 
II array to represent Equation (17) takes the form: 

Therefore, the final integration is a weighted sum of three calculated derivatives and the 
parallel Runge-Kutta 2-stage 3-order geometric mean algorithm formula is given by 

y„ + i = y» +J*i V • 


4. Results and conclusion 

In this paper, the ultimate idea is focused on making use of parallel integration algorithms 
of Runge-Kutta form for the step by step solution of ordinary differential equations to solve 
system of second order robot arm problem. The discrete and exact solutions of the robot arm 
model problem have been computed for different time intervals using equation (5) and y n +i- 
The values of ej(t), £2(t),e3(t) and 64(f) can be calculated for any time t ranging from 0.25 to 1 
and so on. 
To obtain better accuracy iorei(t), ei(t), e^t) and e^t) by solving the equations (5) and y n +i. 

Sol. No. 



Parallel RKAM 

Parallel RKAM 


























Table 1. Solutions of equation (5) for ei(t) 

Solution to a System of Second Order Robot Arm 
by Parallel Runge-Kutta Arithmetic Mean Algorithm 





Parallel RKAM 

Parallel RKAM 


























Table 2. Solutions of equation (5) for 62ft) 





Parallel RKAM 

Parallel RKAM 


























Table 3. Solutions of equation (5) for e^(t) 





Parallel RKAM 

Parallel RKAM 


























Table 4. Solutions of equations (5) for tt(t) 

48 Robot Arms 

Similarly, by repeating the same computation process for parallel Runge-Kutta 2- stage 3- 
order geometric mean algorithm of type-I and type-II respectively, yield the required 
results. It is pertinent to pinpoint out that the obtained discrete solutions for robot arm 
model problem using the 2-parallel 2-processor 2- Stage 3-order arithmetic mean Runge- 
Kutta algorithm gives better results as compared to 2-parallel 2-procesor 2-stage 3-order 
geometric mean Runge-Kutta algorithm of type-I and 2-parallel 2-procesor 2-stage 3-order 
geometric mean Runge-Kutta algorithm of type-II. The calculated numerical solutions using 
2-parallel 2-procesor 2-stage 3-order arithmetic mean Runge- Kutta algorithm is closer to the 
exact solutions of the robot arm model problem while 2-parallel 2-procesor 2-stage 3-order 
geometric mean Runge-Kutta algorithm of type-I and type-II gives rise to a considerable 
error. Hence, a parallel Runge-Kutta 2-stage 3-order arithmetic mean algorithm is suitable 
for studying the system of second order robot arm model problem in a real time 
environment. This algorithm can be implemented for any length of independent variable on 
a digital computer. 

5. Acknowledgement 

The first author would like to extend his sincere gratitude to Universiti Sains Malaysia for 
supporting this work under its post-doctoral fellowship scheme. Much of this work was 
carried out during his stay at Universiti Sains Malaysia in 2011. He wishes to acknowledge 
Universiti Sains Malaysia's financial support. 

6. References 

[1] K. Murugesan, N.P.Gopalan and D. Gopal,(2005),"Error free Butcher algorithms for 
linear electrical circuits", ETRI Journal, Vol.27, No. 2, pp. 195-205. 

[2] J.Y. Park, D.J.Evans, K. Murugesan, S. Sekar, and V.Murugesh,(2004),"Optimal control 
of singular systems using the RK-Butcher algorithm", International Journal of 
Computer Mathematics, Vol.81, No.2, pp.239-249. 

[3] J.Y. Park, K Murugesan, D.J.Evans, S. Sekar, and V.Murugesh,(2005), "Observer 
design of singular systems (transistor circuits) using the RK-Butcher algorithm", 
International Journal of Computer Mathematics, Vol.82, No.l, pp. 111-123. 

[4] S.Sekar,V.Murugesh and K. Murugesan (2004), "Numerical strategies for the system of 
second order IVPs Using the RK-Butcher algorithms", International Journal of 
Computer Science and Applications, Vol.1, No.2, pp. 96-117. 

[5] Z.Taha, "Approach to variable structure control of industrial robots", pp. 53-59, 
Compiled by K.Warwick and A.Pugh, (1988), Robot Control: Theory and 
applications, (I E E Control Engineering Series, Peter Peregrinus Ltd, North- 

[6] S.Oucheriah, (1999), "Robust tracking and model following of uncertain dynamic delay 
systems by memory less linear controllers", IEEE Transactions on automatic 
control, Vol.44. No. 7, pp. 1473-1481. 

[7] D.Lim and H. Seraji, (1997), "Configuration control of a mobile dexterous Robot: Real 
time implementation and experimentation", International Journal of Robotics 
Research, Vol. 16,No.5. pp. 601-618. 

Solution to a System of Second Order Robot Arm 

by Parallel Runge-Kutta Arithmetic Mean Algorithm 49 

[8] M.M.Polvcarpou and P.A.Loannou, (1996), "A Robust adaptive non-linear control 

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50 Robot Arms 

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Knowledge-Based Control for Robot Arm 

Aboubekeur Hamdi-Cherif 

1 Qassim University, Computer Science Department, Buraydah 
2 Universite Ferhat Abbas Setif (UFAS), Computer Science Department, Setif 

1 Saudi Arabia 
2 Algeria 

1. Introduction 

The present research work reports the usability of knowledge-based control (KBC) as an 
alternative control method with specific concentration robot arm (RA). This novel control 
approach is based on the combination of inferences and calculations. It is dictated by the 
advent of microprocessor technology which has been one of the sources of inspiration for 
techniques spanning the whole spectrum of controllers design. KBC can contribute to build 
simple proportional integral and derivative (PID) control schemes (Astrom et al., 1992) to 
large classes of regulators such as self-tuning regulators and model-reference adaptive 
controllers, among others (Hanlei, 2010). Because knowledge base systems (KBSs) research 
has focused on implementing heuristic techniques, the corresponding knowledge-based 
controllers can justly be considered as the next logical step in control design and 
implementation (Handelman et al., 1990). The main characteristics of knowledge-based 
controllers is that they incorporate years-long human expertise under the form of machine- 
understandable heuristic rules. In KBC, the knowledge elicited from human experts is 
codified and embodied within the KB in the form of IF-THEN rules. As a result, the KB 
technology takes into account the increase in system complexity. This sophistication is 
naturally encountered as efforts are made to stretch the limits of system performance and 
integrate more capabilities as a response to technological advances (Calangiu et al., 2010). In 
addition, the inherent ability of KBSs to support incremental expansion of capabilities and 
provide justification for recommendations or actions is offered by conventional 
programming techniques. Serious considerations are being given to increasing system 
reliability by predicting algorithm failure in RAs control and reconfiguring control laws in 
response to algorithm failure due to instability/ chattering, or large RAs parameter 
The knowledge-based control (KBC) benefits as applied to RA are to: 

Implement/ incorporate heuristics within the RA control schemes. 

Diagnose or predict algorithm failure. 

Identify changes in RA parameters or structure. 

Recalculate control laws based upon knowledge of the current RA parameters. 

Select appropriate control laws based on the current RA responses. 

Execute supportive control logic which has been used for practical controllers in the past. 

Provide an explanation of the situation to the user as and when requested. 

52 Robot Arms 

However KBC approach is not without issues. Indeed, KBC design problem requires 
elicitation / acquisition and coding of the "useful expertise", gained by humans over a 
lifetime. It is highly difficult to find proper ways of extracting this expertise from the human 
experts. Discerning "usefulness", avoiding unnecessary data and finding ways of optimizing 
this knowledge representation is not a straight-forward task. How far are we from common 
sense-based control? This paper extends the limits of RA control using KBC approach in 
order to reach this distant end. 

The main issue of the present work is to answer positively our central question, i.e., whether 
it is possible to integrate the diversified methods dealing with dynamical systems control 
exemplified by RA control, while concentrating on KBC as an alternative control method. 
We describe the epistemological characteristics of a framework that is believed to integrate 
two distinct methodological fields of research i.e., artificial intelligence (Al)-based methods 
where KBC is partly rooted, on the one hand, and control theory, where RA control is 
formulated, on the other hand. Blending research from both fields results in the appearance 
of a richer research community. Emphasis is now made on RA control as a prelude to other 
classes of robotic systems; ultimately enhancing full programmable self-assembly 
compounds (Klavins, 2007). The chapter is organized as follows. In Section 2, the main KBC 
issues are discussed. Section 3 presents KBC within the general area of intelligent control 
and places KBC with respect to generalized hybrid control. Section 4 summarizes RA 
control in standard mathematical terms. Section 5 deals with an architecture for KBC for RA 
as an alternative control method followed by a conclusion and future developments. 

2. Knowledge-based control issues 

2.1 Our specific problem 

The specific problem we want to tackle can broadly be expressed as follows: 

• A plant configuration library describing the actual system to be controlled, 

• A library of control algorithms with various degrees of complexity, 

One (class of) algorithm (s) that control one plant configuration. 

Address simulation of RAs dynamics under various control schemes. 
For doing this, consider two complementary environments, i.e. a numeric environment 
responsible for making calculations (trajectory, control law,...) and a symbolic environment 
responsible for making logical inferences incorporating human experience. These two 
environments are the main components of any KBC architecture. Two modes of operation 
are therefore possible. In the numerical or exploitation mode, the program generates the 
outputs using imposed algorithms. In the inferential or exploration mode, the algorithm is 
not known before hand. Using the codified expertise in the KB, the program has to choose it 
from a library before firing the numeric mode. For the sequel, we first start by considering 
standard RA control and then KBC within the larger context of intelligent control. 

2.2 From standard RA control to KBC 

RA control is the process whereby a physical system, namely a set of robotic linked arms, is 
made compliant with some prescribed task such as following an imposed trajectory or 

Knowledge-Based Control for Robot Arm 53 

keeping in pace with a given angular velocity (Siciliano, 2009). Welding and assembly-line 
robots are popular examples of RA industrial applications. RA control is a much diversified 
field. As a result, it makes concentrated research a difficult task. While RA control has been 
extensively studied from the pure control side (Lewis et al., 2003), for the last four decades, 
or so, very little attention has been made with regard to KBC. Indeed, the symbolic 
approach efforts as applied to control at large remain quite isolated (Martins et al. 2006). 
Our fundamental aim is to contribute to the integration of RA control within KBC, 
considered within a larger intelligent control methodology; this latter being defined as a 
computational methodology that provides automatic means of improving tasks from 
heuristics (Hamdi-Cherif & Kara-Mohamed, 2009). As a subfield of intelligent control, KBC 
attempts to elaborate a control law on the basis of heuristics. KBC aim is therefore consistent 
with the overall goal of intelligent control and, as such, automatically generates a control 
law from heuristic rules and actual facts describing the actual RA status (control law, errors, 

2.3 Pending control issues 

Although KBC is a promising applied research area, there remain many challenges to be 
addressed. The main pending issues are: 

• the system under control can be very complex (e.g. nonlinearities in robot arm (RA)) ; 

• our knowledge of the system is imprecise (e.g. unknown RA parameters, unknown 
conditions of operation) although gradually increasing during operation, in the 
optimistic case of successful identification process, 

• the influence of the environment is strong (e.g. outside perturbation, modeling errors), 
may vary and may even influence the current task, 

• the goal of the system is described symbolically and may have internal hierarchy to be 
further investigated and structured. 

If the answer to these challenges can be obtained from human experts, then this knowledge 
is codified within the KB by knowledge engineers. If the answer is unknown, then offline 
experimentation is done by control engineers to gradually build an answer and codify it in 
the KB. In any case, the KBC designer has to constantly upgrade the KB with human 
expertise and/ or manual experimentations. 

2.4 Overview of related works 

Few authors have addressed the issue of designing and developing systems that cater for 
general-purpose RA control. For example (Yae et al. 1994) have extended the EASY5 - the 
Boeing Engineering and Analysis SYstem - incorporating constrained dynamics. (Polyakov 
et al. 1994) have developed, in MATHEMATICA™, a symbolic computer algebra system 
toolbox for nonlinear and adaptive control synthesis and simulation which provides flexible 
simulation via C and MATLAB™ code generation. MATHEMATICA™ has also been used 
in a simulation program that generates animated graphics representing the motion of a 
simple planar mechanical manipulator with three revolute joints for teaching purposes 
(Etxebarria, 1994). A toolbox is available for RA control running on MATLAB™ (Corke, 
1996). For supplementary and more general applications of computer algebra to CACSD 
(computer-aided control system design), we refer to (Eldeib and Tsai, 1989). Recent research 
directions aim at the development of operating systems for robots, not necessarily for the 

54 Robot Arms 

RA class. An overview of ROS, an open source robot operating system has been recently 
reported. ROS is not an operating system in the traditional sense of process handling and 
scheduling. It provides a structured communications layer above the host operating systems 
of a heterogeneous cluster. ROS was designed to meet a specific set of challenges 
encountered when developing large-scale service robots as part of the so-called STAIR 
project []. The way how ROS relates to existing robot 
software frameworks, and a brief overview of some of the available application software 
which uses ROS are reported in (Quigley, et al. 2009). However, none of these works 
addressed the issue of using the KBC approach to solve the RA control problem. Hence our 

3. Solution components 

3.1 KBC within intelligent control 

3.1.1 The area of intelligent control 

One of the fundamental issues that concerns intelligent control is the extent to which it is 
possible to control the dynamic behavior of a given system independently of 

• its complexity, 

• our capability of separating it from the environment and localizing it, 

• the context in which this system operates, 

• the forms of knowledge available and the categories it manipulates, 

• the methods of representation. 

As formulated, this issue cannot be handled by either control theory or artificial intelligence 
(AI). Indeed, control theory has a very localized mostly numerical vision of the problem. 
This prevents it from looking beyond the localized constraints self-imposed by the designer 
and hidden within the mechanism of the mathematical representation. From the standpoint 
of AI, the available knowledge-related methods cannot easily handle dynamic systems and 
have very little consideration for numerical manipulation. Indeed, computations of margins 
of stability, controllability, observability are alien to AI. Moreover, both control theory and 
artificial intelligence (AI) cannot properly operate out of the operations research (OR) 
paradigm. Its queues, graphs and game-theoretic situations are typical of the variety of 
control applications. That is why an early proposal for the definition of intelligent control is 
to consider this field as the intersection of the three previously-cited disciplines namely 
control theory, AI and OR, (Saridis, 1987). Other fields such as soft computing represented 
by fuzzy, genetic, neural systems and their combinations, on the one hand, and cognitive 
science, on the other hand have been progressively integrated within the intelligent control 
discipline over the last three decades, or so (Lewis et al; 2003). 

3.1.2 Landmarks of intelligent control 

Intelligent control is a term that first appeared in the seventies and later developed in 
(Saridis , 1987). An early, but constantly refined definition of this field describes itself as that 
area beyond adaptive, learning and self-organizing systems which represents the meeting 
point between artificial intelligence (AI), automatic control (AC) and operations research 
(OR). A tremendous body of literature has been developed to account for the description / 
design within this novel paradigm. International intelligent control symposia have been 

Knowledge-Based Control for Robot Arm 55 

held every year since 1985 and numerous contributions appear regularly in the specialized 
and thoroughly documented literature where novel original definitions and applications of 
the field are proposed e.g. (Rao, 1992), (Handelman et al., 2010). Extensions of the field are 
reported by (Astrom, 1989), (Astrom and MacAvoy, 1992), and (Cellier et al, 1992). Other 
approaches have also been considered by researchers like the cognition-oriented approach 
with applications, (Meystel, 1994). Among the several advanced theoretical and applied 
results are those due to (Saridis, 1987) who proposed the so-called an entropy-based theory 
for hierarchical controller design based on the so-called "principle of decreasing precision 
with increasing intelligence". More recently, methods concentrated on soft computing 
methods such as: 

a. Neural networks (NNs). In (Kwan et al., 2001), a desired compensation adaptive law- 
based neural network (NN) controller is proposed for the robust position control of 
rigid-link robots where the NN is used to approximate a highly nonlinear function. 
Global asymptotic stability is obtained with tracking errors and boundedness of NN 
weights. No offline learning phase is required as learning is done on-line. Compared 
with classic adaptive RA controllers, parameters linearity and determination of a 
regression matrix are not needed. However, time for converging to a solution might be 

b. Fuzzy-Genetic. In (Merchan-Cruz and Morris, 2006), a simple genetic algorithm planner 
is used to produce an initial estimation of the movements of two RAs' articulations and 
collision free motion is obtained by the corrective action of the collision-avoidance 
fuzzy units. 

3.1.3 Scope of intelligent control 

Intelligent control as a discipline provides generalization of the existing control theories and 
methods on the basis of the following elements (Astrom and MacAvoy, 1992): 

• combined analysis of the plant and its control criteria, 

• processes of multisensor operation with information (knowledge) integration and 
recognition in the loop, 

• man-machine cooperative activities, including imitation and substitution of the human 

• computer structures representing these elements. 

3.1.4 Specific issues in intelligent control 

One of the main drawbacks of intelligent control is that, up to now, there is no established 
terminology identifiable with this discipline. There remains an inertia in following 
conventional views and recommendations. This attitude hinders the development of 
intelligent control ideas and methods. For the purpose of immediate applications, we will 
concentrate on a small area of intelligent control. On the one hand, we will focus on the use 
of numerical/ exploitation (procedural) and inferential/ exploration processing (declarative, 
rule-based) systems. The former describe the RA control algorithms while the latter 
represent the way in which the expertise is explored and used in firing the adequate 
algorithm according to the actual situation (plant, errors). In the multiresolutional control 
architectures for intelligent machines proposed by (Meystel, 1991), the general structure of 
the intelligent controller is described by a set of feedback loops. Each one of these loops is 
declared for a particular resolution level and works with a different time-scale. Resolution of 

56 Robot Arms 

a given level is defined by (Meystel, 1994) as "the size of undistinguishability zone for the 
representation of goal, plan and feedback law." 

3.2 KBC as a generalized hybrid control methodology 

3.2.1 Hybrid control 

KBC can alternatively be considered with respect to hybrid control. In the early sixties, the 
discipline of hybrid control referred to controlled systems using both discrete and 
continuous parts. This discipline spanned a substantial area of research from basic switched 
linear systems to full-scale hybrid automata. Later, symbolic control methods came to 
include abstracting continuous dynamics to symbolic descriptions, instruction selection and 
coding in finite-bandwidth control applications, and applying formal language theory to the 
continuous systems domain. A number of results have emerged in this area with a 
conventional control-theoretic orientation, including optimal control, stability, system 
identification, observers, and well-posedness of solutions. At the same time, symbolic 
control provides faithful descriptions of the continuous level performance of the actual 
system, and as a result, provides a formal bridge between its continuous and the discrete 
characteristics (Egerstedt et al., 2006). 

3.2.2 Generalized hybrid control 

Generalized hybrid control is meant to incorporate logic and control, whether discrete or 
continuous. For our KBC concern, we will consider KBC as an integration of pure control 
and logical inference as expressed by either propositional logic or first-order logic (FOL). As 
a result, KBC addresses questions at the highest level, i.e., at the level of symbols, and as 
such stands half-way between computer science and logic, on the one hand and control 
theory, on the other hand. A whole research area is to be investigated whereby results from 
hybrid control are to be mapped onto generalized hybrid control. As for now, a new line of 
research in hybrid systems has been initiated that studies issues not quite standard to the 
controls community, including formal verification, abstractions, model expressiveness, 
computational tools, and specification languages. These issues were usually addressed in 
other areas, such as software engineering and formal languages (Hamdi-Cherif, 2010). 

3.3 Overall architecture for intelligent control 

Intelligent machines are those that perform anthropomorphic tasks, autonomously or 
interactively and/ or proactively with a human operator in structured or unstructured, 
familiar or unfamiliar environments. The intelligent controller represents the driving force 
that allows intelligent machines achieve their goals autonomously. It embodies functions of 
inferences as well as conventional control based on numeric processing. When such 
environments treat more than one state of the process to be controlled, as in the case of RA 
control, then it is careful to separate between control and inference, both functionally and 
architecturally. To this end we propose, in Figure 1, an overall architecture for intelligent 
control which considers the following levels: 

3.3.1 Formulation level 

At the formulation level, we find a hierarchical task formulation / task negotiation process. 
In the worst case situation, this formulation elaborates a model of an imprecise and 
incomplete plant. 

Knowledge-Based Control for Robot Arm 


3.3.2 Controller-plant matching level 

This level uses knowledge from the KB to decide which controller algorithm is suitable for a 
given plant when operating under some prescribed user-defined specifications and other 
additional constraints. This level is further expanded in Figure 2. 

3.3.3 Reasoning level 

At this level, a KB contains the necessary knowledge to solve the controller-plant matching 
problem on the basis of the formulation. For the obtainment of the final controller-plant 
matching, a hybrid numeric / symbolic system representation has to be used. Some trade- 
off tasks as part of control process have to be considered. 

Incomplete r,i;'s toinnuitiun 

- Design iteration 

- Piohlcin luriiiukilimi il. 
(help iron: experts and users) 

Task negotiation 

Rough .■ iv\ i-jhlc specific; 

- Design le 

- Exploitation level 


Fig. 1. Overall architecture for intelligent control 

3.4 Inference issues 

In evaluating any knowledge base system (KBS), and therefore any knowledge-based 
control (KBC) system, a wide range of criteria can be considered. We will define a generic 
framework for a description of the inferential part intervening in the KBC system. There are 
thousands of such systems ranging from free software (e.g. CLIPS, to large industrial advanced packages such as G2™ 
from Gensym™ ( 

3.4.1 Knowledge base structure 

Under this heading, we describe whether the system provides the representation by frames, 
messages, object-oriented languages, semantic nets, among others. 

3.4.2 Type of logic involved 

The usual types of logic available in KBS shells/systems are : 

• Prepositional: Boolean with no variables. 

• Predicate or first order logic (POL): Boolean with variables. 

• Temporal: involves time in reasoning. 

• Fuzzy: handles uncertainty, imprecision. 


Robot Arms 

Non-monotone: handles changing data. 

Default: handles situations like "most controllers are acceptable for these specifications". 
Modal: handles situations like "it's possible that", "it has been shown that this type of 
controller does not fit". 

Perturbation* Innn mil.i 


Fig. 2. Controller-plant matching problem 

3.4.3 Reasoning strategy 

• Forward chaining: hypothesis-driven. 

• Backward chaining: goal-driven. 

• Hybrid chaining: combining both forward and backward chaining. 

• Blackboards: for keeping set of hypotheses of partial and final solutions. 

3.4.4 Knowledge issues 

• Knowledge management : browsers, editors, workspaces, workspaces security. 

• Knowledge validation : "what if's" simulation capabilities. 

• Knowledge building tools : human interface quality, KB construction quality, natural 
language environment. 

• Knowledge debugging : levels of tracing, rules reporting, quality in entry and knowledge 

3.4.5 Explanation, truth and uncertainty 

• Explanation of reasoning: why's, natural language explanations, messages, variables 
values representation. 

• Truth maintenance : forward update, backward update. 

Knowledge-Based Control for Robot Arm 59 

• Uncertainty management : certainty factors, fuzzy-oriented management. 

3.4.6 Miscellaneous 

• Interface with outside world: data acquisition, data bases, other specialized software 

• Other performance: stand-alone off-line, real-time performance, networking, other 
advanced special features. 

4. RA Standard control problem 

4.1 Brief history 

On the control side, we concentrate on some classes of control methods such as adaptive 
control and passivity-based control. The development of RA control algorithms has gone 
through at least three historical phases. The first is the model reference adaptive control and 
self-tuning control followed by the passivity approach and then by the soft computing 
methods. We report here the first two phases while the soft computing methods have been 
described in Section 3.1.3 above.. 

4.1.1 Model Reference Adaptive Control (MRAC) and Self-Tuning Control (STC) 

The first phase (1978-1985) concentrated its efforts on the approximation approach. The 
methods developed during this period are well-documented in the literature and some 
review papers have been written for that period (e.g. Hsia, 1986). Researches were 
concentrated on issues expanded below. 

a. Model reference adaptive control approach (MRAC) guided by the minimization of the error 
between the actual system and some conveniently chosen model of it. At the 
methodological level, this represents a traditional example of supervised learning based 
on comparison between the actual and desired outputs while trying to minimize the 
error between desired and actual values. 

b. Self-tuning control based on performance criteria minimization. 

4.1.2 Parametrization approach 

The methods developed during the second period that followed with some time overlaps 
with the previous period, concentrated on the parameterization approach. The methods 
developed within this period can be further separated in two broad classes, namely inverse 
dynamics and passivity-based control. 
a. Inverse dynamics 

The first set of methods treats the inverse dynamics-based control or computed torque 
method. It relies on the exact cancellation of all the nonlinearities in the system. In the 
ideal case, the closed-loop system is decoupled and linear. Stability in this case is based 
on the Lyapunov direct method. A dynamical system is said to be stable in the sense of 
Lyapunov if it has the characteristics that when it loses an un-restored energy over 
time, then it will stabilize at some final state, called the attractor. In Lord Kelvin's terms 
this means that conservative systems in the presence of dissipative forcing elements will 
decay to a local minimum of their potential energy. However, finding a function that 
gives the precise energy of a given physical system can be extremely difficult. On the 

60 Robot Arms 

other hand, for some systems (e.g. econometric and biological systems), the Lyapunov 
function has no physical meaning. 
b. Passivity-based control 

The second set of methods deals with passivity-based control. The aim is to find a 
control law that preserves the passivity of the rigid RA in closed-loop. Stability here is 
based on the Popov hyperstability method (Popov, 1973). One of the main motivations 
for using these control laws, as far as stability is concerned, is that they avoid looking 
for complex Lyapunov functions - a bottleneck of the Lyapunov-based design. These 
laws also lead, in the adaptive case, to error equations where the regressor is 
independent of the joint acceleration. The difficult issue of inertia matrix inversion is 
also avoided. At the opposite of inverse dynamics methods, passivity-based methods 
do not look for linearization but rather for the passivity of the closed-loop system. 
Stability is granted if the energy of the closed-loop system is dissipated. The resulting 
control laws are therefore different for the two previous classes. 

4.2 Issues in adaptive and passivity RA control 

From the vast literature on adaptive control, only a small portion is applicable to RA control. 
One of the first approaches to adaptive control, based on the assumption of decoupled joint 
dynamics, is presented in (Craig, 1988). In general, multi-input multi-output (MIMO) 
adaptive control provides the means of solving problems of coupled motion, though 
nonlinear robot dynamics with rapidly changing operating conditions complicate the 
adaptive control problem involved, even if there are also advantages when compared with 
the adaptive control of linear systems. Specialized literature has appeared in the field, e.g., 
the interesting tutorial reported in (Ortega & Spong, 1989). As far as adaptive control is 
concerned, some methods assume that acceleration is available for measurement and that 
the inertia matrix inverse is bounded. Others avoid at least the boundedness constraint (e.g. 
Amestegui et al., 1987) while passivity-based control avoids both limitations. We propose to 
classify the specialized contributions in the field as follows: 

a. Parameter estimation: such as the linear estimation models suitable for identification of 
the payload of a partially known robot, going back to (Vukabratovic et al., 1984). 

b. Direct adaptive control of robot motion as studied by : 

1. (Craig et al., 1987) in conjunction with model reference adaptive control (MRAC). 
Here stability is studied using strictly positive real transfer functions (SPR-TF). 

2. (Slotine and Li, 1987) in conjunction with the so-called "MIT rule". Here the 
regulator is independent of the acceleration measurement and linear in the 

3. Johansson has still improved the work of (Craig et al., 1987) in terms of stability. 
This method avoids matrix inversion and SPR-TF requirements (Johansson, 1990). 

c. Decentralized control for adaptive independent joint control as proposed by (Seraji, 1989). 

d. Control and stability analysis such as passivity-based control developed by (Landau and 
Horowitz, 1989). 

4.3 RA dynamics 

A standard mathematical model is needed for any RA control problem. The RA dynamics 
are modeled as a set of n linked rigid bodies (Craig, 2005). The model is given by the 
following standard ordinary differential equation in matrix form. 

Knowledge-Based Control for Robot Arm 61 

r(t) = M(q)q + C(q,'q)q+ G(q) + V{q) (1) 

Time arguments are omitted for simplicity. The notations used have the following meaning: 

q : joint angular position, nxl real vector. 

q : joint angular velocity, nxl real vector. 

q : joint angular acceleration, nxl real vector. 

r(t) : joint torque, nxl real vector. 

M(q) : matrix of moment of inertia or inertia matrix, nxn real matrix. 

C(q,q)q : Coriolis, centrifugal and frictional forces. C is nxn real matrix. 

G(q) : gravitational forces. G is an nxl real vector describing gravity. 

V(q) : nxl real vector for viscous friction. It is neglected in our forthcoming treatment. 

4.4 RA PID control 

Proportional integral and derivative (PID) control is one of the simplest control schemes. It 
has been successfully used for the last six decades, or so, in many diversified applications of 
control. Despite its simplicity, PID is still active as an applied research field. In February 
2006, a special issue of IEEE Control Systems Magazine has been devoted to the subject to 
account for its importance and actuality. Insofar as automatically-tuned PIDs (or autotuners) 
are concerned, commercial products became available around the early eighties. Since the 
Ziegler-Nichols rules of thumb developed in the 1940's, many attempts have been made in 
the "intelligent" choice of the three gains (e.g. Astrom et al. 1992). The intelligent approach 
also helps in explanation of control actions usage. Indeed, in many real-life applications, 
explanation of control actions is desirable, e.g., why derivative action is necessary. 
On the numerical level, the PID control u(t) is given by: 

u(t) = K p e(t) + K v e(t) + (2) 

e(t) = q(t)-q d (t) (3) 

e(t) = q(t)-q d (t) (4) 

Equation (1) describes the control u(t). K p , K{, K v are the gains for the proportional (P), 

integral (I) and derivative (D) actions, respectively. 

Equation (3) defines the position error e(t), i.e., the difference between the actual system 

position q(t) and the desired position qd(t). 

Equation (4) defines the velocity error and is simply the time-derivative of the error given in 

Equation (3) above. Equation (4) describes the difference between the actual system velocity 

and the desired velocity. The PID scheme block-diagram is given in Figure 3. 

4.5 RA adaptive control 

4.5.1 Purpose of adaptive control 

The general adaptive controller design problem is as follows : given the desired trajectory 
qd(t), with some (perhaps all) manipulator parameters being unknown, derive a control law 
for the actuator torques and an estimation law for the unknown parameters such that the 


Robot Arms 

manipulator output q(t) tracks the desired trajectories after an initial adaptation process. 
Adaptive control laws may be classified on the basis of their control objective and the signal 

Fig. 3. RA PID Control 

that drives the parameter update law. This latter can either be driven by the error signal 
between the estimated parameters and the true parameters (prediction or parametric error) 
or by the error signal between the desired and actual outputs (tracking error). Stability 
investigations are at the basis of acceptability of the proposed scheme. 

4.5.2 Example of adaptive control scheme 

As an example, the method due to (Amestegui et ah, 1987) compensates the modeling errors 
by a supplementary control St. First, the computed torque approach is used whereby the 
linearizing control is obtained by a suitable choice of the torque. This amounts to simply 
replacing the acceleration q by the control u in (1) above resulting in: 

r(r) = M(q)u + C(q,q)q+ G(q) + V(q) 

Combining (1) and (5) yields: 

M{q)(q-u) = 



Which amounts to n decoupled integrators (q = u) . In this case, the control u can be 

expressed in terms of the desired acceleration as a PD compensator. 

Now compensate the modeling errors by a supplementary control St and neglect viscous 


T{t) = M (q)(u) + C l> (q,q)q+ G(q) + St 
Using the linear parametrization property, we obtain: 


Knowledge-Based Control for Robot Arm 


M (q)(u -q) + St = y/(q,q,q)&.9 
The compensating control is then given by : 


St = y/(q,q,q)A0 
and the estimated parametric error vector is solution of : 


A0 = -T V / , (q,q,q)M o (q)(u-q) 

In the previous equations, the following notations are used: 
i//(q,q,q) represents the regressor matrix, of appropriate dimensions. 
The parametric error vector: 

A0 = o -0 

where is the actual parameter vector and 

a a constant and linear vector with respect to the nominal robot model. 
A is the estimate of A0 and 

T = diag(y 1 ,y 2 .,„y n ) 




is a positive-definite diagonal matrix with y. > , representing the adaptation gain for the 
gradient parametric estimation method. Note that this last scheme avoids the inversion of 
the inertia matrix. It reduces the calculations complexity. However the measurement of the 
acceleration is always required. The block-diagram is given in Figure 4. 




f(u, y , M a 

y A© 

*► M Q (q) 

+ Jo 



C fl (q,q)q+G (g) 

NB : In Figure 4, the following notations are used: y = y/; t = t 

Fig. 4. Amestegui's adaptive compensation scheme 

64 Robot Arms 

4.6 RA robust control 

Robust control approach considers adding a correcting term to the control signal. This 
compensates the parametric error. This supplementary signal gives better tracking and 
makes the system more robust with respect to parametric error. We can classify the robust 
methods as Lyapunov-based methods, variable structure methods and non-chattering high 
gains methods. 

4.6.1 Lyapunov-based methods 

This class of methods is based on the Lyapunov direct method and is based on (Spong and 
Vidyasagar, 2006). The main problem encountered by Lyapunov-based class of RA control 
algorithms is the so-called chattering effect which results from commutation of the 
supplementary signal. This behavior creates control discontinuities. Research efforts have 
been accomplished that cater for this undesirable chattering effect. The algorithm proposed 
by (Cai and Goldenberg 1988) is a tentative answer to the problem of chattering. The issue of 
chattering represents a predilection area for the applicability of KB methods, since 
chattering can be modeled using human expertise. 

4.6.2 Variable structure methods 

Variable structure methods, such as the one proposed by (Slotine, 1985) are based on high- 
speed switching feedback control where the control law switches to different values 
according to some rule. This class of methods drives the nonlinear plant's trajectory onto an 
adequately designed sliding surface in the phase space independently of modeling errors. In 
(Chen and Papavassilopoulos, 1991) four position control laws have been analyzed and 
compared for a single-arm RA dynamics with bounded disturbances, unknown parameter, 
and unmodeled actuator dynamics. Although very robust to system's disturbance and 
simplifying the complexity of control laws implementation, these methods suffer from 
undesirable control chattering at high frequencies. 

4.6.3 Non-chattering high gains methods 

The non-chattering high gains class of methods is based on the singular perturbation 
theory and considers two time scales. This class avoids the chattering effect (Samson, 
1987). However, robustness in this case is guaranteed by the choice of a nonlinear gain 
which is calculated from the a priori knowledge of the parametric uncertainties and from 
the model chosen for control calculation. The resulting control can be considered as a 
regulator which automatically adapts the gains in accordance with the displacement 
errors (Seraji, 1989) and uses high gains only when these are needed, for instance when 
displacement error is large. 

4.6.4 Example of robust control scheme 

In this case, the parameters are not known but their range of variations is known. The basic 
idea of this method is to add a compensating term to the control which is obtained from an a 
priori estimated model. This compensation term takes into account the parameters bounds 
and tries to compensate the difference between the estimated and the real parameters of the 
robot. This makes possible an improved trajectory tracking and provides robustness with 
respect to the parametric errors. Several schemes of RA robust control have been studied 

Knowledge-Based Control for Robot Arm 


and compared (Abdallah et ah, 1991). As an example, only one robust algorithm is described 
here, whose control law is given by : 

r(0 = M (q)(u + Su) + C (q,q)q+ G a {q) 


* Mo, Co and Go are the a priori estimates of M, C and G, respectively. 

* 8u is the compensating control supplement. 

* u is given by a PD compensator of the form: 



The additional control Su is chosen so as to ensure robustness of the control by 
compensating the parametric errors. Stability must be guaranteed. A reformulation of this 
control gives: 


x - 


+ B{ 

Su + r](u,q,q) 

E r =Cx 

A, B, C and x are given by 

A = 

" I ' 

-K -K 

p v _ 

B = 

"0 n 

C = [a 


x = 


with a is a diagonal constant positive-definite matrix of rank n, and 
rj(u,q,q) = E(q)Su + E t M +M~ 1 (q)AH(q,q) 
E{q) = M\q)M {q)-l 



AH(q,q)=[C (q,q)-C(q,q)]q+[G (q)-G] 


Stability is granted only if the vector r/(u,q,q) is bounded. These bounds are estimated on the 
worst-case basis. Furthermore, under the assumption that there exists a function p such that: 

\8u\< p(e,e,t) 


\r]\< p{e,e,t) 
the compensating control 8u can be obtained from : 

Su ■■ 

-p{e,e,t) T ± r if HEJI^O 






Robot Arms 

This last control 5m presents a chattering effect due to the discontinuities in (23). This 
phenomenon can cause unwanted sustained oscillations. Another control has been proposed 
which reduces these unwanted control jumps, (Cai and Goldenberg, 1988) as given in 
equation (24). 

Su - 

-p(e,e,t)-rr J j if \\E 1 \\>£ 





The robust control scheme is represented in Figure 5. 



i r q 


■>- k v _^<S-^>-^ M^q) -»LC 
I ' ' It ' ' + i 




k p 

C (q,q)q+G (q) "*" 

Fig. 5. Spong and Vidyasagar's robust control algorithm 

5. Implementation 

5.1 Basic architecture 

The basic architecture is described in Figure 6. The general menus are described in Figure 7. 
The main program is started from the Matlab™ workspace window. Simulation triggers the 
Simulink™ environment and results can be obtained under the Matlab™ graphics window 
or in the Simulink™ environment (e.g. through scopes). Results can also be stored in *.MAT 
data files to be later handled by the knowledge base, through the interface. 
The overall system is written in the Matlab™/ Simulink™ environment 
( One of the main reasons for this choice is the possibility of 
interfacing it with the developed knowledge base using higher programming language, 
such as Microsoft Visual C++™ (MVC++™), under Windows™. The knowledge base is 
developed under a commercial expert system generator that supports interfacing with 
external MVC++™ executable programs. The other fundamental reason is the Matlab™ 
control systems library functions and specialized toolboxes, e.g. control systems toolbox and 
identification toolbox needed for adaptive control. Although, many languages / 
environments can be identified as suitable for the solution to our RA problem, we do not 
know, however whether any of these is interfaceable with the chosen expert system 

Knowledge-Based Control for Robot Arm 


Numeric Environment 


Starting the simulation 

Numeric Environment 

SIMULINK sub-systems library 

System bloc-diagram 


Workspace results 

- Immediate use 

- Knowledge base use 

m-files and 


Symbolic/ Numeric 
Numeric/ Symbolic Interface 

Symbolic Environment 

Knowledge Base 

Fig. 6. Implemented Architecture 


Robot Arms 



Menu 1: Type of Robot 




4- Close 

(1, 2, 3) 


Robot Parameters Introduction 

(1, 2, 3, 4) 

Menu 2: Control Choice 


Known Parameter Case 

2.1 Computed Torque 

2.2 Passivity 
Unknown Parameter Case 

3.1 Adaptive Computed Torque 

3.2 Passive adaptive 
Robust Control 

Block Diagram 
Curve Plotting 

Control Parameters 


System s-blocks 



Calculation of response 
time and responses 

Saving in .MAT files 

Menu 3: Curve 

1- Positions + errors 

2- Velocities + errors 

3- Control law 

4- Adaptive Parameters 

5- Close 


Curves Plotting 

Fig. 7. Exploitation Environment 

Knowledge-Based Control for Robot Arm 


5.2 Plant configuration 

Some of the available RA configuration have been used in the implementation, as examples. 
PLANAR, SCARA and RZERO are chosen because they are widely used and they represent 
different classes of configurations, as described in Figure 8,9,10 below. Of course the system 
is open to other configurations through the Matlab™ environment. 

Fig. 8. PLANAR Robot 

Fig. 9. SCARA Robot 

Fig. 10. RZERO Robot 







70 Robot Arms 

5.3 Knowledge organization: from worlds to objects 

Knowledge organization is handled by the inferential (exploration) environment. The 
knowledge is organized in different levels : 

5.3.1 Global vs. local search 

i. At the global level : this is done through the partitioning of the KB in coherent thematic 
sets of rules (each of these sets is called a world). These worlds can be hierarchically 
organized offering the possibility of describing global knowledge (ascending worlds, 
fathers worlds) and local knowledge (descending worlds, descendants worlds). 

ii. At the local level : this is done through the expertise structuring using a network of 
classes and objects. A class is defined as an abstract object maker. Objects represent a 
declarative knowledge described by sets of particular data (called attributes) and the 
corresponding attributes values. Rules allow description of the expert knowledge using 
objects and / or classes. They are expressed in the conventional IF-THEN form. We only 
operate the KB as a stand-alone module to test its behaviour against that of human 
experts for further refinement. 

5.3.2 The existing worlds 

Worlds are coherent sets of rules and represent independent and encapsulated entities 

ensuring a high degree of knowledge modularity and maintenance. A world can be created 

according to the type of knowledge that is handled {e.g. set of rules dealing with PID 

controller). Hierarchical representation is available. This allows the organization of 

knowledge from the more general to the more specific (top-down fashion). 

i. The meta-level nucleus (MLN) 

The meta-level nucleus (MLN) represents the world that governs the navigation from one 

world to the other (or from one individual KB to the other). It is placed at the highest level of 

the hierarchy. All remaining worlds are sub-worlds of the MLN. All the rules of the MLN 

world (called a father world) are applicable in all the other inheriting sub-worlds (called 

descendants). Its description is given by the following structure: 

World name : MLN 

Father World : None 

Descendants Worlds %List of all the other remaining worlds% 

ii. Pruning worlds 

Based on the data and specifications provided by the user, the pruning worlds help in 
guiding the search towards the specialized individual knowledge base (1KB) as soon as 
possible. This pruning process is efficient in concentrating on the specific knowledge of 
interest at this or that particular step of the reasoning process. Once these worlds are 
selected, they become active while all other worlds remain inactive. The pruning worlds 
consist of the following : 

Wl World : DynamicModelKnown 

% Describing the rules applicable for this case% 
Wll Sub-World : SpeedSlow 

Will Sub-sub-World 
W112 Sub-sub-World 
W113 Sub-sub-World 
W114 Sub-sub-World 
W12 Sub-World : SpeedHigh 


Knowledge-Based Control for Robot Arm 


(Same sub-worlds as in Wll above) 
W13 Sub-World : SpeedUnknown 
(Same sub-worlds as in Wll above) 
W14 Sub-World : SpeedOthers 
(Same sub-worlds as in Wll above) 
W15 Sub-World : SpeedNoImportance 
(Same sub-worlds as in Wll above) 

W2 World : DynamicModelUnknown 

% Describing the rules applicable for this case% 

W21 Sub-World : SpeedSlow 

W211 Sub-sub-World 
W212 Sub-sub-World 
W213 Sub-sub-World 
W22 Sub-World : SpeedHigh 
(Same sub-worlds as in W21 above) 
W23 Sub-World : SpeedUnknown 
(Same sub-worlds as in W21 above) 
W14 Sub-World : SpeedOthers 
(Same sub-worlds as in W21 above) 
W24 Sub-World : SpeedNoImportance 
(Same sub-worlds as in W21 above) 


W3 World : DynamicModelOthers 

% Describing the rules applicable for this case% 
W31 Sub-World : SpeedSlow 

W311 Sub-sub-world : ParametersKnown 

W312 Sub-sub-World : ParametersUnknown 

W313 Sub-sub-World : ParametersOthers 

W314 Sub-sub-World : ParametersNoImportance 

W32 Sub-World 
(Same sub-worlds 
W33 Sub-World 
(Same sub-worlds 
W14 Sub-World 
(Same sub-worlds 
W34 Sub-World 
(Same sub-worlds 


as in W31 above) 

as in W31 above) 

as in W31 above) 

as in W31 above) 

W4 World : DynamicModelNoImportance 

% Describing the rules applicable for this case% 

W41 Sub-World : SpeedSlow 

W411 Sub-sub-World 
W412 Sub-sub-World 
W413 Sub-sub-World 
W414 Sub-sub-World 
W42 Sub-World : SpeedSlow 
(Same sub-worlds as in W41 above) 
W43 Sub-World : SpeedUnknown 
(Same sub-worlds as in W41 above) 
W14 Sub-World : SpeedOthers 
(Same sub-worlds as in W41 above) 
W44 Sub-World : SpeedNoImportance 
(Same sub-worlds as in W41 above) 


72 Robot Arms 

For each sub-world Wij (i = 1 to 4 ; j = 1 to 4 ), there correspond a sub-sub-world for RA 
parametric description. These pruning worlds give a preliminary guide to a world 
(corresponding to the chosen algorithm) where the initial search is to be started. If the 
results given by this algorithm are satisfactory then choose this algorithm as a solution. 
Otherwise, either fine-tune the obtained solution within the same world (or other eventual 
specialized sub-worlds) or go back to the meta-level nucleus (MLN) for further search. 

Hi. The worlds describing the RA algorithms 

For each RA algorithm, we have developed a world. Each of these worlds can be considered 
as an independent KB (1KB). Some of the worlds have very few rules. Each 1KB can 
obviously be incremented, provided the expertise is available. We have considered worlds 
and sub-worlds partially describing the following algorithms. 

World PID 

Sub-worlds : Basic PID, Gravitational PID, Adaptive PID, Robust PID. 
World Computed Torque (known parameters) 

Sub-worlds PD control , Predictive control 
World Compensators 

Sub-worlds Spong's adaptive compensator, Amestegui's adaptive 

World Adaptive Control 

Sub-worlds linearized adaptive, passive adaptive 
World Robust Control 

Sub-worlds : Robust PID, large gains, variable structure control (VSC) 

5.4 Example : Fuzzy rule involving fuzzy attributes in its conclusion. 

If the user does not know the RA parameters but knows the dynamic model and that the RA 
is slow, then a tentative algorithm is the passive adaptive or the linear adaptive. In the 
conclusion, we can therefore translate this by a certainty factor (CF) of 50 meaning that 
either algorithm can be used with a degree of equal certainty. The CF can of course be 
changed according to the available knowledge and refined expertise. This rule can be 
expressed by : 

WORLD : MLN % New world % 

DESCENDANTS WORLDS % Here is a list of all worlds % 

Rule TryPassvAdaptCF60 % This is the name of the rule % 

CHAINING : forward 

PRIORITY : 40 % can be changed from to 100 % 


IF Guide .DynamicModelKnown_VelocitySlow = TRUE 

AND RA. Parameters = "don't know" 

AND Algorithm. AlgoActivation = "Activable" 

THEN TryAlgorithm. PassivAdaptivFuzzy = TRUE CF 50 

AND Guide. PassivAdaptivCF50 = TRUE 

Other situations can be described in a similar manner. 

6. Conclusion 

We have described some foundational steps to solve the RA control using knowledge base 
systems approach. More specifically, this research work reports some features of KBC 

Knowledge-Based Control for Robot Arm 73 

approach as applied to some RA control algorithms spanning PID through adaptive, and 
robust control. As such, this research represents an early contribution towards an objective 
evaluation of the effectiveness of KBC as applied to RA control. A unification of the 
diversified works dealing with RAs, while concentrating on KBC as an alternative control 
method, is therefore made possible. The adopted knowledge base systems approach is 
known for its flexibility and conveys a solution better than that provided by numerical 
means alone since it incorporates codified human expertise on top of the algorithms. The 
fundamental constraints of the proposed method is that it requires an elicitation of human 
expertise or extensive off-line trials to construct this expertise. This expertise codification has 
a direct impact on the size of the KB and on the rapidity of the user-defined problem 
solution. Like any KBS method, the proposed procedure also requires a diversified coverage 
of the working domain during the elicitation stage to obtain a richer KB. As a consequence, 
the results report only some aspects of the overall issue, since these describe only a fragment 
of the human expertise for a small class of control algorithms. Much work is still required on 
both sides, i.e., robotics and KBS in order to further integrate these two entities within a 
single one while meeting the challenges of efficient real-life applications. 

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Distributed Nonlinear Filtering Under Packet 
Drops and Variable Delays for Robotic 

Visual Servoing 

Gerasimos G. Rigatos 

Industrial Systems Institute 

1. Introduction 

State estimation over communication networks is in use by many robotic applications in 
industry in defense systems, as well as in several exploration and surveillance tasks. The 
incorporation of a communication network in the control loop has enabled to perform 
multi-sensor fusion and distributed information processing, thus improving significantly 
the autonomy and reliability of robotic systems (Medeiros et al., 2008), (Olfati-Saber, 2006), 
(Watanabe & Tzafestas, 1992). It has been shown that scalable distributed state estimation can 
be achieved for robotic models, when the measurements are linear functions of the state and 
the associated process and measurement noise models follow a Gaussian distribution (Mahler, 
2007), (Nettleton et al., 2003). The results have been also extended to the case of nonlinear 
non-Gaussian dynamical systems (Rigatos, 2010a), (Makarenko & Durrant-Whyte, 2006). 
An issue which is associated to the implementation of such networked control systems is how 
to compensate for random delays and packet losses so as to enhance the accuracy of estimation 
and consequently to improve the stability of the control loop. The idea of incorporating 
delayed measurements within a Kalman Filter framework is a possible solution for the 
compensation of network-induced delays and packet losses, and is also known as update with 
out-of-sequence measurements (Bar Shalom, 2002). The solution proposed in (Bar Shalom, 
2002) is optimal under the assumption that the delayed measurement was processed within 
the last sampling interval (one-step-lag problem). There have been also some attempts to 
extend these results to nonlinear state estimation (Golapalakrishnan et al., 2011), (Jia et al., 
2008). More recently there has been research effort in the redesign of distributed Kalman 
Filtering algorithms for linear systems so as to eliminate the effects of delays in measurement 
transmissions and packet drops, while also alleviating the one-step-lag assumption (Xia et al., 
2009). This chapter presents an approach to distributed state estimation-based control 
of nonlinear systems, capable of incorporating delayed measurements in the estimation 
algorithm while being also robust to packet losses. 

First, the chapter examines the problem of distributed nonlinear filtering over a 
communication/sensors network, and the use of the estimated state vector in a control 
loop. As a possible filtering approach, the Extended Information Filter is proposed (Rigatos, 
2010a). In the Extended Information Filter the local filters do not exchange raw measurements 
but send to an aggregation filter their local information matrices (local inverse covariance 
matrices which can be also associated to Fisher Information Matrices) and their associated 

78 Robot Arms 

local information state vectors (products of the local information matrices with the local state 
vectors) (Lee, 2008). The Extended Information Filter performs fusion of state estimates from 
local distributed Extended Kalman Filters which in turn are based on the assumption of 
linearization of the system dynamics by first order Taylor series expansion and truncation 
of the higher order linearization terms. Moreover, the Extended Kalman Filter requires the 
computation of Jacobians which in the case of high order nonlinear dynamical systems can be 
a cumbersome procedure. This approach introduces cumulative errors to the state estimation 
performed by the local Extended Kalman Filter recursion which is finally transferred to 
the master filter where the aggregate state estimate of the controlled system is computed. 
Consequently, these local estimation errors may result in the deterioration of the performance 
of the associated control loop or even risk its stability (Rigatos, 2009),(Rigatos et al., 2009). 
To overcome the aforementioned weaknesses of the Extended Information Filter 
a derivative-free approach to Extended Information Filtering has been proposed 
(Rigatos & Siano, 2010), (Rigatos, 2010c). The system is first subject to a linearization 
transformation and next state estimation is performed by applying the standard Kalman 
Filter to the linearized model. At a second level, the standard Information Filter is used to 
fuse the state estimates obtained from local derivative-free Kalman filters running at the local 
information processing nodes. This approach has significant advantages because unlike the 
Extended Information Filter (i) is not based on local linearization of the system dynamics (ii) 
it does not assume truncation of higher order Taylor expansion terms thus preserving the 
accuracy and robustness of the performed estimation, (iii) it does not require the computation 
of Jacobian matrices. 

At a second stage the chapter proposes a method for the compensation of random delays and 
packet drops which may appear during the transmission of measurements and state vector 
estimates, and which the can cause the deterioration of the performance of the distributed 
filtering-based control scheme (Xia et al., 2009), (Schenato, 2007), (Schenato, 2008). Two cases 
are distinguished: (i) there are time delays and packet drops in the transmission of information 
between the distributed local filters and the master filter, (ii) there are time delays and packet 
drops in the transmission of information from distributed sensors to each one of the local 
filters. In the first case, the structure and calculations of the master filter for estimating the 
aggregate state vector remain unchanged. In the second case, the effect of the random delays 
and packets drops has to be taken into account in the redesign of the local Kalman Filters, 
which implies a modified Riccati equation for the computation of the covariance matrix of the 
state vector estimation error, as well as the use of a correction (smoothing) term in the update 
of the state vector's estimate so as to compensate for delayed measurements arriving at the 
local Kalman Filters. 

Finally, the chapter shows that the aggregate state vector produced by a derivative-free 
Extended Information Filter, suitably modified to compensate for communication delays and 
packet drops, can be used for sensorless control and robotic visual servoing. The problem 
of visual servoing over a network of synchronised cameras has been previously studied in 
(Schuurman & Capson, 2004). In this chapter, visual servoing over a cameras network is 
considered for the nonlinear dynamic model of a planar single-link robotic manipulator. It 
is assumed that the network on which the visual servoing loop relies, can be affected by 
disturbances, such as random delays or loss of frames during their transmission to the local 
processing vision nodes. The position of the robot's end effector in the cartesian space (and 
equivalently the angle of the robotic link) is measured through m cameras. In turn, these 
measurements are processed by m distributed derivative-free Kalman Filters thus providing 

Distributed Nonlinear Filtering Under Packet 

Drops and Variable Delays for Robotic Visual Servoing 79 

m different estimates of the robotic link's state vector. Next, the local state estimates are fused 
with the use of the standard Information Filter. After all, the aggregate estimation of the state 
vector is used in a control loop which enables the robotic link to perform trajectory tracking. 
The structure of the chapter is as follows: In Section 2 the Extended Kalman Filter is 
introduced and its use for state estimation of nonlinear dynamical systems is explained. In 
Section 3 a derivative-free Kalman Filtering approach to state estimation of nonlinear systems 
is analyzed. In Section 4 the derivative-free Extended Information Filter is formulated as 
an approach to distributed state estimation for nonlinear systems, capable of overcoming 
the drawbacks of the standard Extended Information Filter. In Section 5 the problem of 
distributed filtering under random delays and packet drops is analyzed. The results are 
also applied to distributed state estimation with the use of the derivative-free Extended 
Information Filter. In Section 6 the previously described approach for derivative-free 
Extended Information Filtering under communication delays and packet drops is applied 
to the problem of state estimation-based control of nonlinear systems. As a case study the 
model of a planar robot is considered, while the estimation of its state vector is performed 
with the use of distributed filtering through the processing of measurements provided by 
vision sensors (cameras). In Section 7 simulation tests are presented, to confirm the efficiency 
of the proposed derivative-free Extended Information Filtering method. Finally, in Section 8 
concluding remarks are given. 

2. Extended Kalman Filtering for nonlinear dynamical systems 

2.1 The continuous-time Kalman Filter for the linear state estimation model 

First, the continuous-time dynamical system of Eq. (1) is assumed (Rigatos & Tzafestas, 2007), 
(Rigatos, 2010d): 

' x(t) = Ax(t) + Bu(t) + w(t), t>t 
z(t) = Cx(t) + v(t), t>t 

where x£R m is the system's state vector, and zeRP is the system's output. Matrices 
A,B and C can be time-varying and w(t),v(t) are uncorrelated white Gaussian noises. The 
covariance matrix of the process noise w(t) is Q(t), while the covariance matrix of the 
measurement noise is R(t). Then, the Kalman Filter is a linear state observer which is given 

' x = Ax + Bu + K[z - Cx], x(t ) =0 

K(t) = PC T R- 1 (2) 

P = AP + PA T + Q- PC T R- l CP 

where x(t) is the optimal estimation of the state vector x(t) and P(t) is the covariance matrix 
of the state vector estimation error with P(£rj) = Po- The Kalman Filter consists of the system's 
state equation plus a corrective term K[z — Cx] . The selection of gain K corresponds actually to 
the solution of an optimization problem. This is expressed as the minimization of a quadratic 
cost functional and is performed through the solution of a Riccati equation. In that case the 
observer's gain K is calculated by K = PC T R~^ considering an optimal control problem for 
the dual system (A , C ), where the covariance matrix of the estimation error P is found by 
the solution of a continuous-time Riccati equation of the form 

80 Robot Arms 

P = AP + PA T + Q-PC T R~ 1 CP (3) 

where matrices Q and R stand for the process and measurement noise covariance matrices, 

2.2 The discrete-time Kalman Filter for linear dynamical systems 

In the discrete-time case a dynamical system is assumed to be expressed in the form of a 
discrete-time state model (Rigatos & Tzafestas, 2007), (Rigatos, 2010d): 

x(k + 1) = A(k)x(k) + L(k)u(k) + w(k) 

z(k) = Cx(k) + v(k) {) 

where the state x(k) is a m-vector, w(k) is a m-element process noise vector and A is a m x m 
real matrix. Moreover the output measurement z(fc) is a p-vector, C is an p x m-matrix of real 
numbers, and v(k) is the measurement noise. It is assumed that the process noise w(k) and 
the measurement noise v(k) are uncorrelated. 

Now the problem of interest is to estimate the state x(k) based on the sequence of output 
measurements z(l),z(2),- ■ ■ ,z(fc). The initial value of the state vector x(0), and the initial 
value of the error covariance matrix P(0) is unknown and an estimation of it is considered, 
i.e. x(0)= a guess of £[x(0)] and P(0) = a guess of Cou[x(0)]. 

For the initialization of matrix P one can set P(0) = M, with A > 0. The state vector 
x(k) has to be estimated taking into account x(0), P(0) and the output measurements Z = 
[z(l),z(2),- ■ ■ ,z(fc)] r , i.e. x(k) = ot„(x(Q)),P(0),Z(k)). This is a linear minimum mean 
squares estimation problem (LMMSE) formulated as x(k + 1) = a n+ \{x (k),z(k + 1)). The 
process and output noise are white and their covariance matrices are given by: E[iu(i)w T (j)] = 
QS(i-j) and E[v(i)v T (i)}=RS(i-i). 

Using the above, the discrete-time Kalman filter can be decomposed into two parts: i) time 
update (prediction stage), and ii) measurement update (correction stage). The first part 
employs an estimate of the state vector x(k) made before the output measurement z(k) is 
available (a priori estimate). The second part estimates x(k) after z(k) has become available (a 
posteriori estimate). 

• When the set of measurements Z~ = {z(l), ■ ■ ■ ,z(k — 1)} is available. From Z~ an a priori 
estimation of x(k) is obtained which is denoted by XT (k)= the estimate of x(k) given Z . 

• Whenz(fc) is available, the output measurements set becomes Z = {z(l),- ■ ■ ,z(fc)}, where 
f (k)- the estimate of x(k) given Z. 

The associated estimation errors are defined by e~ (k) = x(k) — XT (k)= the a priori error, 
and e(k) = x(k) — x(k)= the a posteriori error. The estimation error covariance matrices 


associated with x(k) and x{k) are defined as P~ (k) = Cov[e~{k)\ = E[e~(k)e~(k) ] 
and P(jfc) = Cov[e(k)] = E[e(k)e T (k)] (Kamen & Su, 1999). From the definition 

of the trace of a matrix, the mean square error of the estimates can be written as 
MSE(x-(k)) = E[e- (k)e- [k) T ] = tr{P~{k)) and MSE(x(k)) = E[e(k)e T (k) = tr(P(k)). 

Finally, the linear Kalman filter equations in cartesian coordinates are 

Distributed Nonlinear Filtering Under Packet 

Drops and Variable Delays for Robotic Visual Servoing 


measurement update: 

time update: 

P- (k)C T [C-P- {k)C T + R}- 1 
x- (Jfc) + K(k)[z{k) - Cx- (Jfc)] 

P(k) = P- (k) - K(k)CP- (k) 



:A(k)P(k)A T (k) + Q(k) 
A{k)x{k) + L{k)u{k) 


2.3 The extended Kalman Filter 

State estimation can be also performed for nonlinear dynamical systems using the Extended 
Kalman Filter recursion (Ahrens & Khalil, 2005), (Boutayeb et al., 1997). The following 
nonlinear state model is considered (Rigatos, 2010a), (Rigatos & Tzafestas, 2007): 


x (k + 1) = (p(x(k)) + L(k)u{k) + w(k) 
z(k)=y(x(k)) + v(k) 

where x£R mx is the system's state vector and z£R px is the system's output, while w(k) 
and v(k) are uncorrected, zero-mean, Gaussian zero-mean noise processes with covariance 
matrices Q(k) and R(k) respectively. The operators <p(x) and 7(x) are vectors defined as 
4>(x) = [<pi(x),cp2(x),- ■ -,(pm{x)] T , and y(x) = [71W/T2W/ ••• ,7pM] T , respectively. It is 
assumed that ip and 7 are sufficiently smooth in x so that each one has a valid series Taylor 
expansion. Following a linearization procedure, <p is expanded into Taylor series about x : 

<p(x(k)) = cp(x(k)) + /,(*(*))[*(*) - x(k)} + ■ 
where /<*(*) is the Jacobian of <p calculated at x(k): 


k( x ) = jfc !*=*(*) 

I w; 2*7 '" 5^: > 

d(p2 dtyi d(p2 

d<p ni d(p n , dip,,, 

\ dxi dx 2 Sx„, / 

Likewise, 7 is expanded about x (k) 


7(x(fc)) = y(x-(k)) + / 7 [x(fc) - x-(k)] + ■■■ (10) 

where x~ (A:) is the estimation of the state vector x(k) before measurement at the fc-th instant 
to be received and x (k) is the updated estimation of the state vector after measurement at the 
fc-th instant has been received. The Jacobian Jy(x) is 

dx l 


dx\ dx2 
dj2 dj2 
dx\ dx2 

3Tjl ^2l 
\ dx\ 3x2 

3 7i\ 
' dx m > 

' ' dx,„ 

37 P 
' ' dx„, ) 



Robot Arms 

The resulting expressions create first order approximations of <p and 7. Thus the linearized 
version of the system is obtained: 

x(k + 1) = <p(x(k)) + /♦(*(*))[*(*) - *(*)] + w(k) 

z(fc) = y(x-(k)) + } 7 (x-(k))[x(k) - x-(k)} + v(k) [iA) 

Now, the EKF recursion is as follows: First the time update is considered: by x(k) the 
estimation of the state vector at instant k is denoted. Given initial conditions XT (0) and P~ (0) 
the recursion proceeds as: 

• Measurement update. Acquire z(k) and compute: 

K(k) = P- (k)jT(x- (*))• [/ 7 (f- (k))P- (k)jT(x- (k)) + R(k)} 

x(k)=x-(k)+K(k)[z(k)- 7 (x-(k))} 

P(k)=p-(k)-K(k)J 7 (x-(k))p-(k) 

• Time update. Compute: 

P-(k + l)=J tp (x(k))P(k)Jl(x(k)) + Q(k) 
x-(k + l)=<p(x{k)) + L(k)u{k) 

The schematic diagram of the EKF loop is given in Fig. 1. 

a priori quantities 

Available measurements 


Estimate of: x(k):x (k) 
Estimation error: e~(k) 

Error covariance: P~{k) 

E[e-(k)(er(k)) T } = tr(P(k)) 

z(k) becomes 

a posteriori quantities 

Available measurements 

Z = {z(l),->z(*)} 
Estimate of: x(k) : x(k) 
Estimation error: e.(k) 

Error covariance: P(k) 


E[e(k)(e(k)f] = tr(P(k)) 



Fig. 1. Schematic diagram of the EKF loop 

3. Derivative-free Kalman Filtering for a class of nonlinear systems 

3.1 State estimator design through a nonlinear transformation 

It will be shown that through a nonlinear transformation it is possible to design a state 
estimator for a class of nonlinear systems, which can substitute for the Extended Kalman 
Filter. The results will be generalized towards derivative-free Kalman Filtering for nonlinear 
systems. The following continuous-time nonlinear single-output system is considered 
(Marino, 1990),(Marino & Tomei, 1992) 

Distributed Nonlinear Filtering Under Packet 

Drops and Variable Delays for Robotic Visual Servoing 


x = f(x) + q (x, u) + l% =1 0iqi(x, u), or 

x = f( x ) + q (x, u) + Q(x, u)6 xeR n , ueR m , 6eR? (15) 

2 = h(x), zeR 

with q, : R"xR m ->■ R" , 0<i<p, f : R"^R", h : R"^R, smooth functions, h(x ) = 0, 
qo(x,0) = for every x G R n ; x is the state vector, u(x,t) : R + ^-R m is the control which is 
assumed to be known, 6 is the parameter vector which is supposed to be constant and y is the 
scalar output. 

The first main assumption on the class of systems considered is the linear dependence on 
the parameter vector 8. The second main assumption requires that systems of Eq.(15) are 
transformable by a parameter independent state-space change of coordinates in R n 

into the system 

C=T(x), T(x ) = 

t = A c Z+ipo(z,u)+Y(z,u)6 




/0 1 
' 001 

A c 

\0 • • • 0/ 




Q = (1 

and xpi : RxR"'^i-R n smooth functions for i = 0, ■■■ ,p. The necessary and sufficient 
conditions for the initial nonlinear system to be transformable into the form of Eq.(17) have 
been given in (Marino, 1990),(Marino & Tomei, 1992), and are summarized in the following: 

(i) rank{dh(x),di h(x), ■ ■ ■ ,d L „-ih(x)} = n, VxGR" (which implies local observability). It 

(Vh)f and the repeated Lie 

is noted that Lrh(x) stands for the Lie derivative Lrh(z 
derivatives are recursively defined as L° f h = h for i 

0,V f h 

L f V7 l h 

VV^hf for i 


(ii) [adKg^dLg] = 0, 0</, j<n — 1. It is noted that ad' f g stands for a Lie Bracket which is 

defined recursively as ad\g = [f,ad'7 l ]g with ad° f g = g and adfg = [f,g] = V gf — V fg. 

(iii) [q ir adKg] = 0, 0</<p, 0<j<n -2V u€R m . 

(iv) the vector fields ad' f g, 0<i<n — 1 are complete, in which g is the vector field satisfying 

I dh \ 


\d(L" f - L h)J 


Robot Arms 

Then for every parameter vector 9, the system 

I = A c C + fo(z,u) + jf i=i eiiPi{z,u) + K(z - C c t) 

is an asymptotic observer for a suitable choice of K provided that the state x(t) is bounded, 
with estimation error dynamics 


e=(A c - KC C 

( -k x i o ■ ■ ■ o\ 

-k 2 1 ■ ■ ■ 

-fc„_! • • • 1 

V -k„ ■ ■ ■ 0/ 


The eigenvalues of A c — KC C can be arbitrarily placed by choosing the vector K, since they 
coincide with the roots of the polynomial s" + k\S n + ■ ■ ■ + k n . 

3.2 Derivative-free Kalman Filtering for nonlinear systems 

Since Eq. (21) provides an asympotic observer for the initial nonlinear system of Eq. (15) 
one can consider a case in which the observation error gain matrix K can be provided by 
the Kalman Filter equations given initially in the continuous-time KF formulation, or in 
discrete-time form by Eq. (5) and Eq. (6). The following single-input single-output nonlinear 
dynamical system is considered 

xW = /(*,*) + g{x,t)u(x,t) 
x is the system's output, and f(x,t), g(x,t) are nonlinear functions. 


where z = x is the system's output, and f(x, t), g(x, t) are nonlinear functions. It can be 
noticed that the system of Eq. (23) belongs to the general class of systems of Eq. (15). 

Assuming the transformation £, 

ft— 1-1 


r n,andi("> = f{x,t) + g{x,t)u(x,t) 

v(£, t), i.e. £ n = p(£, t), one obtains the linearized system of the form 

tl = h 
£2 = £3 


which in turn can be written in state-space equations as 

( tl\ 

/o 1 ■ 
001 • 




000 ■ 

■ 1 


V Cn ) 

^000 ■ 


V u ) 




2 = (1 • • • 0) C (26) 

The system of Eq. (25) and Eq. (26) has been written in the form of Eq. (17), which means that 
Eq. (21) is the associated asymptotic observer. Therefore, the observation gain K appearing in 
Eq. (21) can be found using either linear observer design methods (in that case the elements 

Distributed Nonlinear Filtering Under Packet 

Drops and Variable Delays for Robotic Visual Servoing 85 

of the observation error gain matrix K have fixed values), or the recursive calculation of the 
continuous-time Kalman Filter gain described in subsection 2.2. If the discrete-time Kalman 
Filter is to be used then one has to apply the recursive formulas of Eq. (5) and Eq. (6) on the 
discrete-time equivalent of Eq. (25) and Eq. (26). 

4. Derivative-free Extended Information Filter 

4.1 Calculation of local estimations in terms of EIF information contributions 

Again the discrete-time nonlinear system of Eq. (7) is considered. The Extended Information 
Filter (EIF) performs fusion of local state vector estimates which are provided by local 
Extended Kalman Filters, using the Information matrix and the Information state vector (Lee, 
2008), (Manyika & Durrant-Whyte, 1994). The Information Matrix is the inverse of the 
state vector covariance matrix, and can be also associated to the Fisher Information matrix 
(Rigatos & Zhang, 2009). The Information state vector is the product between the Information 
matrix and the local state vector estimate 

Y(k)=p- 1 (k) = I(k) 

y(k) = p-{k)- 1 x(k) = Y{k)x{k) 

The update equation for the Information Matrix and the Information state vector are given by 

Y{k) = p-{k)- l +f 1 {k)R-\k)] 1 {k) 
= Y-(k) + I(k) 

9(h) = r (*) + #(*)r(*) _1 [z(k) - 7 ( X (k)) + i 7 (k) x - (k)} 

= y-(k) + i(k) 


I{k) = jl(k)R(k) 1 /-y(fc) is the associated information matrix and 
i(k) = /^(fc)R(fc) -1 [(z(k) — j(x(k))) + JjX~ (k)] is the information state contribution 

The predicted information state vector and Information matrix are obtained from 



y-(k)=P-(k) X x~{k) 
Y-{k)=p-(ky l = [J^k)p-{k)J^k) T + Q{k)]-^ 

The Extended Information Filter is next formulated for the case that multiple local sensor 
measurements and local estimates are used to increase the accuracy and reliability of the 
estimation. It is assumed that an observation vector z' (k) is available for N different sensor 
sites i = 1, 2, • • • , N and each sensor observes a common state according to the local 
observation model, expressed by 

z l (k) =y{x{k)) + v i {k), / = 1,2,--- ,N (32) 

where the local noise vector v' (k) ~ N(0, R' ) is assumed to be white Gaussian and uncorrelated 
between sensors. The variance of a composite observation noise vector v^ is expressed in terms 
of the block diagonal matrix 

R(k) = diag^ik),-- ■ ,R N (k)} T (33) 


Robot Arms 

The information contribution can be expressed by a linear combination of each local 
information state contribution i' and the associated information matrix 7' at the z'-th sensor 


m = Ytiiy (wrvm - 7 k (x(k))+r 7 (k) X -(k)} 

Hk) = Ll 1 r 1 T (k)R i (k)- i r 1 (k) 

Using Eq. (34) the update equations for fusing the local state estimates become 


y(k)=r(k)+LtiJV(k)R'(k)- 1 [z'(k)-7 k (x(k))+J' 1 (k)x-(k)] 

Y{k) = Y-(fc) +Ll 1 r 7 T (k)R i (k)- 1 J 1 7 (k) 

It is noted that in the Extended Information Filter an aggregation (master) fusion filter 
produces a global estimate by using the local sensor information provided by each local filter. 

Local Filter 

ifii Aft 



v,{i) Yylk) 

f ' 

Time update 

\[{k] Y~lk) 

Local Filter 


k4:i i A' 


y N jk) Y K jk) 


;gregation Filter 



Time update 

Fig. 2. Fusion of the distributed state estimates with the use of the Extended Information 

As in the case of the Extended Kalman Filter the local filters which constitute the Extended 
Information Filter can be written in terms of time update and a measurement update equation. 

Measurement update: Acquire z(fc) and compute 


Y(k) = P~(k) 
or Y(Jfc) = Y-(fc) + I(k) where I(k) 

E{k)R{k)^] 7 {k) 

J 7 (k)R-t(k)J 7 (k) 


Time update: Compute 

Y-(fc + l) 

r (k) + tfmtk)- 1 [z(k) - 7 (f (*)) + J 7 (k)x- (k)] 

or y(k) = y- (k) + i(k) 



[/>(fc)P(fc)/>(fc) T + Q(fc)]- 



Distributed Nonlinear Filtering Under Packet 

Drops and Variable Delays for Robotic Visual Servoing 



y-(k + l)=p-{k + l) L x-{k + l) 


a priori quantities 

a posteriori quantities 

Available measurements 



y {k) = p-(k) ] x (B 

Available measurements 

z -mo m) 

T(k)=r(kT'w'ik)R"\kyr f (k) 

rfjtt becomes 

r(t) /(« 

with itQ-J^fflr'Ci). 

■[:(k)-y(x(k))+J,(k)x (*)] 



Fig. 3. Schematic diagram of the Extended Information Filter loop 

4.2 Extended Information Filtering for state estimates fusion 

In the Extended Information Filter each one of the local filters operates independently, 
processing its own local measurements. It is assumed that there is no sharing of measurements 
between the local filters and that the aggregation filter (Fig. 2) does not have direct access to 
the raw measurements giving input to each local filter. The outputs of the local filters are 
treated as measurements which are forwarded to the aggregation fusion filter (Lee, 2008). 
Then each local filter is expressed by its respective error covariance and estimate in terms of 
information contributions given in Eq.(48) 

p r l (k)=Pr(k) 1 + jT(k)R(k)- l J 7 (k) 
*i(k) = P ; (fc)(Pr(fc)-ifr(fc)) + /r( fc )R(k)-i[z i (k) - /(*(*)) + £(*)*-(*)] 


It is noted that the local estimates are suboptimal and also conditionally independent given 
their own measurements. The global estimate and the associated error covariance for the 
aggregate fusion filter can be rewritten in terms of the computed estimates and covariances 
from the local filters using the relations 

j! f (k)R(k)-ij 7 (k)=P l (k)-L-Pr(k) 



#(*)R(k)-V(fc) - 7 k (x(k)) +}i y (k)x-(k)] = Pi(k)- l Xi(k) - Pi(k)-^i{k-l) 

For the general case of N local filters i = 1, • ■ • , N, the distributed filtering architecture is 
described by the following equations 



X (k) = p{k)[p-{k)-H-{k)+zti(mr v m) - prw-^rw)] 


88 Robot Arms 

It is noted that again the global state update equation in the above distributed filter can be 
written in terms of the information state vector and of the information matrix 

Y(k) = Y-(k)+El 1 (Y l (k)-Yr(k)) ( > 

The local filters provide their own local estimates and repeat the cycle at step k + 1. In turn the 
global filter can predict its global estimate and repeat the cycle at the next time step k + 1 when 
the new state x (k + 1) and the new global covariance matrix P(k + 1) are calculated. From 
Eq. (59) it can be seen that if a local filter (processing station) fails, then the local covariance 
matrices and the local state estimates provided by the rest of the filters will enable an accurate 
computation of the system's state vector. 

4.3 Local estimations in terms information contributions for the derivative-free EIF 

After applying the transformation described in Section 3, the nonlinear discrete-time model 
of the dynamical system given in Eq. (15) can be substituted by a linear model of the form 
given in Eq. (1). For this linearized model, the Information Filter (IF) performs fusion of the 
local state vector estimates which are provided by the local Kalman Filters, using again the 
Information matrix and the Information state vector (Rao & Durrant-Whyte, 1991). In place of the 
Jacobian matrix L, matrix A d is used, (discretized equivalent of matrix A c , which appears in 
Eq. (18)), while in place of the Jacobian matrix Jj, matrix Q/ is used (discretized equivalent of 
matrix C c , which appears in Eq. (19)). As defined before, the Information Matrix is the inverse 
of the state vector covariance matrix, and can be also associated to the Fisher Information 
matrix (Rigatos & Zhang, 2009). The Information state vector is the product between the 
Information matrix and the local state vector estimate 

Y(k) = P-I(fc) = I(k) 

y(k)=p-(k)- 1 x(k)=Y(k)x(k) 

The update equation for the Information Matrix and the Information state vector are given by 

Y(k) = p-(fc)- 1 + C/(fc)R- 1 (fc)Q(fc) 

= Y-(k) + I(k) (4 ^ 

m = rw +c/{k)R(k)- i [ Z (k) - 7 (x(fc)) + Qi-m] 

= y-(k) + i(k) W 


I(k) = Cj(k)R(k) Cd(k) is the associated information matrix and 
i{k) = Cj(fc)R(fc) _1 [(z(fc) - C d (k)x(k)) + C d x- (Jfc)] is the information state contribution 

The predicted information state vector and Information matrix are obtained from 

ij-(k)=P-W l x-(k) 
Y-(k) = p-(k)- 1 = [A d (k)p-(k)A d (k) T + Q(k)}-i 

The derivative-free Extended Information Filter is next formulated for the case that multiple 
local sensor measurements and local estimates are used to increase the accuracy and reliability 
of the estimation. It is assumed that an observation vector z' (k) is available for N different 

Distributed Nonlinear Filtering Under Packet 

Drops and Variable Delays for Robotic Visual Servoing 89 

sensor sites i = 1, 2, ■ ■ ■ , N and each sensor observes a common state according to the local 
observation model, expressed by 

z'(Jfc) = C d (k)x(k) + v\k), i = 1,2, ■ • • ,N (49) 

where the local noise vector v' (k) ~ N(0, R' ) is assumed to be white Gaussian and uncorrelated 
between sensors. The variance of a composite observation noise vector v^ is expressed in terms 
of the block diagonal matrix 

R(jfc) = diag[R l {k),-- ■ ,R N (k)} T (50) 

The information contribution can be expressed by a linear combination of each local 
information state contribution i' and the associated information matrix P at the Z-th sensor 

iW = ZtiC'i \k)R l {k)- l [z\k) - C' d (x(k)) + C' d (k)x-(k)} 

i(fc) = v^cf (*)R'(k)-iq(*) 

Using Eq. (34) the update equations for fusing the local state estimates become 



m = r^)+i^ =1 j\\k)R\k)-\z i (k)-c i {k){x{k)) + c d {k)x-{k)\ 

Y(k) = Y~(k) + TtA T WR't*)" 1 ^*) 

It is noted that, as in the Extended Information Filter case, an aggregation (master) fusion filter 
produces a global estimate by using the local sensor information provided by each local filter. 
The local filters which constitute the Information Filter can be written in terms of time update 
and a measurement update equation. 

Measurement update: Acquire z(fc) and compute 

Y(fc) = p-(fc)- 1 + Cj(fc)R(fc)- 1 Q(fc) 
or y(jfc) = Y~(k) + I(k) where I(k) = Cj(fc)R- 1 (fc)Q(fc) 

y(k) = r (*) + Cj(fc)R(fc)-![2W - Q(f (fc)) + Qx-(fc)] 

or $(k)=-g-{k)+i(k) 


Tn«e update: Compute 

Y-(* + l) = p-(k + l) 1 = [ J 4 d (fc)P(fc)A d (fc) T + Q(fc)]- 1 (55) 

y-(Jr + l)=p-(fc + l)" 1 f-(fc + l) (56) 

4.4 Derivative-free information filtering for state estimates fusion 

The outputs of the local Kalman Filters described in subsection 4.3 are treated as 
measurements which are fed into the aggregation fusion filter (Rao & Durrant-Whyte, 1991). 
Then each local filter is expressed by its respective error covariance and estimate in terms of 
information contributions given in Eq.(48) 

PC 1 (k) = Pf(k) i + Cj(k)R(k)- 1 C d (k) 
Xi (k) = Pi{k){Pr{k)-HT(k)) + C T d {k)R{k)-\z'{k) - C d {k)x{k) + C d {k)xr{k)\ 


90 Robot Arms 

As explained in subsection 4.2, the local estimates are suboptimal and also conditionally 
independent given their own measurements. The global estimate and the associated error 
covariance for the aggregate fusion filter can be rewritten in terms of the computed estimates 
and covariances from the local filters using the relations 

Cj(fc)K(fc)" 1 Q(fc) = P,(fc)-! - Pr(k)-i 

cj(fc)R(fcrv(fc) - c d (x(k)) + c d (k)x-(k)\ = Pi(k)-Hi(k) - p.ikr^^k-i 

For the general case of N local filters i = 1, • • • , N, the distributed filtering architecture is 
described by the following equations 


-i _ 


■(kyi + Eliiwy 

1 - Pi 


- 1 ] 



- 1 * 


Xi (k) - 



x{k) = P{k)[p-(k)-'x-{k) +LfL,(P I (k)-'x 1 {k) - Pr(fc)-ifr(/ c ))] 

It is noted that, once again, the global state update equation in the above distributed filter can 
be written in terms of the information state vector and of the information matrix 

Y(k) = Y-(k)+El 1 (Y l (k)-tr(k)) (W > 

The local filters provide their own local estimates and repeat the cycle at step k + 1. In turn 
the global filter can predict its global estimate and repeat the cycle at the next time step k + 1 
when the new state x (k + 1) and the new global covariance matrix P(k + 1) are calculated. 
From Eq. (59) it can be seen again that if a local filter (processing station) fails, then the local 
covariance matrices and the local state estimates provided by the rest of the filters will enable 
an accurate computation of the system's state vector. 

5. Distributed nonlinear filtering under random delays and packet drops 

5.1 Networked Kalman Filtering for an autonomous system 

The structure of networked Kalman Filtering is shown in Fig. 4. The problem of distributed 
filtering becomes more complicated if random delays and packet drops affect the transmission 
of information between the sensors and local processing units (filters), or between the local 
filters and the master filter where the fused state estimate is computed. First, results on the 
stability of the networked linear Kalman Filter will be presented (Xia et al., 2009). The general 
state-space form of a linear autonomous time-variant dynamical system is given by 

x(k) = Ax{k-l)+w(k,k-l) (61) 

where x(fc)eR m is the system's state vector, AgR" x " is the system's state transition matrix, 
and w(k,k — 1) is the white process noise between time instants k and k — l.The sensor 
measurements are received starting at time instant k>l and are described by the measurement 

z(k) = Cx(k) + v{k) (62) 

where CeRP xm , z(fc)G-R pxl and v(k) is the white measurement noise. Measurements z(fc) are 
assumed to be transmitted over a communication channel. 

To denote the arrival or loss of a measurement to the local Kalman Filter, through the 
communication network, one can use variable ^^-£{0,1}, where 1 stands for successful 
delivery of the packet, while stands for loss of the packet. 

Distributed Nonlinear Filtering Under Packet 

Drops and Variable Delays for Robotic Visual Servoing 


Thus, in the case of packet losses, the discrete time Kalman Filter recursion that was described 
in Eq. (5) (measurement update) is modified as 

K(k) = y k P- {k)C T [CP- (k)C T + R]- 1 (63) 

where 7££{0, 1}. This modification implies that the value of the estimated state vector z(fc) 
remains unchanged if the a packet drop occurs, i.e. when y^ = 0. 

It is assumed that the system [A, C] is observable. Next, the following time sequences {tj-} 
and {fa} are defined X\ = infjfc : k > 1, y^ = 0}. Time T\ denotes the first time instant 
when the transmission over the communication channel is interrupted (loss of connection). 
On the other hand, time sequence fa is defined as fa = inf{k : k > T\,y^ = 1}. Time 
fa denotes the fc-th time instant in which the transmission over the communication channel 
is restored (reestablishment of connection). Therefore, for time sequences Tj. and fa it holds 
1 < Tj < fa < t 2 < fa < ■ ■ ■ < T/c < fa < ■ ■ ■ ■ 

Thus, 1 is the beginning of tranmission, X\ is the time instant at which the connection is lost 
for the first time, fa is the time instant at which the connection is re-established after first 
interruption, t 2 is the time instant at which the connection is lost for second time, fa is the time 
instant at which the connection is re-established after second interruption,etc. The following 
variable is also defined fa = fa — 1, where fa is the last time instant in a period of subsequent 
packet losses. Time fa is useful for analyzing the behavior of the Kalman Filter in case of a 
sequence of packet losses (deterioration of the estimation error covariance matrix). It is noted 
that in the case of the filtering procedure over the communication network, the sequence of 
covariance matrices Pa. is stable if SMpj->l£| I Pr Ic 1 1 < °° (Xi a et al., 2009). Equivalently, it can be 
stated that the networked system satisfies the condition of peak covariance stability (Xia et al, 


-» sensor 1 


Filter 1 


Filter N 


Fig. 4. Distributed filtering over sensors network with communication delays and packet 

5.2 Processing of the delayed measurements for an autonomous system 

Now, the processing of the delayed measurements for the networked linear Kalman Filter 
proceeds as follows: it is assumed that for all local filters the packet losses and time delays 


Robot Arms 

have the same statistical properties. It is also assumed that measurement z;(fc — N) should 
have arrived at the z'-th local filter at time instant k — N. Instead of this, the measurement 
arrives at time instant k + 1. The delayed measurement z,(/c — N) must be integrated in the 
estimation which has been performed by each local Kalman Filter (see Fig. 5 and Fig. 6). 

z l 

Xi, Pi 

Master Filter 

Local Filter 1 







Local Filter M 

Xm i* M 

Z M 

Fig. 5. Distributed filtering diagram implemented with the use of local filters and a master 
(aggregation) filter 

delayed measurement 


\(k) z\k-N) 


k-N k-N + 1 k fc+1 

Fig. 6. Delayed measurement over the communication channel 

This means that the estimation f,(/c|fc) and the associated state estimation error covariance 
matrix P,(/c|fc) have to be modified. The transition matrices between different time instances 
of the discrete-time system of Eq. (61) are defined 

A(k,k-f) = A(k,k - 1)- ■ ■ A(k - ) + \,k - j), jeZ^ 
Using the system's dynamic equation in transition matrix form, i.e 

x(k) =A(k,k-l)x(k-l)+w(k,k-l) 
z i (k) = C i (k)x(k)+v i (k) 

one has 



x(k) =A(k,k-N)x(k-N) + w(k,k-N) 



Distributed Nonlinear Filtering Under Packet 

Drops and Variable Delays for Robotic Visual Servoing 93 

w{k,k- N) = LjLiMKk- j + l)w(k -/ + l,fc- /) (67) 

which means that knowing the state estimation x(k— N) and the sequence of noises from time 
instant k — N to time instant k one can calculate an estimation of the state vector at time instant 
k. Denoting <JE>i(* - N,k) = A(k,k - N)" 1 and w a (k - N,k) = -A(k,k-N)- l w(k,k-N) 
then, from Eq. (66) one obtains 

x(k-N) = ®i(k-N,k)x{k)+w a (k-N,k) (68) 

To incorporate the delayed measurement z,(fc — N) which arrives at the i-th local filter at time 
instant k + 1, a state estimation is created first for instant k— N using Eq. (68), i.e. 

Xj{k-N,k) =<$ 1 (k-N,k)£ i {k\k) + fi a {k-N,k\k) (69) 

where Xj(k\k) is the state estimation of the i-th local filter at time instant k and Wj(k— N,k\k) is 
the noise sequence for the i-th local filter, at time instant k— N. For the measurement (output) 
equation one has from Eq. (65) 

Z j(k-N) =Ci(k-N)x(k-N)+Vi(k-N) (70) 

while substituting x(k — N) from Eq. (68) one gets 

Zi(k-N) = C i {k-N)® 1 {k-N,k)x(k) + C i {k-N)w ai {k-N,k) + v i (k-N) (71) 

Next, using the current state estimate x(k\k) and Eq. (71) one can find the measurement 
estimate ij(k - N\k) for the i-th local filter, i = 1, ■ ■ ■ , M: 

z,{k-N) = Cj(k - N)<S> u (k - N,k)x{k\k) + C,{k - N)w Ki {k - N,k) (72) 

Defining, Zj(k\j) = z,(fc) — z,(fc|;) (innovation), Xj(k\j) = x(k) — Xj{k\j) (state estimation error), 
and Wi(k — N,k\k) = w(k — N,k) — ibi(k — N,k) (noise estimation error) one obtains 

Zj{k-N\k) = C i (k-N)^ li (k-N,k)S i {k\k) + C i (k-N)& ai (k-N,k\k) + v i (k-N) (73) 

The innovation z, (k — N, k) at time instant k — N will be used to modify the estimation x, (k\k) 

x*{k\k) = Xi(k\k) + M,z,{k - N\k) (74) 

Thus, one can update (smooth) the state estimate at time instant k by adding to the current 
state estimate Xj(k\k) the corrective term 

M,z,(fc-N,fc) (75) 

where M; is a gain matrix to be defined in the sequel, and z,(fc — N,k) is the innovation 

between the measurement Z;(fc — N) taken at time instant k — N and the output estimate 

z,(fc — N) which has been calculated in Eq. (72). 

The main difficulty in Eq. (74) is that one has to calculate first the noise estimation error 

Wcii {k— N,k\k), which means that one has to calculate an estimate of the process noise w ai (k — 


94 Robot Arms 

The following theorem has been stated in (Xia et al., 2009), and is also applicable to the 
distributed filtering approach presented in this chapter: 

Theorem 1: It is assumed that the observation error (innovation) at the i-th information 
processing unit (local filter), at time instant k — n where n£ [0, N], is given by 

z(k-n)=Zj(k-n)-z i {k-n) (76) 

and that the covariance matrix of the white process noise w a (k — j + l,k — j) is 

Q(k- j + l,k- j) = E{w a (k- j + l,k- j)w a (k- j + l,k- jf} (77) 

while the estimation error for the noise w a j (k — N, k\ k) is 

w a ,(k - N,k\k) = w a ,(k - N,k) - ti ai (k - N,k\k) (78) 

Moreover, the covariance matrix for the error of the white estimated noise vector ib aj (k — 
N,k\k) is 

Q*(k- N,k) = E{w a ,(k- N,k\k)w a ,(k- N,k\kf} (79) 

Then, one can obtain the noise estimate w ai (k — N, k) from the relation 

w a ,(k- N,k\k) = -®i(k-N,k)^-J 
Ci{n)[Ci{k - n)Pi(k -n\k-n- l)Q(fc - n) T + R t (k - n)Y~ l Zi{k - n) 




C i (n) = {A(k,k-n)Q(k-n,k-n-l) + Zf =n+2 A(k,k-j + l)Q(k-j + l,k-j)x 
x tnL~i n+1 A(fc- m + U- m)[l - K { (k - m)Ci(k - m)]] T }C,(k - n) T 

while the covariance matrix of the estimated white noise w Xi (k — N, k) is calculated as 

Q*(k-N,k) =Q{k-N,k)-® 1 (k-N,k)x 
xE^o 1 C ! '(")[Q(fc-")^(fc-«|fc-"-l)Q(fc-«) T + -R/(fc-«)]- 1 x (82) 

xQ( M ) r d> 1 (fc-N,*:) T 


Q(k-N,k) = ® 1 (k-N,k)lZf =1 A(k / k-j + V J 

x Q(k - j + \,k - j)A(k,k- j + l) T ]<I) 1 (fc - N,k) T 


Next, a theorem is given about the calculation of covariance matrix M, appearing in the 
modified state estimation of Eq. (74). The theorem comes from (Xia et al., 2009) and is also 
applicable to the distributed filtering approach which is presented in this chapter. 

Theorem 2: It is assumed that the modified state estimation error at time instant k is 

x*(k\k)=x(k)-x i (k\k) (84) 

and that the covariance matrix of the modified state estimation error is 

Distributed Nonlinear Filtering Under Packet 

Drops and Variable Delays for Robotic Visual Servoing 95 

P*{k\k) = E{x*{k\k)x*{k\k) T ) (85) 

and that the cross-covariance between Xj(k\k) and idi(k — N,k\k) is 

Pf»(k\k) = E{xj{k\k)wj(k-N,k\k) T } (86) 

Then, the optimal filter for the processing of the delayed measurements is given by Eq. (74), 

x*(k\k) = £i(k\k)+Mi[zi(k-N) - z,{k-N\k)] (87) 


Mi = [Pi(k\k)^x(k - N,k) T + P,™]C,(fc - Nfwr 1 (88) 

In that case, the covariance matrix of the modified state estimation error becomes 

P*{k\k) = Pj{k\k) - \P™ + PiikW&tik - N,k) T ] x 

xC,(fc-N) T Wr 1 Q(fc-N)x (89) 

x [Pf @ + P,-(Jt|fc)4>i(fc - N,k) T ] T 

where matrices W, and P™ are defined as 

Wi = Q(fc-N){$i(fc-N,fc)P,(fc|)t)<Di(fc-N,fc) T + 
+4>i (k - N, k)Pf l " + [A(k- N, k)Pf & ] T + Q*(k-N,k)} (90) 

xCi(k-N)' + Ri(k-N) 

pxiu = $l ( fc _ N,k)£%-*Pi(k - N\k - N)Dj{n) T x 
x [Q(fc - n)P,(k - n \k-n- l)C,(fc - n) T + R t {k - n)}- 1 x (91) 

xQ(n) r <l> 1 (fc-N,fc) T - A(k,k-N)Q*(k-N,k) 

and matrix Dj(n) is defined as 

{Ci(k-n)A(k-n,k-n-l), UN = 1 
C,(k - n)A(k -n,k-n- l)IlfS n 2 [I - K,(k - j - l)Q(fc - ; - 1)] x (92) 

xA{k-j-l,k-j-2), ifN > 1 

5.3 Processing of the delayed measurements for a linear non-autonomous system 
5.3.1 The case of a time-variant linear system 

In the case of a linear non-autonomous system, in place of Eq. (61) one has 

x(k) = A{k,k-l)x(k-i) + B{k,k-i)u(k-l) + w{k,k-l) (93) 

Setting wi(k,k- 1) = B(k,k- l)u(k- 1) +w(k,k- 1) one obtains 

x(k) = A{k,k - l)x(k - 1) + Wl {k,k - \) (94) 

and consequently it holds 

x(k)=nf =1 A(k-j + l,k-j)x(k-N) + 
+L%Z{njUA(k -j+l,k- ;>! (k - m, k-m-l) + Wl (k, fc - 1) 


96 Robot Arms 


W\(k — m + \,k — m) = B(k — m + \,k — m)u(k — m) + w(k — m + l,k — m) (96) 
Thus, one can obtain a more compact form 

x(k) = <$>{k,k - N)x{k - N) + io x (k,k - N) (97) 



®(k,k-N) =Uf=iM k -j + 1, k-j), and 
wi(k,k- N) = L^zlTVjUMk -j + l,k- ;>i(fc -m,k- m - 1) + w x {k,k- 1) 

5.3.2 The case of a time-invariant linear system 

For a linear time-invariant non-autonomous system 

x(k) = Ax(k - 1) + Bu{k - 1) + w(k - 1) (99) 

it holds 

N N 

x(k) = A N x(k -N) + J^A N -)Bu(k - N + j - 1) + J^A N -'zv(k - N + j - 1) (100) 

;=1 j=l 

Denoting A N = <S>(k,k - N) one has 

N N 

x(k) = <$>{k,k-N)x(k-N) + Y^A N ~i Bu(k - N + j - 1) + J^A N ->iv(k - N + j - 1) (101) 

N N 

Wi(k,k-N) = Y^A N -'Bu{k-N + i-l) + , }2A N -hu{k-N + i-l) (102) 

7=1 7=1 

one has that Eq. (101) can be written in a more compact form as 

x(k) = ®(k,k - N)x{k - N) + wi(k,k - N) (103) 

Using that matrix <t>(fc, k — N) is invertible, one has 

x(k - N) = <t>(/c,fc - N)" 1 x(fc) - 4>(fc, k - N^w^k, k - N) (104) 

The following notation is used ®i(k — N,k) = <£>(fc,fc — N) while for the retrodiction of 
Wi{k,k- N) it holds zv a (k-N,k\k) = -0(fc,fc- ~N)~ l Wi(k,k- N). Then, to smooth the state 
estimation at time instant k — N, using the measurement of output z,(fc — N) received at time 
instant fc + 1 one has the state equation 

x{k-N,k) =<$ 1 (k-N,k)£(k\k)+ib a (k-N,k\k) (105) 

while the associated measurement equation becomes 

Distributed Nonlinear Filtering Under Packet 

Drops and Variable Delays for Robotic Visual Servoing 


z(Jfc-N) = Cx(k-N) 
Substituting Eq. (104) into Eq. (106) provides 


z(k-N) = C®i{k-N,k)x(k) + Cw ll (k-N,k)+v(k-N) 
and the associated estimated-output at time instant k — N is 



z(k-N) = C<&!(fc - N,k)x{k\k) + Cw a (k - N,k) (108) 

From Eq. (108) and Eq. (107) the innovation for the delayed measurement can be obtained 

z(k-N)=z(k-N)-z{k-N) (109) 

i.e. z(k - N) = C4>i(fc - N)x(k\k) + Cw a {k - N), where x(k\j) = x(jfc) - x(k\j) is the state 
estimation error and iv a {k — N,k\k) = w a {k — N,k) — w a {k — N,k) is the estimation error for 
w x - With this innovation the estimation of the state vector x(k\k) at time instant k is corrected. 
The correction (smoothing) relation is 

x*(k\k)=x{k\k)+Mz{k-N,k) (110) 

Therefore, again the basic problem for the implementation of the smoothing relation provided 
byEq. (110) is the calculation of the term w a (k— N,k) i.e. io a (k- N) = <E>(fc,fc— N)~ 1 Wi(k,k- 
N) . This in turn requires the estimation of the term W\ (k— N,k) which, according to Eq. (80), 
is provided by 


ti 1 (k-N,k) = -<!> 1 (k-N,k)T£-J- 
■C(n)[C(k-n)P(k-n\k-n - l)C(fc- n)} T + R{k- n)]- l z{k- n) 

where z(k — n) = z(k — n) — z(k — ri) is the innovation for time-instant k— n, while, as given 
in Eq. (81) 

C(n) = {A(k,k-n)Q(k-n,k 

x[r£i, + i*(*- 

1) + Lf =n+2 Mk,k- j + l)Q(k- j + l,k- j)x 

+ l,k-m)[I-K i (k-m)C(k-m)] T ]}C{k-ny 


5.4 Derivative-free Extended Information Filtering under time-delays and packet drops 

It has been shown that using a suitable transform (diffeomorphism), the nonlinear system of 
Eq. (15) can be transformed into the system of Eq. (17). Moreover, it has been shown that for 
the systems of Eq. (23) and Eq. (24) one can obtain a a state-space equation of the form 

/0 1 ■ 
001 • 






V in J 

000 ■ 
^000 ■ 

■ 1 


V U ) 






where v(t) = f(x, t) + g(x, t)u(t), with u(t) being the control input of the dynamical system. 
The description of the initial system of Eq. (17) in the form of Eq. (113) and Eq. (114) enables 

98 Robot Arms 

the application of the previous analysis for the compensation of time-delays and packet-drops 
through smoothing in the computation of the linear Kalman Filter. The fact that the system of 
Eq. (113) and Eq. (114) is a time invariant one, facilitates the computation of the smoothing 
Kalman Filter given in Eq. (100) to Eq. (112). Thus, one has to use the time invariant matrices 
A c and C c defined in Eq. (18) and Eq. (19), while for matrix B c it holds according to Eq. (25) 
that B c = [0, 0, ■ ■ ■ ,0,1]. The discrete-time equivalents of matrices A c , B c and C c are noted 
as A&, 6^ and C^, respectively. It is also noted that due to the specific form of matrix B c , the 
term Bu(k — 1) appearing in Eq. (99) is a variable of small magnitude with mean value close 
to zero. Thus the term w\ (k, k — 1) = Bu(k— 1) + io(k,k— 1) differs little from w (k, k — 1). 
It also becomes apparent that through the description of the initial system of Eq. (17) in the 
form of Eq. (113) and Eq. (114), the application of the derivative-free Extended Information 
Filter can be performed in a manner that enables the compensation of time-delays and packet 
drops. Writing the controlled system in the form of Eq. (113) and Eq. (114) permits to develop 
local linear Kalman Filters that smooth the effects of delayed sensor measurement or the loss 
of measurement packets. Moreover, the application of the standard Information Filter for 
fusing the estimates provided by the local Kalman Filters, permits to avoid the approximation 
errors met in the Extended Information Filter algorithm. 

6. Distributed filtering under time-delays and packet drops for sensorless control 

6.1 Visual servoing over a network of synchronized cameras 

Visual servoing over a network of synchronized cameras is an example where the efficiency of 
the proposed distributed filtering approach under time delays and packet drops can be seen. 
Applications of vision-based robotic systems are rapidly expanding due to the increase in 
computer processing power and low prices of cameras, image grabbers, CPUs and computer 
memory. In order to satisfy strict accuracy constraints imposed by demanding manufacturing 
specifications, visual servoing systems must be fault tolerant. This means that despite failures 
in its components or the presence of disturbances, the system must continue to provide valid 
control outputs which will allow the robot to complete its assigned tasks (DeSouza & Kak, 
2004),(Feng & Zeng, 2010),(Hwang & Shih, 2002),(Malis et al., 2000). 

The example to be presented describes the control of a planar robot with the use of a 
position-based visual servo that comprises multiple fixed cameras. The chapter's approach 
relies on neither position nor velocity sensors, and directly sets the motor control current 
using only visual feedback. Direct visual servoing is implemented using a distributed filtering 
scheme which permits to fuse the estimates of the robot's state vector computed by local filters, 
each one associated to a camera in the cameras network (see Fig. 7). The cameras' network can 
be based on multiple RS-170 cameras connected to a computer with a frame grabber to form 
a vision node. Each vision node consists of the camera, the frame grabber and the filter which 
estimates motion characteristics of the monitored robot joint. The vision nodes are connected 
in a network to form a distributed vision system controlled by a master computer. The master 
computer is in turn connected to a planar 1-DOF robot joint and uses the vision feedback to 
perform direct visual servoing (see Fig. 7). 

The master computer communicates video synchronization information over the network to 
each vision node. Typical sources of measurement noise include charge-coupled device (CCD) 
noise, analog-to-digital (A/D) noise and finite word-length effects. Under ideal conditions, 
the effective noise variance from these sources should remain relatively constant. Occlusions 
can be also considered as a noise source. Finally, communication delays and packet drops 
in the transmission of measurements from the vision sensors to the information processing 

Distributed Nonlinear Filtering Under Packet 

Drops and Variable Delays for Robotic Visual Servoing 




J n. 



Computer #1 




motor ^l[ 





Computer #2 

Power amplifier 








100 Mbit Network 

Fig. 7. Distributed cameras network and distributed information processing units for visual 

nodes induce additional disturbances which should be compensated by the virtual servoing 
control loop. 

6.2 Distributed filtering-based fusion of the robot's state estimates 

Fusion of the local state estimates which are provided by filters running on the vision 
nodes can improve the accuracy and robustness of the performed state estimation, thus also 
improving the performance of the robot's control loop (Sun et al., 2011), (Sun & Deng, 2005). 
Under the assumption of Gaussian noise, a possible approach for fusing the state estimates 
from the distributed local filters is the derivative-free Extended Information Filter (DEIF). As 
explained in Section 4, the derivative-free Extended Information Filter provides an aggregate 
state estimate by weighting the state vectors produced by local Kalman Filters with the inverse 
of the associated estimation error covariance matrices. 

Visual servoing over the previously described cameras network is considered for the nonlinear 
dynamic model of a single-link robotic manipulator. The robot can be programmed to execute 
a manufacturing task, such as disassembly or welding (Tzafestas et al., 1997). The position of 
the robot's end effector in the cartesian space (and consequently the angle for the robotic link) 
is measured by the aforementioned m distributed cameras. The proposed multi-camera based 
robotic control loop can be also useful in other vision-based industrial robotic applications 
where the vision is occluded or heavily disturbed by noise sources, e.g. cutting. In such 
applications there is need to fuse measurements from multiple cameras so as to obtain 
redundancy in the visual information and permit the robot to complete safely and within the 
specified accuracy constraints its assigned tasks (Moon et al, 2006),(Yoshimoto et al., 2010). 
The considered 1-DOF robotic model consists of a rigid link which is rotated by a DC motor, 
as shown in Fig. 8. The model of the DC motor is described by the set of equations: LI = 
— k e co — RI + V, Jcb = k e l — kfiOJ — Tj, with the following notations L : armature inductance, 
7 : armature current, k e : motor electrical constant, R : armature resistance, V : input voltage, 


Robot Arms 

Fig. 8. Visual servoing based on fusion of state estimates provided by local derivative-free 
nonlinear Kalman Filters 

taken as control input, / : motor inertia, to : rotor rotation speed, kj : mechanical dumping 
constant, Tj : disturbance or external load torque. It is assumed that T^ = mgl-sin(9), i.e. 
that the DC motor rotates a rigid robotic link of length / with a mass m attached to its end. 
Then, denoting the state vector as [x\, x 2 , x 3 ] T = [9, 9, 9] T , a nonlinear model of the DC motor 
is obtained 

x = f{x,t)+g{x,t)u (115) 

where f(x, t) = [f\(x, t),f 2 (x, t),f 3 (x, t)] T is a vector field function with elements: f\{x, t) = 
x 2 ,f 2 (x,t) = x 3 ,f 3 (x,t) = -S+^x 2 - ^±x 3 - ^sin( Xl ) - 'lfcos( Xl )x 2 . Similarly, 
for function g(x, t) it holds that g(x, t) = [g\ (x, i),g 2 {x, i),g 3 (x, t)] T , i.e. it is a vector field 
function with elements: g\{x,t) = 0, g 2 (x,t) = 0, g 3 (x,t) = -r^. Having chosen the joint's 
angle to be the system's output, the state space equation of the 1-DOF robot manipulator can 
be rewritten as 

x W=f(x)+g(x)u (116) 

where functions f(x) and g{x) are given by f(x) = — '- , ^' x 2 1"^ '' x 3 '^-sin(xi) — 

'^f-cos(xi)x 2 , and g(x) = -A-. This is a system in the form of Eq. (23), therefore a 

state estimator can be designed according to the previous results on derivative-free Kalman 


The controller has to make the system's output (angle 9 of the motor) follow a given reference 

signal Xj. For measurable state vector x and uncertain functions f(x, t) and g(x, t) an 

appropriate control law for the 1-DOF robotic model is 


[xy-f(x,t)-K T e + u c ] 


Distributed Nonlinear Filtering Under Packet 

Drops and Variable Delays for Robotic Visual Servoing 


withe = x — xj, e T = [e,e,e,--- ,e'"~ 1 )] r , K T = \k n ,k n -\,- ■ ■ ,k{\, such that the polynomial 
e'"' + kie' n ~ ) + J^e'" ' + • • • + k n e is Hurwitz. The previously defined control law results 
into e("> = —K T e + u c + d, where the supervisory control term u c aims at the compensation of 
modeling errors as well as of the additive disturbance d (Rigatos & Tzafestas, 2007). Suitable 
selection of the feedback gain K assures that the tracking error will converge to limf_ >00 e(f ) = 
0. In case of state estimation-based (sensorless control), and denoting, x as the estimated state 
vector and e = x — x^ as the estimated tracking error one has 


{ " ] -f{x,t)-K T e+u c 


7. Simulation tests 

The fusion of the distributed state estimates for the robotic model was performed with the use 
of the derivative-free Extended Information Filter. First, it was assumed that the transmission 
of measurements from the vision sensors (cameras) to the local information processing 
units, where the state estimators (filters) were running, was not affected by time delays or 
packet drops. At the local vision nodes, Kalman filters were used to produce estimations 
of the robot's state vector as well as the associated covariance matrices, after carrying out a 
linearization of the robot's nonlinear dynamic model through the transformation described in 
subsection 3.2 and processing the local xy position measurements. This standard Information 
Filter provided the overall estimate of the robot's state vector, through weighting of the local 
state vectors by the local covariance matrices. The obtained results are depicted in Fig. 
9(a) and Fig. 9(b) in case of a sinusoidal and a see-saw reference trajectory (both reference 
trajectories are denoted with the red line). 


I 1 




3 o 


DEIF master station 


DEIF master station 




3 -1 

5 10 15 20 
t (sec; 
DEIF master station 

5 10 15 20 
t (sec) 
DEIF master si 

10 15 20 

i (sec) 



Fig. 9. Control of the robotic manipulator with fusion of position measurements from 
distributed cameras through the use of the derivative-free Extended Information Filter (a) 
when tracking of a sinusoidal trajectory (b) when tracking of a see-saw trajectory 


Robot Arms 

Next, time-delays were assumed in the transmission of image frames from the distributed 
cameras to the associated local vision nodes, where the local derivative-free Kalman Filters 
were running. For both vision nodes the delays in the transmission of measurements varied 
randomly between 6 and 25 sampling periods. Longer delays could be also handled by the 
proposed distributed filtering algorithm. The variation of measurement transmission delays 
with respect to time, is depicted in Fig. 10. 

DEIF local station 1 

if 10 

I 2 









t (sec) 

DEIF local station 2 

Fig. 10. Variation in time (in multiples of the sampling period) of the measurement delays 
appearing at the local information processing nodes 1 and 2. 

The delayed measurements were processed by the Kalman Filter recursion according to the 
stages explained in subsection 5.3. The smoothing of the delayed measurements that was 
performed by the Kalman Filter was based on Eq. (74), i.e. x*(k\k) = x(k\k) + My(k— N,k). 
As explained in subsection 5.3, matrix M is a gain matrix calculated according to Eq. (88). 
The innovation is given by z(k — N) = z(k — N) — z(k — N). The tracking accuracy of the 
distributed filtering-based control loop is depicted in Fig. 11 to Fig. 13. 

Additionally, some performance metrics were used to evaluate the distributed filtering-based 
control scheme. Table I, shows the variation of the traces of the covariance matrices at the 
local filters and at the master filter with respect to delay levels [d\,d.2 = k-T s i.e. multiples of 
the sampling period T s ), as well as with respect to the probability of delay occurrence in the 
transmission of the measurement packets (pG [0, 1]). 

Moreover, the variation of the tracking error of the three state variables Xj, i = 1, ■ ■ ■ , 3 with 
respect to delay levels as well as with respect to the probability of delay occurrence in the 
transmission of the measurement packets is given in Tables II to IV. 

Distributed Nonlinear Filtering Under Packet 

Drops and Variable Delays for Robotic Visual Servoing 


DEIF local station 1 

DEIF local station 1 

DEIF local station 1 

5 10 15 


5 10 15 


5 10 15 


5 10 15 


t (sec) 

t (sec) 


t (sec) 

DEIF local station 1 

DEIF local station 1 

DEIF local station 1 

DEIF local station 1 

10 15 20 



Fig. 11. Estimation of the motion of the robotic manipulator under transmission delays at the 
first local measurement processing node, (a) when tracking a sinusoidal trajectory (b) when 
tracking a see-saw trajectory 

DEIF local station 1 

DEIF local station 1 

DEIF local station 1 

5 10 15 20 5 10 15 20 

t (sec) t (sec) 

DEIF local station 1 DEIF local station 1 

— : : : 1 200 

5 10 15 20 

DEIF local station 1 

DEIF local station 1 



A A 


\l \I 


V ......$. 

5 10 15 20 

t (sec) 
DEIF local station 1 

-i 2 
5 ° 

i -0 



JS -4 


/\L A L 


2 100 

8 -ioo 





t (sec) 


10 15 20 


10 15 20 




Fig. 12. Estimation of the motion of the robotic manipulator under transmission delays at the 
second local measurement processing node, (a) when tracking a sinusoidal trajectory (b) 
when tracking a see-saw trajectory 

It can be noticed that the smoothing performed by the distributed filtering algorithm, 
through the incorporation of out-of-sequence-measurements, enhances the robustness of the 


Robot Arms 

DEIF master station 

5 10 15 


5 10 

t (sec) 

t (sec) 

DEIF master station 

DEIF master 

DEIF master station 

5 10 15 


5 10 15 



t (sec) 

DEIF master station 

DEIF master station 

10 15 20 



Fig. 13. Control of the robotic manipulator under measurement transmission delays and 
using the derivative-free Extended Information Filter for state estimation, (a) tracking of a 
sinusoidal trajectory (b) tracking of a see-saw trajectory 

Table I: Traces of the covariance error matrices for various 

delay levels 


d 2 






2.780- 10" 2 

2.780- 10- 2 

6.720- 10 - - 3 




2.782- 10" 2 

2.777- 10" 2 

6.730- 10~ J 




2.782- 10" 2 

2.783- 10" 2 

6.730-10~ 3 




2.782- 10" 2 

2.776- 10" 2 

6.730- 10- J 




2.782- 10" 2 

2.775- 10" 2 

6.730-10~ 3 




2.780- 10" 2 

2.783- 10" 2 

6.730-10~ 3 

Table II: RMSE tracking error at the 1st local filter for various 

delay levels 


d 2 


x x - A 


x 3 - x$ 


4.419-10- 3 

5.490- 10- 3 

1.125- 10" 2 




4.413-10" 3 

5.504-10~ 3 

1.129-10 -2 




4.392-10" 3 

5.437- 10" 3 

1.121-10 -2 




4.402- 10" 3 

5.465- W- 3 

1.117- lO -2 




4.474- 10~ 3 

5.707- 10" 3 

1.151-10" 2 




4.433- 10- J 

5.655- 10- J 

1.144- lO -2 

estimation. Despite the raise of the delay levels in the transmission of measurements from 
the sensors (cameras) to the local information processing nodes (local derivative-free Kalman 
Filters) only slight variations of the tracking errors for state variables X{, i = 1, • • • , 3 were 
observed. Similarly, the changes of the traces of the estimation error covariance matrices, both 
at the local filters and at the master filter, were small. 

Distributed Nonlinear Filtering Under Packet 

Drops and Variable Delays for Robotic Visual Servoing 


Table III: RMSE tracking error at the 2nd local filter for various delay levels 


d 2 



x 2 -x% 

x 3 -x« 


4.390-10" 3 

5.453- 10- 3 

1.116- 10 -2 




4.380-10- J 

5.468-10- J 

1.114- 10 -2 





5.495- 10" 3 

1.118-10" 2 




4.451- 10- J 

5.521-10" J 

1.125-10" 2 




4.508-10- J 

5.744- lO" 3 

1.16M0 -2 




4.432- lO" 3 

5.755- 10- J 

1.150-10~ 2 

Table IV: RMSE tracking error at the master filter for various delay levels 


d 2 


xi -A 

x 2 -A 

x 3 -x« 


4.416-10" 3 

5.452- 10~ 3 

1.101-10 -2 




4.504-10~ 3 

5.505-10~ 3 

1.130-10~ 2 




4.473- 10- J 

5.493- 10- J 

1.106-10" 2 




4.408- 10~ 3 

5.423- 10~ 3 

1.094-10" 2 




4.533-10~ 3 

5.785- 10~ 3 

1.139- 10~ 2 




4.529- 10" J 

5.755- 10~ 3 

1.149-10" 2 

8. Conclusions 

This chapter has proposed a solution to the problem of state estimation-based control under 
communication delays and packet drops. The considered approach was within the frame 
of distributed Kalman Filtering. First, the Extended Information Filter was presented as 
a basic approach to nonlinear distributed filtering. The Extended Information Filter (EIF) 
performs fusion of the the state estimates provided by the local monitoring stations, under 
the assumption of Gaussian noises. The Extended Information Filter is a generalization of 
the Information Filter in which the local filters do not exchange raw measurements but send 
to an aggregation filter their local information matrices (local inverse covariance matrices or 
differently known as Fisher Information Matrices) and their associated local information state 
vectors (products of the local information matrices with the local state vectors). 
To improve the estimation accuracy and convergence properties of the Extended Information 
Filter, the derivative-free Extended Information Filter has been introduced. The 
derivative-free Extended Information Filter, has the following features (i) it is not based on 
local linearization of the controlled system dynamics, (ii) it does not assume truncation of 
higher order Taylor expansion terms, (iii) it does not require the computation of Jacobian 
matrices. In the proposed filtering method, the system is first subject to a linearization 
transformation and next state estimation is performed by applying local Kalman Filters to 
the linearized model. The class of systems to which the derivative-free Extended Information 
Filter can be applied has been also defined. 

Next, distributed state-estimation under communication delays and packet drops was 
examined. First, results on networked linear Kalman Filtering were overviewed. These results 
were generalized in the case of the derivative-free Extended Information Filter, where the 
problem of communication delays and packet drops has again the following forms: (i) there 
are time delays and packet drops in the transmission of information between the distributed 
local filters and the master filter, (ii) there are time delays and packet drops in the transmission 
of information from distributed sensors to each one of the local filters. In the first case, the 
structure and calculations of the master filter for estimating the aggregate state vector remain 
unchanged. In the second case, the effect of the random delays and packets drops has to be 

1 06 Robot Arms 

taken into account in the redesign of the local Kalman Filters, which implies (i) a modified 
Riccati equation for the computation of the covariance matrix of the state vector estimation 
error, (ii) the use of a correction term in the update of the state vector's estimate so as to 
compensate for delayed measurements arriving at the local Kalman Filters. 
In the simulation experiments it was shown that the aggregate state vector produced by the 
derivative-free Extended Information Filter can be used for sensorless control and robotic 
visual servoing. Visual servoing over a cameras network was considered for the nonlinear 
dynamic model of a planar single-link robotic manipulator. The position of the robot's end 
effector in the cartesian space (and equivalently the angle of the robotic link) was measured 
through m cameras. In turn m distributed derivative-free Kalman Filters were used to estimate 
the state vector of the robotic link. Next, the local state estimates were fused with the use of the 
standard Information Filter. Finally, the aggregate estimation of the state vector was used in a 
control loop which enabled the robotic link to perform trajectory tracking. It was shown that 
the proposed redesign of the local derivative-free Kalman filters enabled to compensate for 
communication delays and packet drops, thus also improving the accuracy of the presented 
distributed filtering approach and the robustness of the associated control loop. 

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Cartesian Controllers for Tracking of Robot 
Manipulators under Parametric Uncertainties 

R. Garcia-Rodriguez and P. Zegers 

College of Engineering and Applied Sciences, Universidad de los Andes 


1. Introduction 

The relevance of robot manipulators in different processes has created the need to design 
efficient controllers with low computational costs. Although several applications for this 
problem are defined in operational coordinates, a wide variety of controllers reported in the 
literature are defined in joint coordinates. Then, for a joint robot control the desired joint 
references are computed from desired Cartesian coordinates using inverse mappings and its 
derivatives up to second order. However, computing the inverse kinematics mappings is 
difficult due to the ill-posed nature of these mappings. 

To circumvent the computation of inverse kinematics, a very old but not less important 
approach coined as Cartesian control can be used. Cartesian control deals with the problem 
of designing controllers in terms of desired Cartesian or operational coordinates. This allows 
saving a significant amount of time in real time applications due to the inherent simplification. 

1.1 Cartesian control 

Based on the seminal work of Miyazaki and Masutani [Miyazaki & Masutani (1990)] have 
been presented several approaches for regulating tasks, working with the assumption that the 
Jacobian is uncertain. Several approaches for setpoint control are presented [Yazarel & Cheah 
(2001)], [ (1999)], [Chea (2001)] [Huang (2002)], [Chea (2004)], 
assuming that the jacobian matrix can be parameterized linearly. Now, if we are interested that 
having the end effector of the robot manipulator follow a desired trajectory, Cartesian robot 
dynamics knowledge is required. However, Cartesian robot dynamics demands even more 
computational power than computing the inverse kinematics. Therefore, non-model based 
control strategies which guarantee convergence of the Cartesian tracking errors is desirable. 
In addition, Cartesian controllers should be robust and efficient with very low computational 

To differentiate this work from other approaches for tracking tasks [Chea (2006)], 
[Chea (2006)], [Moosavian & Papadopoulus (2007)], [Zhao (2007)] in this chapter it 
is assumed that the initial condition and desired trajectories belong to the Cartesian workspace 
Q, which defines the hyperspace free of singular configurations, an standard assumption for 
joint robot control. However, this assumption is not evident for others Cartesian controllers 
[Huang (2002)], [ (2001)]. This assumption allows us to use a well posed 
inverse Jacobian for any initial condition. In addition, it is possible to prove that exponential 

110 Robot Arms 

stability is guaranteed despite the fact the Jacobian is not exactly known and the Jacobian 
adaptive law is avoided. 

Brief introduction to sliding mode control 

The name variable structure control (sliding mode control) comes from the fact that the control 
signal is provided by one of two controllers. Which one? It depends on the sign of a scalar 
switching function S that in turn depends on the states of the system. If the outcome of this 
function is positive, one controller is used. If not, the other one. It is clear that the selection of 
the switching function is crucial for the control and that it allows to the designer to generate a 
rich family of behaviors. 

If this switching function is designed such that the state velocity vectors in the vicinity of the 
switching surface (the geometric locus of the states that comply with S = 0) points to the 
surface, then it is said that a sliding surface exists. Why this name? Because once the system 
intercepts such a surface it continues sliding within it until an equilibrium point is reached. 
Therefore, sliding mode control needs to comply with two conditions 

• The control law has to provide with sufficient conditions to guarantee the existence and 
the reachability of the sliding surface. 

• Once the state space behavior of the system is restricted to the sliding surface, the dynamics 
corresponds to the desired one, i.e. stability or tracking. 

The properties of sliding mode control ensure that a properly controlled system will reach the 
sliding surface in a finite time f;, < oo, beyond which the states of the system are ketp within 
the sliding surface and displaying the desired dynamics. 

All the considerations given above rest on assuming ideal sliding modes. This implies having 
the capability of producing infinitely fast switchings, something of course impossible in the 
physical world. Therefore, the states of the system oscillate within a neighborhood of the 
sliding surface. This effect translates into a chattering signal [Utkin (1977)], [DeCarlo 
(1988)], [Hung (1993)] that looks like noise. 


In this chapter, free-chattering second order sliding mode control is presented in order to 
guarantee convergence of the tracking errors of the robot manipulator under parametric 
uncertainty. Specifically, a Cartesian second order sliding mode surface is proposed, which 
drives the sliding PID input. Therefore, the closed loop system renders a sliding mode for all 
time, whose solution converges to the sliding surface in finite time and a perfect tracking is 
guaranteed under assumption that the Jacobian is uncertain. 
The main characteristics of the proposed scheme can be summarized as follows: 

• The regressor is not required. 

• Very fast tracking is guaranteed. 

• The controller is smooth. 

• An exact Jacobian is not required. 

• A conservative tuning of feedback gains is required. 

The chapter is organized as follows: Section II presents the dynamical model of a rigid n-link 
serial non-redundant robot manipulator and some useful properties. Section III presents 
a parameterization of the system in terms of the Cartesian coordinates. Furthermore, two 

Cartesian Controllers for Tracking of Robot Manipulators under Parametric Uncertainties 111 

Cartesian controllers are presented assuming parametric uncertainty. In the first case, a 
traditional Cartesian controller based on the inverse Jacobian is presented. Now, assuming 
that the Jacobian is uncertain a Cartesian controller is proposed as a second case. In 
Section IV, numerical simulations using the proposed approaches are provided. Finally, some 
conclusions are presented in section V. 

2. Dynamical equations of robot manipulator 

The dynamical model of a non-redundant rigid serial n-link robot manipulator with all 
revolute joints is described as follows 

H(q)q+^H(q)+S(q,q))q + g(q)=u (1) 

where q,q£ 5R" are the joint position and velocity vectors, H(q) € K"*" denotes a symmetric 
positive definite inertial matrix, the second term in the left side represent the Coriolis and 
centripetal forces, g(q) G 5R" models the gravitational forces, and u G SR" stands for the 
torque input. 
Some important properties of robot dynamics that will be used in this chapter are: 

Property 1. Matrix H(q) is symmetric and positive definite, and both H(q) and H l (q) are uniformly 
bounded as a function of q G K" [Arimoto (1996)]. 

Property 2. Matrix S(q, q) is skew symmetric and hence satisface [Arimoto (1996)]: 

q T S(q,q)q = \/q,qe$t" 

Property 3. The left-hand side of (1) can be parameterized linearly [Slotine & Li (1987)], that is, a 
linear combination in terms of suitable selected set of robot and load parameters, i.e. 

Y0 = H(q)q + (hl{q) + S(q,q)\ q + g(q) 

where Y = Y(q, q, q, q) G W lxp is known as the regressor and © £ W is a vector constant parameters 
of the robot manipulator. 

2.1 Open loop error equation 

In order to obtain a useful representation of the dynamical equation of the robot manipulator 
for control proposes, equation (1) is represented in terms of the nominal reference (q r ,q r ) G 
5R 2 " as follows, [Lewis (1994)]: 

H(q)q,. + Qft(q) + S(q,q) j q r + g(q) = Y,.0 r (2) 

where the regressor Y r = Y r (q, q, q r , q r ) G $t nx P and & r G 5R p . 

If we add and subtract equation (2) into (1) we obtain the open loop error equation 

H(q)S, + Qfi(q) + S(q, q) j S, = u Y r © (3) 

where the joint error manifold S r is defined as 

Sr = q q r (4) 

112 Robot Arms 

The robot dynamical equation (3) is very useful to design controllers for several control 

techniques which are based on errors with respect to the nominal reference [Brogliato 

(1991)], [Ge & Hang (1998)], [Liu (2006)]. 

Specially, we are interesting in to design controllers for tracking tasks without resorting on 

H(q), S(q, q),g(q). Also, to avoid the ill-posed inverse kinematics in the robot manipulator, 

a desired Cartesian coordinate system will be used rather than desired joint coordinates 

(qJ,qJ) r GK 3 ". 

In the next section we design a convenient open loop error dynamics system based on 

Cartesian errors. 

3. Cartesian controllers 

3.1 Cartesian error manifolds 

Let the forward kinematics be a mapping between joint space and task space (in this case 
Cartesian coordinates) given by 1 

X = f(q) (5) 

where X is the end-effector position vector with respect to a fixed reference inertial frame, and 
f (q) : 5R" — > W is generally non-linear transformation. Taking the time derivative of the 
equation (5), it is possible to define a differential kinematics which establishes a mapping at 
level velocity between joint space and task space, that is 

q = J" 1 (q)X (6) 

where J (q) stands for the inverse Jacobian of J(q) £ 5R" X ". 

Given that the joint error manifold S,- is defined at level velocities, equation (6) can be used to 

defined the nominal reference as 

4 r = J- 1 (q)* f (7) 

where X r represents the Cartesian nominal reference which will be designed by the user. Thus, 
a system parameterization in terms of Cartesian coordinates can be obtained by the equation 
(7). However an exact knowledge on the inverse Jacobian is required. 
Substituting equations (6) and (7) in (4), the joint error manifold S r becomes 

S, =r a (q)(X-X r ) 

= T 1 (q)Sx (8) 

where S x is called as Cartesian error manifold. That is, the joint error manifold is driven by 

Cartesian errors through Cartesian error manifold. 

Now two Cartesian controllers are presented, in order to solve the parametric uncertainty. 

Case No.l 

Given that the parameters of robot manipulator are changing constantly when it executes a 

task, or that they are sometimes unknown, then a robust adaptive Cartesian controller can be 

designed to compensate the uncertainty as follows [Slotine & Li (1987)] 

u=-K rfl S rl +Y r (9) 

& = -T\JS rl (10) 

1 In this paper we consider that the robot manipulator is non-redundant, thus m ■■ 

Cartesian Controllers for Tracking of Robot Manipulators under Parametric Uncertainties 113 

where K dl = Kj x > 6 U nxn , T = T T > G W K P. 

Substituting equation (9) into (3), we obtain the following closed loop error equation 

H(q)S r i + Qft(q) + S(q, q) J S rl = -K dl S rl + Y r A0 

where A© = © — ©. If the nominal reference is defined as X r j = x d — ajAxj where 0i\ is a 
positive-definite diagonal matrix, Axj = Xj — x d and subscript d denotes desired trajectories, 
the following result can be obtained. 

Assumption 1. The desired Cartesian references x d are assumed to be bounded and uniformly 
continuous, and its derivatives up to second order are bounded and uniformly continuous. 

Theorem 1. [Asymptotic Stability] Assuming that the initial conditions and the desired trajectories 
are defined in a singularities-free space. The closed loop error dynamics used in equations (9), (10) 
guarantees that Ax\ and Ax\ tends to zero asymptotically. 

Proof. Consider the Lyapunov function 

y=is, T 1 H(q)s rl + lA© r r- 1 A© 

Differentiating V with respect to time, we get 

V = -S rl K dl S rl < 

Since V < 0, we can state that V is also bounded. Therefore, S r i and A© are bounded. 
This implies that © and J~ (q)S 1 -i are bounded if J (q) is well posed for all t. From the 
definition of S x \ we have that Axj, and Axj are also bounded. Since Axj, Axj, A©, and S r i are 
bounded, we have that S r \ is bounded. This shows that V is bounded. Hence, V is uniformly 
continuous. Using the Barbalat's lemma [Slotine & Li (1987)], we have that / — > at t — > oo. 
This implies that Axj and Axj tend to zero as t tends to infinity. Then, tracking errors Axj and 
Axj are asymptotically stable [Lewis (1994)]. □ 

The properties of this controller can be numbered as: 

a) On-line computing regressor and the exact knowledge of J (q) are required. 

b) Asymptotic stability is guaranteed assuming that J (q) is well posed for all time. 
Therefore, the stability domain is very small because q(f ) may exhibit a transient response 
such that J(q) losses rank. 

In order to avoid the dependence on the inverse Jacobian, in the next case it is assumed that 

the Jacobian is uncertain. At the same time, the drawbacks presented in the Case No.l are 


Case No.2 Considering that the Jacobian is uncertain, i.e. the Jacobian is not exactly known, 

the nominal reference proposed in equation (7) is now defined as 

4,=r X (q)** (11) 

114 Robot Arms 

where J (q) stands as an estimates of J (q) such that rank(] (q) ) = n for all t and for all 
q 6 Q where Q = {q|r«nfc(J(q)) = n}. Therefore, a new joint error manifold arises coined as 
uncertain Cartesian error manifold is defined as follows 

S,2 = q - q, 

= J- 1 (q)X-r 1 (q)X r2 (12) 

In order to guarantee that the Cartesian trajectories remain on the manifold S x although the 
Jacobian is uncertain, a second order sliding mode is proposed by means of tailoring X r2 . 
That is, a switching surface over the Cartesian manifold S x should be invariant to changes in 
J (q). Hence, high feedback gains can to ensure the boundedness of all closed loop signals 
and the exponential convergence is guaranteed despite Jacobian uncertainty. 
Let the new nominal reference X r2 be defined as 

X r2 =x d - a 2 Ax 2 + S d - -y p tr (13) 

& =sgn(S e ) 

where a 2 is a positive-definite diagonal matrix, Ax 2 = x 2 — x d , x d is a desired Cartesian 
trajectory, y„ is positive-definite diagonal matrix and function sgn(*) stands for the signum 
function of (*) and 

O c = o x ~ &d 

S x = Ax 2 + « 2 Ax 2 

S rf = Sxi^exp-'^-^, k > 

Now, substituting equation (13) in (12) we have that 

S r2 =r 1 (q)X-r 1 (q)(x d -a 2 Ax 2 + S d -7p f sgn(Se(r))dr) (14) 

Uncertain Open Loop Equation 

Using equation (11), the uncertain parameterization of Y r © r becomes 

H(q)q r + Qft(q) + S(q,q)) q,. + g(q) = Y r & r (15) 

If we add and subtract equation (15) to (1), the uncertain open loop error equation is defined 

H(q)§r2 + Qfl(q) + S(q,q)J S r2 = u %© r (16) 

Theorem 2: [Local Stability] Assuming that the initial conditions and the desired trajectories 
are within a space free of singularities. Consider the uncertain open loop error equation (16) 
in closed loop with the controller given by 

u = -K d2 S r2 (17) 

with K d2 an n x n diagonal symmetric positive-definite matrix. Then, for large enough 
gain K d 2 and small enough error in initial conditions, local exponential tracking is assured 
provided that 7 p > ||j(q)S r2 + J(q)S r2 + j(q)A/X r2 + J(q)A/X r2 + J(q)A/X r2 ||. 

Cartesian Controllers for Tracking of Robot Manipulators under Parametric Uncertainties 1 1 5 

Proof. Substituting equation (17) into (16) we obtain the closed-loop dynamics given as 

H(q)S r2 = - Qft(q) + S(q,q) j S r2 - K rf2 S r2 - %® (18) 

The proof is organized in three parts as follows. 

Part 1: Boundedness of Closed-loop Trajectories. Consider the following Lyapunov function 

V = ls, T 2 H(q)S,. 2 (19) 

whose total derivative of (19) along its solution (18) leads to 

\/ = -S, T 2 K d2 S r2 -S, T 2 Y r © (20) 

Similarly to [Parra & Hirzinger (2000)], we have that Y,@ < rj(t) with ;/ a functional that 
bounds Y,-. Then, equation (20) becomes 

y<-S, T 2 K d2 S,. 2 -||S,. 2 ||>7(t) (21) 

For initial errors that belong to a neighborhood e\ with radius r > near the equilibrium 
S r 2 = 0/ we have that thanks to Lyapunov arguments, there is a large enough feedback gain 
K^ 2 such that S r2 converges into a set-bounded e\. Thus, the boundedness of tracking errors 
can be concluded, namely 

S r2 -► ei as t -)■ oo (22) 


S r2 G Ceo => ||S r2 || < e x (23) 

where e\ > is a upper bounded. 

Since desired trajectories are C 2 and feedback gains are bounded, we have that (q r , q r ) G Coo, 

which implies that X r2 6 Coo if J (q) G ^Coo- Then, the right hand side of (18) is bounded 
given that the Coriolis matrix and gravitational vector are also bounded. Since H(q) and 

H (q) are uniformly bounded, it is seen from (18) that S r2 G Coo- Hence there exists a 
bounded scalar e 2 > such that 

II ^2 II < £2 (24) 

So far, we conclude the boundedness of all closed-loop error signals. 
Part 2. Sliding Mode. If we add and subtract J _1 (q)X r to (12), we obtain 

S r2 = T 1 (q)X - T 1 (q)X r2 ± r 1 (q)X r2 

= J- 1 (q)(X-X r2 ) + (J- 1 (q)-r 1 (q))X r 2 

= J- 1 (q)S. Y -A/X r2 (25) 

which implies that A/ = J (q) — J (q) is also bounded. Now, we will show that a sliding 

mode at S e = arises for all time as follows. 

If we premultiply (25) by J(q) and rearrange the terms, we obtain 

S. l -=J(q)S r2 + J(q)A/X r2 (26) 

116 Robot Arms 

Since S. T = S e + j p j t sgn(S e (£))d£, we have that 

S e = - 7p [ t sgn(S e (0)d£ + K<l)(Sr2 + A/X r2 ) (27) 

J to 

Deriving (27), and then premultiplying by SJ, , we obtain 

SjS, = - 7p |S e | + Sj- (j(q)§ r2 + J(q)A/X r2 )) 

< - 7 p|S e | + C|S e | 

<-( 7p -f)|S e | 

= -FlSel (28) 

where ft = 7,, - £and Z, = j(q)S r2 + J(q)S r2 + /(<?)A/X r2 + J(q)A/X r2 + J(q)A/X r2 . Therefore, 
we obtain the sliding mode condition if 

7 P > C (29) 

in such a way that }i > guarantees the existence of a sliding mode at S e = at time t e < 
'' , However, notice that for any initial condition S e (io) = 0, and hence t = implies that 
a sliding mode in S e = is enforced for all time without reaching phase. 
Part 3: Exponential Convergence. Sliding mode at S e = implies that S, v = Srf, thus 

Ax 2 = -a 2 Ax 2 + S x (*o)e~*'' f (30) 

which decays exponentially fast toward [Ax 2 , Ax 2 ] — > (0, 0), that is 

*2 ->• Xrf and x 2 -»• x d (31) 

it is locally exponential. □ 

Fig. 1. Planar Manipulator of 2-DOF. 

The properties of this controller can be numbered as 

Cartesian Controllers for Tracking of Robot Manipulators under Parametric Uncertainties 


Real and Desired Trajectories in Cartesian Space X-Y 



I] 0.01 


(a) Theorem 1: Plane Phase 

Cartesian Tracking Errors 

Cartesian Tracking Errors 

(b) Theorem 1: Cartesian Tracking Errors 
Fig. 2. Cartesian Tracking of the Robot Manipulator using Theorem 1. 

a) The sliding mode discontinuity associated to S,-2 = is relegated to the first order time 
derivative of S r 2- Then, sliding mode condition in the closed loop system is induced 
by the sgn(S e ) and an exponential convergence of the tracking error is established. 
Therefore, the closed loop is robust due to the invariance achieved by the sliding mode, 
robustness against unmodeled dynamics, and parametric uncertainty. A difference of 
this approach from others [Lee & Choi (2004)], [Barambones & Etxebarria (2002)], [Jager 
(1996)], [Stepanenko (1998)], is that the closed loop dynamics does not exhibit 
chattering. Finally, notice that the discontinuous function sgn(S e ) is only used in the 
stability analysis. 

c) The control synthesis does not depend on any knowledge of the robot dynamics: it is 
model free. In addition, a smooth control input is guaranteed. 

d) Taking y„ = in equation (13), it is obtained the joint error manifold S,.j defined in the 
Case No.l, which is commonly used in several approaches. However under this sliding 
surface it is not possible to prove convergence in finite time as well as reaching the sliding 
condition. Then, a dynamic change of coordinates is proposed, where for a large enough 


Robot Arms 

feedback gain K^ in the control law, the passivity between i/j and S r 2 is preserved with 
Vl = S r 2 [Parra & Hirzinger (2000)]. In addition, for large enough 7 p the dissipativity is 
established between S e and r]2 with r]2 = S e . 

e) In order to differentiate from other approaches where the parametric uncertainty in 
the Jacobian matrix is expressed as a linear combination of a selected set of kinematic 
parameters [Chea (1999)], [Chea (2001)], [Huang (2002)], [Chea 
(2004)], [Chea (2006)], [Chea (2006)], in this chapter the Jacobian uncertainty 
is parameterized in terms of a regressor times as parameter vector. To get the parametric 
uncertainty, this vector is multiplied by a factor with respect to the nominal value. 

Real and Desired Trajectories in Cartesian Space X-Y 

0.4 0.45 0.5 0.55 


(a) Theorem 2: Plane Phase 

0.6 0.65 

E -0.01 


Cartesian Tracking Err 

Cartesian Tracking e 

(b) Theorem 2: Cartesian Tracking Errors 
Fig. 3. Cartesian Tracking of the Robot Manipulator using Theorem 2. 

4. Simulation results 

In this section we present simulation results carried out on 2 degree of freedom (DOF) planar 
robot arm, Fig. 1. The experiments were developed on Matlab 6.5 and each experiment has an 
average running of 3 [s]. Parameters of the robot manipulator used in these simulations are 
shown in Table 1. 

Cartesian Controllers for Tracking of Robot Manipulators under Parametric Uncertainties 


Joint Control 1 

Joint Control 2 

(a) Theorem 1: Control Inputs 

Joint Control 1 

(b) Theorem 2: Control Inputs 

Fig. 4. Control Inputs applied to Each Joint. 







8 Kg 

5 Kg 

0.5 m 

0.35 m 







0.19 m 

0.12 m 

0.02 Kgm 1 

0.16 Kgm 1 

Table 1. Robot Manipulator Parameters. 

The objective of these experiments is to given a desired trajectory, the end effector must follow 
it in a finite time. The desired task is defined as a circle of radius 0.1 [m] whose center located 
at X=(0.55,0) [m] in the Cartesian workspace. The initial condition is defined as [<7i(0) = 
— 0.5, 92(0) = 0.9] [rad]. which is used for all experiments. In addition, we consider zero 
initial velocity and 95% of parametric uncertainty. 

The performance of the robot manipulator using equations (9) and (10) defined in theorem 1 
are presented in Fig. 2. In this case, the end-effector tracks the desired Cartesian trajectory 
once the Cartesian error manifold is reached, Fig. 2(a). In addition, as it is showed in Fig. 2(b), 


Robot Arms 

Real and Desired Trajectories in Cartesian Space X-Y 

(a) TBG: Plane Phase 

Cartesian Tracking Errors 

(b) TBG: Cartesian Tracking Errors 
Fig. 5. Cartesian Tracking of the Robot Manipulator using TBG 

the Cartesian tracking errors converge asymptotically to zero in few seconds. However, for 
practical applications it is necessary to know exactly the regressor and the inverse Jacobian. 
Now, assuming that the Jacobian is uncertain, there is no knowledge of the regressor, and 
there cannot be any overparametrization, then a Cartesian tracking of the robot manipulator 
using control law defined in equation (17) is presented in Fig 3(a). As it is expected, after 
a very short time, approximately 2 [s], the end effector of the robot manipulator follows the 
desired trajectory, Fig. 3(a) and Fig. 3(b). This is possible because in the proposed scheme all 
the time it is induced a sliding mode. Thus, it is more faster and robust. 

On the other hand, in Fig. 4 are shown the applied input torques for each joint of the robot 
manipulator for the cases 1 and 2. It can be see that control inputs using the controller defined 
in equation (17) are more smooth and chattering free than controller defined in equation (9). 
Given that in several applications, such as manipulation tasks or bipedal robots, it is not 
enough the convergence of the errors when f tends to infinity. Finite time convergence faster 
that exponential convergence has been proposed [Parra & Hirzinger (2000)]. To speed up the 

Cartesian Controllers for Tracking of Robot Manipulators under Parametric Uncertainties 


response, a time base generator (TBG) that shapes a feedback gain a 2 is used. That is, it is 
necessary to modify the feedback gain otj defined in equation (13) by 

CC 2 (t) = 0C 




where ocq = 1 + e, for small positive scalar e such that dcq is close to 1 and < J C 1. The 
time base generator £ = £(f ) £ C 2 must be provided by the user so as to get £ to go smoothly 
from to 1 in finite time t = tf,, and f = £(f) is a bell shaped derivative of £ such that 
£(fo) = f (t/j) = [Parra & Hirzinger (2000)]. Accordingly, given that the convergence speed 
of the tracking errors is increased by the TBG, a finite time convergence of the tracking errors 
is guaranteed. 

In the Fig. 5 are shown simulation results using a finite time convergence at tj, = 0.4 [s]. As 
it is expected, the end effector follows exactly the desired trajectory at £(, > 0.4 [s], as shown 
in Fig. 5(a). At the same time, Cartesian tracking errors converge to zero in the desired time, 
Fig. 5(b). 

The feedback gains used in these experiments are given in Table 2 where the subscript ji 
represents the joint of the robot manipulator with i = 1, 2. 







k p 
































Table 2. Feedback Gains 

5. Conclusion 

In this chapter, two Cartesian controllers under parametric uncertainties are presented. In 
particular, an alternative solution to the Cartesian tracking control of the robot manipulator 
assuming parametric uncertainties is presented. To do this, second order sliding surface is 
used in order to avoid the high frequency commutation. In addition, closed loop renders a 
sliding mode for all time to ensure convergence without any knowledge of robot dynamics 
and Jacobian uncertainty. Simulation results allow to visualize the predicted stability 
properties on a simple but representative task. 

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on Robotics and Automation, pp.2349-2354. 

Robotic Grasping of Unknown Objects 

Mario Richtsfeld and Markus Vincze 
Institute of Automation and Control Vienna University of Technology, Vienna 


1. Introduction 

This work describes the development of a novel vision-based grasping system for unknown 
objects based on laser range and stereo data. The work presented here is based on 2.5D point 
clouds, where every object is scanned from the same view point of the laser range and 
camera position. We tested our grasping point detection algorithm separately on laser range 
and single stereo images with the goal to show that both procedures have their own 
advantages and that combining the point clouds reaches better results than the single 
modalities. The presented algorithm automatically filters, smoothes and segments a 2.5D 
point cloud, calculates grasping points, and finds the hand pose to grasp the desired object. 

Fig. 1. Final detection of the grasping points and hand poses. The green points display the 
computed grasping points with hand poses. 

The outline of the paper is as follows: The next Section introduces our robotic system and its 
components. Section 3 describes the object segmentation and details the analysis of the 
objects to calculate practical grasping points. Section 4 details the calculation of optimal 
hand poses to grasp and manipulate the desired object without any collision. Section 5 
shows the achieved results and Section 6 finally concludes this work. 

124 Robot Arms 

1.2 Problem statement and contribution 

The goal of the work is to show a new and robust way to calculate grasping points in the 
recorded point cloud from single views of a scene. This poses the challenge that only the 
front side of objects is seen and, hence, the second grasp point on the backside of the object 
needs to be assumed based on symmetry assumptions. Furthermore we need to cope with 
the typical sensor data noise, outliers, shadows and missing data points, which can be 
caused by specular or reflective surfaces. Finally, a goal is to link the grasp points to a 
collision free hand pose using a full 3D model of the gripper used to grasp the object. The 
main idea is depicted in Fig. I 1 . 

The main problem is that 2.5D point clouds do not represent complete 3D object 
information. Furthermore stereo data includes measurement noise and outliers depending 
on the texture of the scanned objects. Laser range data includes also noise and outliers 
where the typical problem is missing sensor data because of absorption. The laser exhibits 
high accuracy while the stereo data includes more object information due to the better field 
of view. The contribution is to show in detail the individual problems of using both sensor 
modalities and we then show that better results can be obtained by merging the data 
provided by the two sensors. 

1.3 Related work 

In the last few decades, the problem of grasping novel objects in a fully automatic way has 
gained increasing importance in machine vision and robotics. There exist several approaches 
on grasping quasi planar objects (Sanz et al., 1999; Richtsfeld & Zillich, 2008). (Recatala et al., 
2008) developed a framework for the development of robotic applications based on a grasp- 
driven multi-resolution visual analysis of the objects and the final execution of the 
calculated grasps. (Li et al., 2007) presented a 2D data-driven approach based on a hand 
model of the gripper to realize grasps. The algorithm finds the best hand poses by matching 
the query object by comparing object features to hand pose features. The output of this 
system is a set of candidate grasps that will then be sorted and pruned based on 
effectiveness for the intended task. The algorithm uses a database of captured human grasps 
to find the best grasp by matching hand shape to object shape. Our algorithm does not 
include a shape matching method, because this is a very time intensive step. The 3D model 
of the hand is only used to find a collision free grasp. 

(Ekvall & Kragic, 2007) analyzed the problem of automatic grasp generation and planning 
for robotic hands where shape primitives are used in synergy to provide a basis for a grasp 
evaluation process when the exact pose of the object is not available. The presented 
algorithm calculates the approach vector based on the sensory input and in addition tactile 
information that finally results in a stable grasp. The only two integrated tactile sensors of 
the used robotic gripper in this work are too limited for additional information to calculate 
grasping points. These sensors are only used if a potential stick-slip effect occurs. 
(Miller et al., 2004) developed an interactive grasp simulator "Grasplt!" for different hands 
and hand configurations and objects. The method evaluates the grasps formed by these 
hands. This grasp planning system "Grasplt!" is used by (Xue et al., 2008). They use the 
grasp planning system for an initial grasp by combining hand pre-shapes and automatically 
generated approach directions. The approach is based on a fixed relative position and 

1 All images are best viewed in colour. 

Robotic Grasping of Unknown Objects 125 

orientation between the robotic hand and the object, all the contact points between the 
fingers and the object are efficiently found. A search process tries to improve the grasp 
quality by moving the fingers to its neighboured joint positions and uses the corresponding 
contact points to the joint position to evaluate the grasp quality and the local maximum 
grasp quality is located. (Borst et al., 2003) show that it is not necessary in every case to 
generate optimal grasp positions, however they reduce the number of candidate grasps by 
randomly generating hand configuration dependent on the object surface. Their approach 
works well if the goal is to find a fairly good grasp as fast as possible and suitable. 
(Goldfeder et al., 2007) presented a grasp planner which considers the full range of 
parameters of a real hand and an arbitrary object including physical and material properties 
as well as environmental obstacles and forces. 

(Saxena et al., 2008) developed a learning algorithm that predicts the grasp position of an 
object directly as a function of its image. Their algorithm focuses on the task of identifying 
grasping points that are trained with labelled synthetic images of a different number of 
objects. In our work we do not use a supervised learning approach. We find grasping points 
according to predefined rules. 

(Bone et al., 2008) presented a combination of online silhouette and structured-light 3D 
object modelling with online grasp planning and execution with parallel-jaw grippers. Their 
algorithm analyzes the solid model, generates a robust force closure grasp and outputs the 
required gripper pose for grasping the object. We additionally analyze the calculated 
grasping points with a 3D model of the hand and our algorithm obtains the required gripper 
pose to grasp the object. Another 3D model based work is presented by (El-Khoury et al., 
2007). They consider the complete 3D model of one object, which will be segmented into 
single parts. After the segmentation step each single part is fitted with a simple geometric 
model. A learning step is finally needed in order to find the object component that humans 
choose to grasp. Our segmentation step identifies different objects in the same table scene. 
(Huebner et al., 2008) have applied a method to envelop given 3D data points into primitive 
box shapes by a fit-and-split algorithm with an efficient minimum volume bounding box. 
These box shapes give efficient clues for planning grasps on arbitrary objects. 
(Stansfield, 1991) presented a system for grasping 3D objects with unknown geometry using 
a Salisbury robotic hand, where every object was placed on a motorized and rotated table 
under a laser scanner to generate a set of 3D points. These were combined to form a 3D 
model. In our case we do not operate on a motorized and rotated table, which is unrealistic 
for real world use, the goal is to grasp objects when seen only from one side. 
Summarizing to the best knowledge of the authors in contrast to the state of the art 
reviewed above our algorithm works with 2.5D point clouds from a single-view. We do not 
operate on a motorized and rotated table, which is unrealistic for real world use. The 
presented algorithm calculates for arbitrary objects grasping points given stereo and / or 
laser data from one view. The poses of the objects are calculated with a 3D model of the 
gripper and the algorithm checks and avoids potential collision with all surrounding objects. 

2. Experimental setup 

We use a fixed position and orientation between the AMTEC 2 robot arm with seven degrees 
of freedom and the scanning unit. Our approach is based on scanning the objects on the 



Robot Arms 

table by a rotating laser range scanner and a fixed stereo system and the execution of the 
subsequent path planning and grasping motion. The robot arm is equipped with a hand 
prosthesis from the company Otto Bock 3 , which we are using as gripper, see Fig. 2. The 
hand prosthesis has integrated tactile force sensors, which detect a potential sliding of an 
object and enable the readjustment of the pressure of the fingers. This hand prosthesis has 
three active fingers the thumb, the index finger and the middle finger; the last two fingers 
are for cosmetic reasons. Mechanically it is a calliper gripper, which can only realize a tip 
grasp and for the computation of the optimal grasp only 2 grasping points are necessary. 
The middle between the fingertip of the thumb and the index finger is defined as tool centre 
point (TCP). We use a commercial path planning tool from AMROSE 4 to bring the robot to 
the grasp location. 

The laser range scanner records a table scene with a pan/ tilt-unit and the stereo camera 
grabs two images at -4° and +4°. (Scharstein & Szeliski, 2002) published a detailed 
description of the used dense stereo algorithm. To realize a dense stereo calibration to the 
laser range coordinate system as exactly as possible the laser range scanner was used to scan 
the same chessboard that is used for the camera calibration. At the obtained point cloud a 
marker was set as reference point to indicate the camera coordinate system. We get good 
results by the calibration most of the time. In some cases at low texture of the scanned 
objects and due to the simplified calibration method the point clouds from the laser scanner 
and the dense stereo did not correctly overlap, see Fig. 3. To correct this error of the 
calibration we used the iterative closest point (ICP) method (Besl & McKay, 1992) where the 
reference is the laser point cloud, see Fig. 4. The result is a transformation between laser and 
stereo data that can now be superimposed for further processing. 

Fig. 2. Overview of the system components and their interrelations. 



Robotic Grasping of Unknown Objects 


Fig. 3. Partially overlapping point clouds from the laser range scanner (white points) and 
dense stereo (coloured points). A clear shift between the two point clouds shows up. 

Fig. 4. Correction of the calibration error applying the iterative closest point (ICP) algorithm. 
The red lines represent the bounding boxes of the objects and the yellow points show the 
approximation to the centre of the objects. 


Robot Arms 

3. Grasp point detection 

The algorithm to find grasp points on the objects consists of four main steps as depicted in 

Fig. 5: 

Raw Data Pre-processing: The raw data points are pre-processed with a geometrical 

filter and a smoothing filter to reduce noise and outliers. 

Range Image Segmentation: This step identifies different objects based on a 3D 

DeLaunay triangulation, see Section 4. 

Grasp Point Detection: Calculation of practical grasping points based on the centre of 

the objects, see Section 4. 

Calculation of the Optimal Hand Pose: Considering all objects and the table surface as 

obstacles, find an optimal gripper pose, which maximizes distances to obstacles, see 

Section 5. 


Raw Data 

Calculation of the 
Optimal Hand Pose 

K Range Image 1 

K Grasp Point | 

Detection M— 

Fig. 5. Overview of our grasp point and gripper pose detection algorithm. 

4. Segmentation and grasp point detection 

There is no additional segmentation step for the table surface needed, because the red light 
laser of the laser range scanner is not able to detect the surface of the blue table and the 
images of the stereo camera were segmented and filtered directly. However, plane 
segmentation is a well known technique for ground floor or table surface detection and 
could be used alternatively, e.g., (Stiene et al., 2006). 

The segmentation of the unknown objects will be achieved with a 3D mesh generation, 
based on the triangles, calculated by a DeLaunay triangulation [10]. After mesh generation 
we look at connected triangles and separate objects. 

In most grasping literature it is assumed that good locations for grasp contacts are actually 
at points of high concavity. That's absolutely correct for human grasping, but for grasping 
with a robotic gripper with limited DOF and only two tactile sensors a stick slip effect 
occurs and makes these grasp points rather unreliable. 

Consequently to realize a possible, stable grasp the calculated grasping points should be 
near the centre of mass of the objects. Thus, the algorithm calculates the centre c of the 
objects based on the bounding box, Fig. 4, because with a 2.5D point cloud no accurate 
centre of mass can be calculated. Then the algorithm finds the top surfaces of the objects 
with a RANSAC based plane fit (Fischler & Bolles, 1981). We intersect the point clouds with 
horizontal planes through the centre of the objects. If the object does not exhibit a top plane, 

Robotic Grasping of Unknown Objects 129 

the normal vector of the table plane will be used. From these n cutting plane points p i we 
calculate the (planar) convex hull V , using Equ. 1 and illustrated in Fig. 6. 

V = ConvexHull\\Jp i ) (1) 

With the distances between two neighbouring hull points to the centre of the object c we 
calculate the altitude d of the triangle, see Equ. 2. v is the direction vector to the 
neighbouring hull point and w is the direction vector to c. Then the algorithm finds the 
shortest normal distance <!,„,„ of the convex hull lines, illustrated in Fig. 6 as red lines, to the 
centre of the object c, where the first grasping point is located. 

d = l i^r ( 2 ) 

In 2.5D point clouds it is only possible to view the objects from one side, however we 
assume a symmetry of the objects. Hence, the second grasping point is determined by a 
reflection of the first grasping point using the centre of the object. We check a potential 
lateral and above grasp of the object on the detected grasping points with a simplified 3D 
model of the hand. If no accurate grasping points could be calculated with the convex hull 
of the cutting plane points p l the centre of the object is displaced in lmm steps towards the 
top surface of the object (red point) with the normal vector of the top surface until a positive 
grasp could be detected. Another method is to calculate the depth of indentation of the 
gripper model and to calculate the new grasping points based on this information. 
Fig. 6 gives two examples and shows that the laser range images often have missing data, 
which can be caused by specular or reflective surfaces. Stereo clearly correct this 
disadvantage, see Fig. 7. 

Fig. 6. Calculated grasping points (green) based on laser range data. The yellow points show 
the centre of the objects. If, through the check of the 3D gripper no accurate grasping points 
could be calculated with the convex hull (black points connected with red lines) the centre of 
the objects is displaced towards the top surface of the objects (red points). 


Robot Arms 

Fig. 7 illustrates that with stereo data alone there are definitely better results possible then 
with laser range data alone given that object appearance has texture. This is also reflected in 
Tab. 2. Fig. 8 shows that there is a smaller difference between the stereo data alone (see Fig. 
7) and the overlapped laser range and stereo data, which Tab. 2 confirms. 


Fig. 7. Calculated grasping points (green) based on stereo data. The yellow points show the 
centre of mass of the objects. If, through the check of the 3D gripper no accurate grasping 
points could be calculated with the convex hull (black points connected with red lines) the 
centre of the objects is displaced towards the top surface of the objects (red points). 

5. Grasp pose 

To successfully grasp an object it is not always sufficient to find locally the best grasping 
points, the algorithm should also decide at which angle it is possible to grasp the selected 
object. For this step we rotate the 3D model of the hand prosthesis around the rotation axis, 
which is defined by the grasping points. The rotation axis of the hand is defined by the 
fingertip of the thumb and the index finger of the hand, as illustrated in Fig. 9. The 
algorithm checks for a collision of the hand with the table, the object that shall be grasped 
and all obstacles around it. This will be repeated in 5° steps to a full rotation by 180°. The 
algorithm notes with each step whether a collision occurs. Then the largest rotation range 
where no collision occurs is found. We find the optimal gripper position and orientation by 
an averaging of the maximum and minimum largest rotation range. From this the algorithm 
calculates the optimal gripper pose to grasp the desired object. 

The grasping pose depends on the orientation of the object itself, surrounding objects and the 
calculated grasping points. We set the grasping pose as a target pose to the path planner, 
illustrated in Fig. 9 and Fig. 1. The path planner tries to reach the target object on his part. Fig. 
10 shows the advantage to calculate the gripper pose. The left Figure shows a collision free 
path to grasp the object. The right Figure illustrates a collision of the gripper with the table. 

6. Experiments and results 

To evaluate our method, we choose ten different objects, which are shown in Fig. 11. The 
blue lines represent the optimal positions for grasping points. Optimal grasping points are 

Robotic Grasping of Unknown Objects 


Fig. 8. Calculated grasping points (green) based on the combined laser range and stereo 

required to be placed on parallel surfaces near the centre of the objects. To challenge the 
developed algorithm we included one object (Manner, object no. 6), which is too big for the 
used gripper. The algorithm should calculate realistic grasping points for object no. 6 in the 
pre-defined range, however it should recognize that the object is too large and the 
maximum opening angle of the hand is too small. 

Fig. 9. The rotation axis of the hand is defined by the fingertip of the thumb and the index 
finger of the gripper. This rotation axis must be aligned with the axis defined by the 
grasping points. The calculated grasping pose of the gripper is by object no. 8 (Cappy) 
-32.5° and object no. 9 (Smoothie) -55°. 


Robot Arms 

Fig. 10. The left Figure shows the calculated grasping points with an angle adjustment, 
where as the right Figure shows a collision with the table and a higher collision risk with the 
left object no. 8 (Cappy) as the left Figure with an angle adjustment of -55°. 

In our work, we demonstrate that our grasping point detection algorithm and the validation 
with a 3D model of the used gripper for unknown objects shows very good results, see 
Tab. 2. All tests were performed on a PC with 3.2GHz Pentium dual-core processor and the 
average run time is about 463.78sec and the calculation of the optimal gripper pose needs 
about 380.63sec, see Tab. 1 for the illustrated point cloud, see Fig. 9. The algorithm is 
implemented in C++ using the Visualization ToolKit (VTK) 5 . 

Calculation Steps 

Time [sec] 

Filter (Stereo Data) 


Smooth (Stereo Data) 


Mesh Generation 




Grasp Point Detection 


Grasp Angle 




Table 1. Duration of calculation steps. 

Tab. 2 illustrates the evaluation results of the detected grasping points by comparing them 
to the optimal grasping points as defined in Fig. 11. For the evaluation every object was 
scanned four times in combination with another object in each case. This analysis shows that 
a successful grasp based on stereo data with 82.5% is considerably larger than with laser 
range data with 62.5%. The combination of both data sets with 90% definitely wins . 
We tested every object with four different combined point clouds, as illustrated in Tab. 3. In 
no case the robot was able to grasp the test object no. 6 (Manner), because the size of the 
object is too big for the used gripper. This fact could be determined before with the 
computation of the grasping points, however the calculated grasping points are in the 

5 Open source software, 

Robotic Grasping of Unknown Objects 


defined range of object no. 6. Thus the negative test object, as described in 
Section 4 was successfully tested. 



Laser [%] 

Stereo [%] 

Both [%] 























































Table 2. Grasping rate of different objects on pre-defined grasping points. 

Tab. 2 shows that the detected grasping points of object no. 2 (Yippi) are not ideal to grasp it. 
The 75%, in Tab. 3 were possible due to the rubber coating of the hand and the compliance of 
the object. For a grasp to be counted as successful, the robot had to grasp the object, lift it up 
and hold it without dropping it. On average, the robot picked up the unknown objects 85%, 
of the time, including the defined test object (Manner, object no. 6), which is too big for the 
used gripper. If object no. 6 is not regarded success rate is 95%,. 

Fig. 11. Ten test objects. The blue lines represent the optimal positions for grasping points 
near the centre of the objects, depending on the used gripper. From left top: 1. Dextro, 2. 
Yippy, 3. Snickers, 4. Cafemio, 5. Exotic, 6. Manner, 7. Maroni, 8. Cappy, 9. Smoothie, 
10. Koala. 

For objects such as Dextro, Snickers, Cafemio, etc., the algorithm performed perfectly with a 
100%, grasp success rate in our experiments. However, grasping objects such as Yippi or 
Maroni is more complicated, because of the strongly curved surfaces, and so its a greater 
challenge to successfully detect possible grasping points, so that even a small error in the 
grasping point identification, resulting in a failed grasp attempt. 


Robot Arms 



Grasp-Rate [%] 































Table 3. Successfully grasps with the robot based on point clouds from combined laser range 
and stereo data. 

7. Conclusion and future work 

In this work we present a framework to successfully calculate grasping points of unknown 
objects in 2.5D point clouds from combined laser range and stereo data. The presented 
method shows high reliability. We calculate the grasping points based on the convex hull 
points, which are obtained from a plane parallel to the top surface plane in the height of the 
visible centre of the objects. This grasping point detection approach can be applied to a 
reasonable set of objects and for the use of stereo data textured objects should be used. The 
idea to use a 3D model of the gripper to calculate the optimal gripper pose can be applied to 
every gripper type with a suitable 3D model of the gripper. The presented algorithm was 
tested to successfully grasp every object with four different combined point clouds. In 85%> 
of all cases, the algorithm was able to grasp completely unknown objects. 
Future work will extend this method to obtain more grasp points in a more generic sense. 
For example, with the proposed approach the robot could not figure out how to grasp a cup 
whose diameter is larger than the opening of the gripper. Such a cup could be grasped from 
above by grasping the rim of the cup. This method is limited to successfully convex objects. 
For this type of objects the algorithm must be extended, but with more heuristic functions 
the possibility to calculate wrong grasping points will be enhanced. 

In the near future we plan to use a deformable hand model to reduce the opening angle of 
the hand, so we can model the closing of a gripper in the collision detection step. 

8. References 

Besl, P.J., McKay, H.D. (1992). A method for registration of 3-D shapes, IEEE 
Transactions on Pattern Analysis and Machine Intelligence, Vol. 14, No. 2, pp. 


Borst, C, Fischer, M., Hirzinger, G. (2003). Grasping the dice by dicing the grasp. IEEE/RSJ 
International Conference on Robotics and Systems, pp. 3692-3697. 

Robotic Grasping of Unknown Objects 135 

Bone, G.M., Lambert, A., Edwards, M. (2008). Automated modelling and robotic grasping of 

unknown three-dimensional objects. IEEE International Conference on Robotics 

and Automation, pp. 292-298. 
Castiello, U. (2005). The neuroscience of grasping. Nature Reviews Neuroscience, Vol. 6, 

No. 9, pp. 726-736. 
Ekvall, S., Kragic, D. (2007). Learning and Evaluation of the Approach Vector for Automatic 

Grasp Generation and Planning. IEEE International Conference on Robotics and 

Automation, pp. 4715-4720. 
El-Khoury, S., Sahbani A., Perdereau, V. (2007). Learning the Natural Grasping Component 

of an Unknown Object. IEEE/RSJ International Conference on Intelligent Robots 

and Systems, pp. 2957-2962. 
Fischler, M.A., Bolles, R.C. (1981). Random Sample Consensus: A Paradigm for Model 

Fitting with Applications to Image Analysis and Automated Cartography, 

Communications of the ACM, Vol. 24, No. 6, pp. 381-395. 
Goldfeder, C, Allen, P., Lackner, C, and Pelossof, R. (2007). Grasp Planning via 

Decomposition Trees. IEEE International Conference on Robotics and Automation, 

pp. 4679-4684. 
Huebner, K., Ruthotto, S., and Kragic, D. (2008). Minimum Volume Bounding Box 

Decomposition for Shape Approximation in Robot Grasping. IEEE International 

Conference on Robotics and Automation, pp. 1628-1633. 
Li, Y., Fu, J.L., Pollard, N.S. (2007). Data-Driven Grasp Synthesis Using Shape Matching and 

Task-Based Pruning. IEEE Transactions on Visualization and Computer Graphics, 

Vol. 13, No. 4, pp. 732-747. 
Miller, A.T., Knoop, S. (2003). Automatic grasp planning using shape primitives. IEEE 

International Conference on Robotics and Automation, pp. 1824-1829. 
Recatala, G., Chinellato, E., Del Pobil, A.P., Mezouar, Y., Martinet, P. (2008). Biologically- 
inspired 3D grasp synthesis based on visual exploration. Autonomous Robots, 

Vol. 25, No. 1-2, pp. 59-70. 
Richtsfeld, M., Zillich, M. (2008). Grasping Unknown Objects Based on 2.5D Range Data. 

IEEE Conference on Automation Science and Engineering, pp. 691-696. 
Sanz, P.J., Inesta, J.M., Del Pobil, A. P. (1999). Planar Grasping Characterization Based on 

Curvature-Symmetry Fusion. Applied Intelligence, Vol. 10, No. 1, 

pp. 25-36. 
Saxena, A., Driemeyer, J., Ng, AY. (2008). Robotic Grasping of Novel Objects using Vision. 

International Journal of Robotics Research, Vol. 27, No. 2, pp. 157-173. 
Scharstein, D., Szeliski, R. (2002). A Taxonomy and Evaluation of Dense Two-Frame 

Stereo Correspondence Algorithms, International Journal of Computer Vision, 

Vol. 47, No. 1-3, pp. 7-42. 
Stansfield, S.A. (1991). Robotic grasping of unknown objects: A knowledge-based approach. 

International Journal of Robotics Research, Vol. 10, No. 4, pp. 314-326. 
Stiene, S., Lingemann, K., Niichter, A., Hertzberg, J. (2006). Contour-based Object 

Detection in Range Images, Third International Symposium on 3D Data Processing, 

Visualization, and Transmission, pp. 168-175. 

136 Robot Arms 

Xue, Z., Zoellner, J.M., Dillmann, R. (2008). Automatic Optimal Grasp Planning Based On 
Found Contact Points. IEEE/ASME International Conference on Advanced 
Intelligent Mechatronics, pp. 1053-1058. 


Object-Handling Tasks Based on 
Active Tactile and Slippage Sensations 

Masahiro Ohka 1 , Hanafiah Bin Yussof 2 and Sukarnur Che Abdullah 12 

x Nagoya University 

2 Universiti Teknologi MARA 



1. Introduction 

Many tactile sensors have been developed to enhance robotic manufacturing tasks, such as 
assembly, disassembly, inspection and materials handling as described in several survey 
papers (Harmon, 1982; Nicholls & Lee 1989; Ohka, 2009a). In the last decade, progress has 
been made in tactile sensors by focusing on limited uses. Many examples of practical tactile 
sensors have gradually appeared. Using a Micro Electro Mechanical System, MEMS-based 
tactile sensors have been developed to incorporate pressure-sensing elements and 
piezoelectric ceramic actuators into a silicon tip for detecting not only pressure distribution 
but also the hardness of a target object (Hasegawa et al., 2004). Using PolyVinylidene 
DiFluoride, a PVDF film-based tactile sensor has been developed to measure the hardness of 
tumors based on comparison between the obtained sensor output and the input oscillation 
(Tanaka et al., 2003). A wireless tactile sensor using two-dimensional signal transmission has 
been developed to be stretched over a large sensing area (Chigusa et al., 2007). An advanced 
conductive rubber-type tactile sensor has been developed to be mounted on robotic fingers 
(Shimojo et al., 2004). Furthermore, image based tactile sensors have been developed using a 
charge-coupled device (CCD) and complementary metal oxide semiconductor (CMOS) 
cameras and image data processing, which are mature techniques (Ohka, 1995, 2004, 2005a, 
2005b, Kamiyama et al, 2005). 

In particular, the three-axis tactile sensor that is categorized as an image based tactile sensor 
has attracted the greatest anticipation for improving manipulation because a robot must 
detect the distribution not only of normal force but also of slippage force applied to its 
finger surfaces (Ohka, 1995, 2004, 2005a, 2005b, 2008). In addition to our three-axis tactile 
sensors, there are several designs of multi-axis force cells based on such physical 
phenomena as magnetic effects (Hackwood et al., 1986), variations in electrical capacity 
(Novak, 1989; Hakozaki & Shinoda 2002), PVDF film (Yamada & Cutkosky, 1994), and a 
photointerrupter (Borovac et al., 1996). 

Our three-axis tactile sensor is based on the principle of an optical waveguide-type tactile 
sensor (Mott et al, 1984; Tanie et al., 1986; Nicholls et al, 1990; Kaneko et al., 1992; Maekawa 
et al., 1992), which is composed of an acrylic hemispherical dome, a light source, an array of 
rubber sensing elements, and a CCD camera (Ohka, 1995, 2004a, 2005a, 2005b, 2008). The 
sensing element of the silicone rubber comprises one columnar feeler and eight conical 


Robot Arms 

feelers. The contact areas of the conical feelers, which maintain contact with the acrylic 
dome, detect the three-axis force applied to the tip of the sensing element. Normal and 
shearing forces are then calculated from integration and centroid displacement of the 
grayscale value derived from the conical feeler's contacts. 

The tactile sensor is evaluated with a series of experiments using an x-z stage, a rotational 
stage, and a force gauge. Although we discovered that the relationship between the 
integrated grayscale value and normal force depends on the sensor's latitude on the 
hemispherical surface, it is easy to modify the sensitivity based on the latitude to make the 
centroid displacement of the grayscale value proportional to the shearing force. 
To demonstrate the effectiveness of the three-axis tactile sensor, we designed a hand system 
composed of articulated robotic fingers sensorized with the three-axis tactile sensor (Ohka, 
2009b, 2009c). Not only tri-axial force distribution directly obtained from the tactile sensor 
but also the time derivative of the shearing force distribution are used for the hand control 
algorithm: the time derivative of tangential force is defined as slippage; if slippage arises, 
grasping force is enhanced to prevent fatal slippage between the finger and an object. In the 
verification test, the robotic hand twists on a bottle cap completely. 

In the following chapters, after the optical three-axis tactile sensor is explained, the robotic 
hand sensorized with the tactile sensors is described. The above cap-twisting task is 
discussed to show the effectiveness of tri-axial tactile data for robotic control. 

2. Optical three-axis tactile sensor 

2.1 Sensing principle 

2.1.1 Structure of optical tactile sensors 

Figure 1 shows a schematic view of the present tactile processing system to explain the 
sensing principle. The present tactile sensor is composed of a CCD camera, an acrylic dome, 
a light source, and a computer. The light emitted from the light source is directed into the 
optical waveguide dome. Contact phenomena are observed as image data, acquired by the 
CCD camera, and transmitted to the computer to calculate the three-axis force distribution. 

Sensing element 

, Light directed by optical fibers 
Bore scope guide 

Cylindrical feeler 
Sensing element 

Fig. 1. Principle of the three-axis tactile sensor system 

Object-Handling Tasks Based on Active Tactile and Slippage Sensations 


In this chapter, we adopt a sensing element comprised of a columnar feeler and eight conical 
feelers, as shown in Fig. 2, because the element showed wide measuring range and good 
linearity in a previous paper (Ohka, 2004b). Since a single sensing element of the present 
tactile sensor should carry a heavier load compared to a flat-type tactile sensor, the height of 
the columnar feeler of the flat-type tactile sensor is reduced from 5 to 3 mm. The sensing 
elements are made of silicone rubber (KE119, Shinetsu) and are designed to maintain contact 
with the conical feelers and the acrylic board and to make the columnar feelers touch an 
object. Each columnar feeler features a flange to fit into a counter bore portion in the fixing 
dome to protect the columnar feeler from horizontal displacement caused by shearing force. 

2.1.2 Expressions for sensing element located on vertex 

Dome brightness is inhomogeneous because the edge of the dome is illuminated and light 
converges on its parietal region. Since the optical axis coincides with the center line of the 
vertex, the apparent image of the contact area changes based on the sensing element's 
latitude. Although we must consider the above problems to formulate a series of equations 
for the three components of force, the most basic sensing element located on the vertex will 
be considered first. 

0.6 0.3 

Column feeler Cone feeler 

Flange part 

unit: mm 

Fig. 2. Sensing element 

Top (North pole) 
g( x v y\) 

Fig. 3. Relationship between spherical and Cartesian coordinates 

140 Robot Arms 

Coordinate O-xyz is adopted, as shown in Fig. 3. Based on previous studies (Ohka, 2005), 
since grayscale value g(x, y) obtained from the image data is proportional to pressure 
p(x, y) caused by contact between the acrylic dome and the conical feeler, normal force is 
calculated from integrated grayscale value G . Additionally, shearing force is proportional 
to the centroid displacement of the grayscale value. Therefore, the F r , F , and F 2 values are 
calculated using integrated grayscale value G and the horizontal displacement of the 
centroid of grayscale distribution u = u x i + u j as follows: 

F, =/,(«,), (1) 

F, =/,(«,), (2) 

F,=-S(G), (3) 

where i and j are the orthogonal base vectors of the x- and y-axes of a Cartesian 
coordinate, respectively, and f x {x), fAx), and g(x) are approximate curves estimated in 
calibration experiments. 

2.1.3 Expressions for sensing elements other than those located on vertex 

For sensing elements other than those located on the vertex, each local coordinate 0,-x,i/,2, is 
attached to the root of the element, where suffix i denotes element number. Each z,-axis is 
aligned with the center line of the element and its direction is along the normal direction of 
the acrylic dome. The 2,-axis in local coordinate Oi-Xiy&i is taken along the center line of 
sensing element i so that its origin is located on the crossing point of the center line and the 
acrylic dome's surface and its direction coincides with the normal direction of the acrylic 
dome. If the vertex is likened to the North Pole, the directions of the Xf and y,-axes are north 
to south and west to east, respectively. Since the optical axis direction of the CCD camera 
coincides with the direction of the z-axis, information of every tactile element is obtained as 
an image projected into the O-xy plane. The obtained image data g(x,y) should be 
transformed into modified image g(x.,y\ , which is assumed to be taken in the negative 
direction of the z,-axis attached to each sensing element. The transform expression is derived 
from the coordinate transformation of the spherical coordinate to the Cartesian coordinate 
as follows: 

g(X;,y,) = g(x,y)/sm l p i (4) 

Centroid displacements included in Eqs. (1) and (2), and uAx,y\ and w (x,y) should be 
transformed into uAx^yA and U (x t ,yA as well. In the same way as Eq. (4), the transform 
expression is derived from the coordinate transformation of the spherical coordinate to the 
Cartesian coordinate as follows: 

, > u,{x,y)cash + u{x,y)smh 

u A x ,,y,) = : — ■ / (5) 

sin q>. 
u(x.,y i ) = uAx,y)sintf> i +u (x,y)costfi ; . (6) 

Object-Handling Tasks Based on Active Tactile and Slippage Sensations 


Sensing element Optical fiber 

Fiber scope 

coo ©"ooo-j 


Fig. 4. Fingertip including three-axis tactile sensor 

2.1.4 Design of optical three-axis tactile sensor 

Since the tactile sensor essentially needs to be a lens system, it is difficult to make it thinner; 
thus, it should be designed as a type of integrated fingertip and hemispherical three-axis 
tactile sensor, as shown in Fig. 4 (Ohka et al., 2008). Forty-one sensing elements are 
concentrically arranged on the acrylic dome, which is illuminated along its edge by optical 
fibers connected to a light source (ELI-100S, Mitsubishi Rayon Co.) Image data consisting of 
bright spots caused by the feelers' collapse (MSGS-1350-III, Moritex Co.) are retrieved by an 
optical fiber scope connected to the CCD camera (C5985, Hamamatsu Photonics Co.) 

2.2 Procedure of evaluation tests 
2.2.1 Experimental apparatus 

We developed a loading machine shown in Fig. 5 that includes an x-stage, a z-stage, rotary 
stages, and a force gauge (FGC-0.2B, NIDEC-SIMPO Co.) to detect the sensing 
characteristics of normal and shearing forces. The force gauge has a probe to measure force 

Motorized stage 

Force gauge 

Tactile sensor 

Fig. 5. Loading machine 


Robot Arms 

Motorized stag* 




1**1 soual computer 

Stacp operation 


(iiiHiic sampling 


Load measurement 

Image analysis 

measurement program 

Displacement of centroid, integrated grayscale value, load 

Fig. 6. Tactile data processing system 

and can detect force ranging from to 2 N with a resolution of 0.001 N. The positioning 
precisions of the y-, the z-, and rotary stages are 0.001 mm, 0.1 mm, and 0.1 , respectively. 
Output of the present tactile sensor is processed by the data processing system shown in Fig. 
6. The system is composed of a tactile sensor, a loading machine, an image processing board 
(Himawari PCI/S, Library, Co.), and a computer. Image data acquired by the image 
processing board are processed by in-house software. 

The image data acquired by the CCD camera are divided into 41 subregions, as shown in 
Fig. 7. The dividing procedure, digital filtering, integrated grayscale value and centroid 
displacement are processed on the image processing board. Since the image warps due to 
projection from a hemispherical surface, as shown in Fig. 7, software installed on the 
computer modifies the obtained data. The motorized stage and the force gauge are 
controlled by the software. 

2.2.2 Procedure of sensing normal force test 

Because the present tactile sensor can detect not only normal force but also shearing force, 
we must confirm the sensing capability of both forces. In normal-force testing, by applying a 
normal force to the tip of a sensing element using the z-stage after rotating the attitude of 
the tactile sensor, it is easy to test the specified sensing element using the rotary stage. Since 
the rotary stage's center of rotation coincides with the center of the present tactile sensor's 
hemispherical dome, testing any sensing element aligned along the hemisphere's meridian 
is easy. 

2.2.3 Procedure of sensing shearing force test 

When generating the shearing-force component, both the rotary and x-stages are adjusted to 
specify the force direction and sensing element. First, the rotary stage is operated to give 
force direction 6 , as shown in Fig. 8. The x-stage is then adjusted to the applied tilted force 

Object-Handling Tasks Based on Active Tactile and Slippage Sensations 








£^£2i 0.03S 

Fig. 7. Addresses of sensing elements 

at the tip of the specified sensing element. Figure 8 shows that the sensing element located 
on the parietal region can be assigned based on the procedure described above. After that, a 
force is loaded onto the tip of the sensing element using the z-stage. Regarding the manner 

Fig. 8. Generation of shearing force component 

of loading, since the force direction does not coincide with the axis of the sensing element, 
slippage between the probe and the tip of the sensing element occurs. To eliminate this 


Robot Arms 

problem, a spherical concave portion is formed on the probe surface to mate the concave 
portion with the hemispherical tip of the tactile element. Normal force F v and shearing 
force F s applied to the sensing elements are calculated using the following formulas, when 
force F is applied to the tip of the tactile element: 

F N =Fcosd 
F. =F sin 6 


2.3 Sensing ability of optical three-axis tactile sensor 
2.3.1 Sensing ability of normal force 

To evaluate the sensing characteristics of sensing elements distributed on the hemispherical 
dome, we need to measure the variation within the integrated grayscale values generated by 
the sensor elements. Figure 9 shows examples of variation in the integrated grayscale value 
caused by increases in the normal force for sensors #00, #01, #05, #09, #17, #25, and #33. In 
these experiments, normal force is applied to a tip of each tactile element. As the figure 
indicates, the gradient of the relationship between the integrated grayscale value and 
applied force increases with an increase in <p ; that is, sensitivity depends upon the latitude 
on the hemisphere. Dome brightness is inhomogeneous because the edge of the dome is 
illuminated and light converges on its parietal region. Brightness is represented as a 
function of latitude cp , and since sensitivity is uniquely determined by latitude, it is easy to 
modify the sensitivity according to q> . 






#00: <p= 90° 

. »#'05: 105°* 

Jo' 8 

.•S Jo 
, k' s #01 : 75° 

V A** #17 , 1 . 2( i° 

,■ #09.^0° „ 

. ■■ r, n □ ° U 

j g a 

...•"' #25:45° 

1 2 

Normal force, F N N 

Fig. 9. Relationship between applied force and grayscale value 

However, sensing elements located at the same latitude cp show different sensing 
characteristics. For example, the sensitivities of #09 and #17 should coincide since they have 

Object-Handling Tasks Based on Active Tactile and Slippage Sensations 


identical latitude; however, as Fig. 10 clearly indicates, they do not. The difference reflects 
the inhomogeneous brightness of the acrylic dome. Therefore, we need to obtain the 
sensitivity of every sensing element. 

[x10 +4 ] 


,.••■' i • 


x ; : ' 





* *■* 





*y r Segment 2 



Threshold !.*** 







y'\ ' 











Jf Segment 1 


1 2 

Normal force, F w N 

Fig. 10. Approximation using bi-linearity 

As shown in Fig. 9, the relationship between the integrated grayscale value and applied 
normal force is not completely linear. Therefore, we adopt the bi-linear lines shown in Fig. 
10 as function g(x) in Eq. (3) to approximate these curves. Linear approximation 
adequately represents the relationship between force and integrated grayscale value for two 
tactile elements (#12 and #29) with high accuracy; for the other elements bi-linear 
approximation can represent the relationship with a rather high correlation factor ranging 
from 0.911 to 0.997. 

To show that under the combined loading condition normal force component was 
independently obtained with Eq. (3), we applied inclined force to the tip of the tactile 
element to examine the relationship between the normal component of applied force and 
integrated grayscale value. Figure 11 displays the relationship for #00. Even if the 
inclination is varied from -30 * to 30 " , the relationship coincides within a deviation of 3.7%. 
Therefore, the relationship between the normal component of applied force and the 
integrated grayscale value is independent of inclination . 

2.3.2 Sensing ability of shearing force 

When force is applied to the tip of the sensing element located in the parietal region under 
several s, the relationships between the displacement of the centroid and the shearing- 
force component calculated by Eq. (5) are obtained, as shown in Fig. 12. Although the 
inclination of the applied force is varied in a range from 15 " to 60 " , the curves converge 
into a single one. Therefore, the applied shearing force is obtained independently from 
centroid displacement. 


Robot Arms 

[x10 +4 ] 

Normal force, 

Fig. 11. Relationship between integrated grayscale value and applied normal force at several 

Shearing force 
Fig. 12. Relationship between centroid displacement and applied shearing 

When the tactile element accepts directional forces of 45 " , 135 " , 225 " , and 315 " , centroid 
trajectories are shown in Fig. 13 to examine shearing force detection under various 
directions except for the x- and y-directions. If the desired trajectories shown in Fig. 13 are 
compared to the experimental results, they almost trace identical desired trajectories. The 
present tactile sensor can detect various applied forces. 

Object-Handling Tasks Based on Active Tactile and Slippage Sensations 





* 0.1 

ra -0.1 




^V 135° 


45 ° J* 
* / 

\l 315° 

Fig. 13. Trajectory of centroid 

-0.2 -0.1 0.1 0.2 0.3 
x-directional coordinate of centroid mm 

3. Object handling based on tri-axial tactile data 

3.1 Hand robot equipped with optical three-axis tactile sensors 

We designed a two-fingered robotic hand as shown in Fig. 14 for general-purpose use in 
robotics (Ohka et al., 2009b, 2009c). The robotic hand includes links, fingertips equipped 
with the three-axis tactile sensor, and micro actuators (YR-KA01-A000, Yasukawa). Each 
micro actuator, which consists of an AC servo-motor, a harmonic drive, and an incremental 
encoder, was particularly developed for application to a multi-fingered hand. Since the 
tactile sensors must be fitted to a multi-fingered hand, we are developing a fingertip that 
includes the hemispherical three-axis tactile sensor shown in Fig. 4. 

No. 1 Finger 

Z stage 

Finger Attachment 

No. 2 Finger 

Fig. 14. Robotic hand equipped with three-axis tactile sensors 


Robot Arms 

3.2 Kinematics of hand robot 

As shown in Fig. 14, each robotic finger has three movable joints. The frame of the 
workspace is set on the bottom of the z-stage. The kinematics of the present hand is derived 
according to Denavit-Hartenberg notation shown in Fig. 14. The frame of the workspace is 
defined as O-xyz. The frames of O,- ( x.y.z. ) (in the following, O-xyz is used instead of Oo- 
x y z ) are attached on each joint, the basement of the z-stage, or the fingertip, as shown in 
Fig. 14. The velocities of the micro actuators ( 8 = ( 0\ 2 0A) are calculated with 

6 = r(8)r 


to satisfy specified velocity vector r ( = (x y z) ), which is calculated from the planed 
trajectory. Jacobian J (8) is obtained by the kinematics of the robotic hand as follows: 


J(8) = 

- R 13 {k + h C 2 + l f* ) k (#„ S 3 + ^2 C 3 ) + h R r2 R J< 
- R 23 {h + ' 3 C 2 + ' 4 C 23 ) k (^A + ^3 ) + l^ RJ t 
- R 33 {h + ^2 + ^23 ) k (#3, S 3 + ^3 ) + I ,£„ RJ t 


R» K 



fl n C 23 + fl 13 S 23 

-«U S 23 + fl 13 c 23 


K 21 -R 2 2 ^23 


«2, C 23 + fl 23 S 23 

-«2! S 23 + «23 C 23 



«* R 32 V 

_«3, C 23 + fl 33 S 23 

-«3! S 23 + fl 33 C 33 

" B 32_ 

«12 fl ,3 

c<PA + s WA 

-c<PA + s W 2 s, 


a 22 a 23 


C(p 2 S(p, 

c<P 2 s<Pi 

-$q> 2 

a 32 a 33 ^ 

—$cp 1 C 1 + scp 1 scp 2 s 1 

s^jSj + C(pfi(p 2 cq>^ 

cg> 1 c<p 2 _ 

c = cos 6 j , s, = sin j , c<p j = 

cos #> , s^>, = sin cp i , 

c. =cos( 



, s,=sin(d +&.),( i; 7=1,2 



In the above equations, the rotations of the first frame around the x Q - and y -axes are 
denoted as cp x and <p 2 , respectively. The distance between the origins of the m-th and m+1- 
th frames is denoted as l m . The joint angles of the micro actuators on O2- x 2 y 2 z 2 , O3- x 3 y 3 z 3 
and O4- x l y l z l are 1 , 2 , and 3 , respectively. 

Position control of the fingertip is performed based on resolved motion rate control. In this 
control method, joint angles are assumed at the first step, and displacement vector r is 
calculated with kinematics. Adjustment of joint angles is obtained by Eq. (9) and the 
difference between r and objective vector r, to modify joint angle 8, , at the next step. 
The modified joint angle is designated as the current angle in the next step, and the above 
procedure is repeated until the displacement vector at /c-th step r, coincides with objective 
vector r, within a specified error. That is, the following Eqs. (12) and (13) are calculated 
until |r,-rj becomes small enough: 

i t =A 


Object-Handling Tasks Based on Active Tactile and Slippage Sensations 


e M =e,-r(ji-0 


3.3 Control algorithm 

Our objective is to show that the robotic hand adapts its finger trajectory to the environment 
according to tri-axial tactile data. Hence, we make a simple control algorithm for the hand. 
In the algorithm, there is an assumption that finger trajectory provided beforehand to the 
hand is as simple as possible. The trajectory is modified to prevent normal force from 
exceeding a threshold and to stabilize slippage caused on the contact area according to tri- 
axial tactile data. 

1 stari 1 

For every active finger 

SLIP ting: down 
OVER flag: up 

k>P<2) ^ 

For every sensing element 

I Read sensing / 
/ elements .■Liins / 

TOUCH flag: up 
SUP flag; up 



>F 2 

TOUCH flae: up 
OVER flag: up 





TOUCH flae: lip 


Fig. 15. Algorithm of flag analyzer 

Return to the initial 

MOVE flag: down 

SEARCH flag: up 

Move index no. is reset 

velocity Av 


Fig. 16. Algorithm of finger speed estimator 

150 Robot Arms 

The hand is controlled according to velocity control. First, hand status becomes "search 
mode" to make fingers approach an object with finger speed v = v . After the fingers touch 
the object, the hand status becomes "move mode" to manipulate the object with finger speed 
v = v, . During both search and move modes, when the absolute time derivative of the 
shearing force of a sensing element exceeds a threshold dr , this system regards the sensing 
element as slippage. To prevent the hand from dropping the object, re-compressive velocity 
is defined as moving the fingertip along the counter direction of applied force. 
However, if normal force of a sensing element exceeds a threshold F 2 , the re-compressive 
velocity is cancelled to prevent the sensing element from breaking. The hand is controlled 
by a control module with applying total velocity obtained by adding the re-compressive 
velocity to current velocity. 

In our system, the sensor control program and hand control program are executed in 
different computers because CPU time is efficiently consumed using a multi-task program 
method. These programs are synchronized with the following five flags. 

SEARCH: Fingers search for an object with initial finger velocity v until normal force of a 
sensing element exceeds a threshold F, or Slip flag is raised. 
MOVE: This flag is raised whenever the robotic hand manipulates an object. 
TOUCH: This flag is raised whenever one of the fingers touches an object. 
SLIP: This flag is raised whenever the time derivative of shearing force exceeds a threshold 
dr . 

OVER: This flag is raised when normal force of a sensing element exceeds a threshold F 2 . 
These flags are decided according to tri-axial tactile data and finger motions. Since two 
modules, the flag analyzer and finger speed estimator, mainly play the role of object 
handling, these modules are shown in Figs. 15 and 16, respectively. 

In the flag analyzer, TOUCH flag, SLIP flag and OVER flag are decided. The flag analyzer 
regards finger status as touching an object when normal force of a sensing element is 
exceeded or the absolute time derivative of the shearing force is exceeded (SLIP flag is 
raised). Whenever it regards finger status as touching an object, the TOUCH flag is raised. 
The OVER flag is raised when normal force of a sensing element exceeds F, to prohibit re- 
compressive motion. 

In the finger speed estimator, the velocity of the fingertip is determined based on the five 
flag values and conserved whenever contact status is not changed. Since the cap-twisting 
problem requires touch-and-release motion, the MOVE and SEARCH flags are controlled 
according to the TOUCH flag and time spent. Whenever the SLIP flag is raised, a sensing 
element of the largest normal force is determined and the re-compressive velocity of the 
finger is determined as an inward normal line of the sensing element. The re-compressive 
velocity is added to the current velocity, and the resultant velocity is applied to the control 

3.4 Evaluation experiment of object handling 
3.4.1 Experimental apparatus and procedure 

To examine the above algorithm, the robotic hand performed the bottle cap-closing task 
because this task requires a curved trajectory along the cap contour. Evaluation of cap 
closing is performed using an apparatus including a torque sensor. Figure 17 shows the 
apparatus composed of the two-fingered hand, the torque sensor (TCF-0.2N, Nippon 

Object-Handling Tasks Based on Active Tactile and Slippage Sensations 


Tokushu Sokki, Co., Ltd.) and a PET bottle holder. A PET bottle is clamped with two twists 
of the PET holder, and its cap is turned by the robotic hand. The torque sensor measures 
torque with four strain gauges. Variation in gauge resistance is measured as voltage through 
a bridge circuit, and it is sent to a computer with an A/D converter to obtain the 
relationship between finger configuration and generated torque. 

The experimental apparatus is shown in Fig. 17. A PET bottle is held by the holder. At first, 
two fingers approach the cap, and moving direction is changed to the tangential direction of 
the cap surface after grasping force exceeds 1 N. After the finger moves, keeping the 
direction within 10 mm, the fingers are withdrawn from the cap surface and returned to 
each home position from which they started moving. Consequently, the trajectory of the 
fingers is designed as shown in Fig. 17. During the task of closing the cap, variation in 
torque is monitored through the torque sensor to evaluate the task. Even if the trajectory is 
simple, we will show that it adapts to the cap contour in the following section. 

Finger 2 

Fig. 17. Experimental apparatus for cap-twisting task 

3.4.2 Relationship between grasping force and torque 

The relationship between grasping force and torque while twisting the bottle cap is shown 
in Fig. 18 as an overview of the experiment. Since touch-and-release motion is continued 
four times, four groupings are found in Fig. 18. As shown in Fig. 18, compared to the first 
twisting motion, both grasping force and torque decrease considerably in the second 
twisting, and in the third and fourth twistings they increase compared to the former two 
twistings. Since the third and fourth twistings show almost the same variations in grasping 
force and torque, twisting seems to become constant. Therefore, after the third twisting, the 
cap seems to be closed. In the first twisting, we can observe the transition from light twisting 
to forceful twisting because torque increases in spite of constant grasping force. It is shown 
that the cap is turned without resistant torque at first. The reason for reducing grasping 
force and torque in the second twisting is the variation in contact position and status 
between the first and second twistings. Twisting on the cap was successfully completed as 
mentioned above. 


Robot Arms 

Normal force 







Time sec 

L- T *l i I 






-r- 50 

Time sec 
Fig. 19. Relationship between variations in time derivative of shearing force and torque 

3.4.3 Relationship between time derivative of shearing force and torque 

When the cap is twisted on completely, slippage between the robotic finger and the cap 
occurs. To examine this phenomenon, the relationship between the time derivative of the 
shearing force and torque is shown in Fig. 19. As can be seen, the time derivative of the 
shearing force shows periodic bumpy variation. This bumpy variation synchronizes with 
variation in torque. This means large tangential force induces the time derivative of the 
shearing force, which is caused by the trembling of the slipping sensor element. 
To examine the cap-twisting, a comparison between the results of the first screwing and 
fourth twisting is performed with Figs. 20 and 21. In the first twisting, since the cap is loose, 
the marked time derivative of the shearing force does not occur in Fig. 20. On the other 

Object-Handling Tasks Based on Active Tactile and Slippage Sensations 


hand, in the fourth twisting, the marked time derivative of the shearing force does occur 
because of the securing of the cap (Fig. 21). Therefore, the robotic hand can twist on the 
bottle cap completely. Additionally, the time derivative of the shearing force can be adopted 
as a measure for twisting the cap. 

o 0.5i ■ 1 ■ ■ ■ ■ ■ ■ ■ ■ - 




• :#00 
o: #02 




o: #03 








Time sec 

Fig. 20. Detailed relationship between variations in time derivative of shearing force and 
torque at first twisting 


• :#00 



o: #02 


° a: #03 





©: #04 






o A I 






-0 1 



|°0 (^ 





» ° 1 

1" - 

Torque flJ 





-0 4 



0.04 E 





Fig. 21. Detailed relationship between variations in time derivative of shearing force and 
torque at fourth twisting 

3.4.4 Trajectory of fingertip modified according to tri-axial tactile data 

Trajectories of sensor element tips are shown in Figs. 22 and 23. If the result of Fig. 22 is 
compared with the result of Fig. 23, trajectories of Fig. 23 are closer to the cap contour. 
Modification of the trajectory is saturated after closing the cap. Although input finger 
trajectories were a rectangle roughly decided to touch and turn the cap as described in the 
previous section, a segment of the rectangle was changed from a straight line to a curved 
line to fit the cap contour. 


Robot Arms 



\ Cap contour 

Pin #02 

Pin #03 





x-directional coordinate mm 
Fig. 22. Trajectories of sensor element before closing the cap 




Cap contour 


x-directional coordinate mm 

Fig. 23. Trajectories of sensor element after closing the cap 


4. Conclusion 

We developed a new three-axis tactile sensor to be mounted on multi-fingered hands, based 
on the principle of an optical waveguide-type tactile sensor comprised of an acrylic 
hemispherical dome, a light source, an array of rubber sensing elements, and a CCD camera. 
The sensing element of the present tactile sensor includes one columnar feeler and eight 
conical feelers. A three-axis force applied to the tip of the sensing element is detected by the 
contact areas of the conical feelers, which maintain contact with the acrylic dome. Normal 
and shearing forces are calculated from integration and centroid displacement of the 
grayscale value derived from the conical feeler's contacts. 

Object-Handling Tasks Based on Active Tactile and Slippage Sensations 155 

To evaluate the present tactile sensor, we conducted a series of experiments using a y-z stage, 
rotational stages, and a force gauge. Although the relationship between the integrated 
grayscale value and normal force depended on the sensor's latitude on the hemispherical 
surface, it was easy to modify sensitivity based on the latitude. Sensitivity to normal and 
shearing forces was approximated with bi-linear curves. The results revealed that the 
relationship between the integrated grayscale value and normal force converges into a single 
curve despite the inclination of the applied force. This was also true for the relationship 
between centroid displacement and shearing force. Therefore, applied normal and shearing 
forces can be obtained independently from integrated grayscale values and centroid 
displacement, respectively. Also, the results for the present sensor had enough repeatability to 
confirm that the sensor is sufficiently sensitive to both normal and shearing forces. 
Next, a robotic hand was composed of two robotic fingers to indicate that tri-axial tactile 
data generated the trajectory of the robotic fingers. Since the three-axis tactile sensor can 
detect higher order information compared to the other tactile sensors, the robotic hand's 
behavior is determined on the basis of tri-axial tactile data. Not only tri-axial force 
distribution directly obtained from the tactile sensor but also the time derivative of shearing 
force distribution is used for the hand-control program. If grasping force measured from 
normal force distribution is lower than a threshold, grasping force is increased. The time 
derivative is defined as slippage; if slippage arises, grasping force is enhanced to prevent 
fatal slippage between the finger and object. In the verification test, the robotic hand twists 
on a bottle cap completely. Although input finger trajectories were a rectangle roughly 
decided to touch and turn the cap, a segment of the rectangle was changed from a straight 
line to a curved line to fit the cap contour. Therefore, higher order tactile information can 
reduce the complexity of the control program. 

We are continuing to develop the optical three-axis tactile sensor to enhance its capabilities 
such as sensing area, precision and sensible range of load. Furthermore, we will apply the 
hand to more practical tasks such as assemble-and-disassemble and peg-in-hole tasks in 
future work. 

5. References 

Borovac, B., Nagy, L., and Sabli, M., Contact Tasks Realization by sensing Contact Forces, 

Theory and Practice of Robots and Manipulators, Proc. of 11th CISM-IFToNN 

Symposium, Springer Wien New York, pp. 381-388, 1996. 
Chigusa, H., Makino, Y. and Shinoda, H., Large Area Sensor Skin Based on Two- 

Dimensional Signal Transmission Technology, Proc. World Haptics 2007, Mar., 

Tsukuba, Japan, pp. 151-156, 2007. 
Hackwood, S., Beni, G., Hornak, L. A., Wolfe, R., and Nelson, T. J., Torque-Sensitive Tactile 

Array for Robotics, Int. }. Robotics Res., Vol. 2-2, pp. 46-50, 1983. 
Hakozaki, M. and Shinoda, H., Digital Tactile Sensing Elements Communicating Through 

Conductive Skin Layers, Proc. of 2002 IEEE Int. Conf. On Robotics and Automation, 

pp. 3813-3817, 2002. 
Harmon, L. D, Automated Tactile Sensing, Int. ]. Robotics Res., Vol. 1, No.2, pp. 3-32, 1982. 
Hasegawa, Y., Shikida, M., Shimizu, T., Miyaji, T., Sakai, H., Sato, K., and Itoigawa, K., A 

Micromachined Active Tactile Sensor for Hardness Detection, Sensors and Actuators 

(A Physical), Vol. 114, Issue 2-3, pp. 141-146, 2004. 
Kamiyama, K., Vlack, K., Mizota, T., Kajimoto, H, Kawakami, N. and Tachi, S., Vision- 
Based Sensor for Real-Time Measuring of Surface Traction Fields, IEEE Computer 

Graphics and Applications, January/ February, pp. 68-75, 2005 

156 Robot Arms 

Kaneko, M., H. Maekawa, and K. Tame, Active Tactile Sensing by Robotic Fingers Based on 

Minimum-External-Sensor-Realization, Proc. of IEEE Int. Conf. on Robotics and 

Automation, pp. 1289-1294, 1992. 
Maekawa, H., Tanie, K., Komoriya, K., Kaneko M., Horiguchi, C, and Sugawara, T., 

Development of a Finger-shaped Tactile Sensor and Its Evaluation by Active Touch, 

Proc. of the 1992 IEEE Int. Conf on Robotics and Automation, pp. 1327-1334, 1992. 
Mott, H., Lee, M. H., and Nicholls, H. R., An Experimental Very-High-Resolution Tactile 

Sensor Array, Proc. 4th Int. Conf. On Robot Vision and Sensory Control, pp. 241-250, 

Nicholls, H. R. & Lee, M. H., A Survey of Robot Tactile Sensing Technology, Int. ]. Robotics 

Res., Vol. 8-3, pp. 3-30, 1989. 
Nicholls, H. R., Tactile Sensing Using an Optical Transduction Method, Traditional and Non- 
traditional Robot Sensors (Edited by T. C. Henderson), Springer- Verlag, pp. 83-99, 1990. 
Novak, J. L., Initial Design and Analysis of a Capacitive Sensor for Shear and Normal Force 

Measurement, Proc. of 1989 IEEE Int. Conf. On Robotic and Automation, pp. 137-145, 

Ohka, M., Mitsuya, Y., Takeuchi, S., Ishihara, H. and Kamekawa, O., A Three-axis Optical 

Tactile Sensor (FEM Contact Analyses and Sensing Experiments Using a Large- 
sized Tactile Sensor), Proc. of the 1995 IEEE Int. Conf. on Robotics and Automation, pp. 

Ohka, M., Mitsuya, Y., Matsunaga, Y., and Takeuchi, S., Sensing Characteristics of an 

Optical Three-axis Tactile Sensor Under Combined Loading, Robotica, vol. 22, pp. 

213-221, 2004. 
Ohka, M., Mitsuya, Y., Higashioka, I., and Kabeshita, H., An Experimental Optical Three- 
axis Tactile Sensor for Micro-robots, Robotica, vol. 23, pp. 457-465, 2005. 
Ohka, M, Kobayashi, H., Takata, J., and Mitsuya, An Experimental Optical Three-axis Tactile 

Sensor Featured with Hemispherical Surface, Journal of Advanced Mechanical Design, 

Systems, and Manufacturing, Vol. 2-5, pp. 860-873, 2008. 
Ohka, M., Robotic Tactile Sensors, Wiley Encyclopedia of Computer Science and Engineering, , 

pp. 2454 - 2461 , 2009. 
Ohka, M., Takata, J., Kobayashi, H, Suzuki, H, Morisawa, N, and Yussof, H. B., 60. 

Object Exploration and Manipulation Using a Robotic Finger Equipped with an 

Optical Three-axis Tactile Sensor, Robotica, vol. 27, pp. 763-770, 2009. 
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on Tri-axial Tactile Data for Cap Screwing Task, Proc. of IEEE Inter. Conf. on Robotic 

and Automation, pp. 883-888, 2009. 
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Using Pressure Conductive Rubber with Electrical-wires Stitched Method, IEEE 

Trans. Sensors, Vol.5-4, pp.589-596, 2004. 
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Device for Monitoring Skin Condition, Skin Research and Techonology, Vol. 9, pp. 

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Sensor Array, Robot Sensors Vol. 2: Tactile and Non-Vision, Kempston, UK: IFS 

(Pubs), pp. 189-198, 1986. 
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Function and Its Application to Detect Rotational Slip, Proc. of 1994 IEEE Int. Conf. 

On Robotics and Automation, pp. 3550-3557, 1994. 

Part 2 

3D Terrain Sensing System using Laser 
Range Finder with Arm-Type 

Movable Unit 

Toyomi Fujita and Yuya Kondo 

Tohoku Institute of Technology 

1. Introduction 

A 3D configuration and terrain sensing is a very important function for a tracked vehicle 
robot to give precise information as possible for operators and to move working field 
efficiently. A Laser Range Finder (LRF) is widely used for the 3D sensing because it can 
detect wide area fast and can obtain 3D information easily. Some 3D sensing systems with 
the LRF have been presented in earlier studies (Hashimoto et al., 2008) (Ueda et al., 2006) 
(Ohno & Tadokoro, 2005). In those measurement systems, multiple LRF sensors are installed 
in different directions (Poppinga et al., 2008), or a LRF is mounted on a rotatable unit 
(Nuchter et al., 2005) (Nemoto et al., 2007). Those kinds of system still have the following 

a. The system is going to be complex in data acquisition because of the use of multiple 
LRFs for the former case, 

b. It is difficult for both cases to do sensing more complex terrain such as valley, deep 
hole, or inside the gap because occlusions occur for such terrain in the sensing. 

In order to solve these problems, we propose a novel kind of sensing system using an 
arm-type sensor movable unit which is an application of robot arm. In this sensing 
system, a LRF is installed at the end of the arm-type movable unit. The LRF can change 
position and orientation in movable area of the arm unit and face at a right angle 
according to a variety of configuration. This system is therefore capable of avoiding 
occlusions for such a complex terrain and sense more accurately. A previous study (Sheh 
et al., 2007) have showed a similar sensing system in which a range imager has been used 
to construct a terrain model of stepfields; The range imager was, however, fixed at the end 
of a pole. Our proposed system is more flexible because the sensor can be actuated by the 
arm-type movable unit. 

We have designed and developed a prototype system of the arm-type sensor movable unit 
in addition to a tracked vehicle robot. In this chapter, Section 2 describes an overview of the 
developed tracked vehicle and sensing system as well as how to calculate 3D sensing 
position. Section 3 explains two major sensing methods in this system. Section 4 presents 
fundamental experiments which were employed to confirm a sensing ability of this system. 
Section 5 shows an example of 3D mapping for wide area by this system. Section 6 discusses 
these results. 


Robot Arms 

2. System overview 

The authors have designed and developed a prototype system of the arm-type movable 
unit. The unit has been mounted on a tracked vehicle robot with two crawlers that we have 
also developed. Fig. 1 shows the overview. The following sections describe each part of this 

2.1 Tracked vehicle 

We have developed a tracked vehicle robot toward rescue activities. Fig. 1 shows an 
overview of the robot system. The robot has two crawlers at the both sides. A crawler 
consists of rubber blocks, a chain, and three sprocket wheels. The rubber blocks are fixed on 
each attachment hole of the chain. One of the sprocket wheels is actuated by a DC motor to 
drive a crawler for each side. The size of the robot is 400[mm] (length) x 330 [mm] (width) x 
230 [mm] (height), when the sensor is descended on the upper surface of the robot. 

Laser Range Finder 

Arm-Type Movable Unit 

Fig. 1. System overview 

2.2 Arm-type sensor movable unit 

We have designed the arm-type sensor movable unit and developed a prototype system. 
This unit consists of two links having a length of 160[mm]. The links are connected by two 
servo motors as a joint in order to make the sensor horizontal orientation easily when 
folded. Another two joints are also attached to the both ends of the connecting links; one is 
connected to the sensor at the end and the other is mounted on the upper surface of the 
robot. The robot can lift the sensor up to a height of 340[mm] and change its position and 
orientation by rotating those joints. 

2.3 Sensors 

HOKUYO URG-04LX (Hokuyo Automatic Co. Ltd.) is used as the Laser Range Finder (LRF) 
in this system. This sensor can scan 240 degrees area and obtain distance data every 0.36 

3D Terrain Sensing System using Laser Range Finder with Arm-Type Movable Unit 161 

degree on a 2D plane. The robot is able to change the position and orientation of this sensor 
because it is equipped at the end of the arm-type movable unit. 

In addition, we have installed an acceleration sensor around three orthogonal axes to detect 
tilt angle of the robot body and to control the orientation of the LRF to be flat corresponding 
to the tilt angle. The use of this sensor enables the arm-type movable unit to change the 
height of the LRF with keeping its orientation. 

2.4 Control system 

The control system of this robot system consists of two embedded micro computers: Renesas 
SH-2/7045F and H8/3052F for controlling the main robot and the arm-type sensor movable 
unit respectively. A Windows/ XP host PC manages all controls of those units as well as 
scanned data of the sensor. The host PC sends movement commands to individual 
embedded micro computers for the robot and arm-type movable unit and request for sensor 
data acquisition to the sensor. The sensor can communicate directly with the host PC. All 
communications for those protocols are made by wireless serial communications using 
bluetooth-serial adapters: SENA Parani-SDIOO. 

2.5 Calculation of 3D sensing position 

In this system, the robot can obtain 3D sensing positions from distance data of the LRF. We 
gave coordinate systems to each joint of the arm-type unit and LRF as shown in Fig. 2. When 
the sensed distance by the LRF is d s at a scan angle S , the 3D measurement position vector X 
in the base coordinate system can be calculated by 

X I = °P P 2 P 3 P 4 P f X - 

where X s shows a position vector of sensed point in the LRF coordinate system: 


X s = ds (cos 0s , sin 0s , ) T (2) 

'P,+i (!=0,...,4) shows a homogeneous matrix that represents a transformation between two 
coordinate systems of joint-(i) and joint-(i+l) : 

,., - ft -) 

where 'R,+i shows a rotation matrix for the rotation angle 6/+i around yi axis, 

R , = 1 (4) 

cos 6 M sin n 


-sin# . cos 

for i- 0, . . .,4. ! T,+i shows a translation vector on the link from joint-(i) to joint-(i+l) the length 
of which is £ ,. : 

-T, +1 = (0 i) T (5) 


Robot Arms 

for i = 0, . . ., 4 ( ( = 0). O3 shows a 3 x 1 zero vector. 

The base position of the sensor is set to the position when the arms are folded: the joint 

angles d\, 02, 03, and 4 in Fig. 2 are 90, -90, -90, and 90 degrees respectively. 

Fig. 2. Coordinate systems 

3. Sensing method 

The mechanism of this system enables the LRF to change position and orientation to face at 
a right angle corresponding to a variety of configuration. For example, this sensing system is 
able to do sensing deep bottom area without occlusions as shown in Fig. 3. Because the 
occlusion can be avoided by this mechanism even for complex terrain, the robot can 
measure a 3D configuration such as valley, gap, upward or downward stairs more 
accurately than conventional 3D sensing system with the LRF. In addition, a robot can do 
sensing more safely by this method because the robot does not have to stand at close to the 
border. It is important when the robot needs to work in an unknown site such as disaster 

On the other hand, this arm-type movable unit can change the height of the LRF by keeping 
its orientation flat. In this way, 2D shape information in a horizontal plane is detected in 
each height with even distance. Consequently, the 3D shape of surrounding terrain can be 
obtained more efficiently by moving the LRF up vertically and keeping its orientation flat. 
We have installed acceleration sensors to detect tilt angle of the robot so that the robot 
performs this kind of sensing even when it is on uneven surface as shown in Fig. 4. 

3D Terrain Sensing System using Laser Range Finder with Arm-Type Movable Unit 



Arm-type sensor movable unit 

Fig. 3. Sensing of deep bottom area 

Arm-type sensor 
ovable unit 

Fig. 4. Sensing by moving the LRF up vertically 

We can switch these two kinds of sensing style, as shown in Fig. 3 and Fig. 4, depending on 
the kind of information that the robot needs to obtain. 

4. Experiments 

Several fundamental experiments were employed to confirm basic sensing ability of the 
sensing system for complex terrains: upward stairs, downward stairs, valley configuration, 
and side hole configuration under the robot. The results for these experiments showed that 
proposed system is useful for sensing and mapping of more complex terrain. 

4.1 Upward stairs 

We employed a measurement of upward stairs as an experiment for basic environment. Fig. 
5 shows an overview of the experimental environment and Fig. 6 shows its schematic 
diagram. The stairs are located 1100[mm] ahead of the robot. Each stair is 80[mm] in height 
and depth. The robot stayed at one position and the LRF sensor was lifted vertically by the 
arm- type unit from the upper surface of robot to the height of 340 [mm] with equal interval 


Robot Arms 

of 50[mm]. Each scanning of the sensor was performed for each height. The robot was tilted 

10 degrees to confirm the usefulness of the acceleration sensor in the robot. The robot 

detected its orientation by the sensor and controlled the height of the LRF according to the 


Fig. 7 shows the measurement result; almost same configuration to actual environment was 

obtained in this sensing system. 

Fig. 5. Overview of an experiment for measurement of upward stairs 

Fig. 6. Schematic diagram of experimental environment for measurement of upward stairs 

4.2 Downward stairs 

Fig. 8 shows an overview of the experimental setup and Fig. 9 shows its schematic diagram 
with reference points for 3D measurement of downward stairs. We picked some reference 
points from corner points for measurement error analysis. Each stair is 80[mm] in height 

3D Terrain Sensing System using Laser Range Finder with Arm-Type Movable Unit 




Fig. 7. Measured 3D shape of upward stairs 

and depth. In this experiment, the robot stayed at one position, 330[mm] away from the top 
stair, and moved the arm-unit so that the sensor was located over the downward stairs. The 
sensor angle was changed by rotating the angle 64, shown in Fig. 2, with the same position 
of the end of the arm-unit by keeping the angles 9i, 62, and 63. The rotation angle 64 was 
controlled remotely from degree to 60 degrees every 1.8 degrees. The scanning of the LRF 
was performed for each sensor angle. The sensing data were then accumulated to make 3D 

Fig. 8. Overview of an experiment for measurement of downward stairs 

Fig. 10 shows the measurement result. We can see almost same configuration to actual 
environment. The measurement positions for reference points are also denoted in the figure. 
The results show that accurate position can be sensed by this system. Table 1 shows actual 


Robot Arms 

and measured distance with error ratio values on the reference points. This result also 
confirms valid sensing of this system because error ratio values of the distance were within 
5.8% for all reference points. 

(330, -540, -80) 

(330, -540, -170) ^n 
(410,-540,-170) }k 
(410, -540, -250) 

(490, -540, -250) 

(330, 540, -80) 

C mm ] 

540, -170) 

, 540, -170) 

(410, 540, -250) 

J, 540, -250) 
(490, 540, -330) 

(570, 540, -330) 

Fig. 9. Schematic diagram of experimental environment for downward stairs with reference 

(301, -578, -92) 
(324, -576, -170) [ j V 

(400, -576, -174) 

(433, -578, -241) 
(503, -578, -234) 

(545, -570, -308) 


(610, 540, -285) 

Laser Range Finder 

Fig. 10. Measured 3D shape of downward stairs with measurement position values for 
reference points (unit: [mm]) 

3D Terrain Sensing System using Laser Range Finder with Arm-Type Movable Unit 


distance [mm] 





error ratio 







































































Table 1. Measured distances and error ratios on reference points for downward stairs 

4.3 Valley 

A valley configuration was set up as an experimental environment as shown in Fig. 11. Fig. 
12 shows its schematic diagram. The valley was 610[mm] deep and 320[mm] long. We gave 
reference points at each corner of the configuration to estimate actual error value on 
measurement points. 

Fig. 11. Overview of an experiment for measurement of a valley configuration (left: front 
view, right: side view) 

In the same way as previous experiment, the robot stayed at one position, 250 [mm] away 
from the border, and the sensor was located over the valley by the arm-unit. The sensor 
angle only was changed and the other joint angles of the arm were kept to fix sensor 
position. The rotation angle of the sensor, 64, was varied from degree to 90 degrees every 
1.8 degrees. Each scanning was performed for each sensor angle. 

Fig. 13 shows the measurement result. We can see very similar configuration to the actual 
valley. The measurement positions for reference points are also denoted in the figure. The 


Robot Arms 

(570,-290,-90) | 

(570, 290, -700) 

C mm ] 

(250, 290, -90) 

(250, 290, -700) 

X «- 

Fig. 12. Schematic diagram of experimental environment for a valley configuration with 
reference points 


-1 000 ' 

Laser Range Finder 

C mm ] 

(0, 0, 0) 

( 200, 297, -105) 

( 596, -296, -670) 

( 590, 270, -630) U j 

( 282, 260, -630) 



Fig. 13. Measured valley configuration with measurement position values for reference 
points (unit: [mm]) 

3D Terrain Sensing System using Laser Range Finder with Arm-Type Movable Unit 


position values show that accurate position can be sensed by this sensing system. Table 2 
shows actual and measured distance with error ratio values on the reference points. Even 
though the error ratio for the point e was higher, the most of the values are less than about 
5% for the other points. 

distance [mm] 





error ratio [%] 































Table 2. Measured distances and error ratios on reference points for a valley configuration 

4.4 Side hole under robot 

We employed an experiment of measurement for a side hole configuration under the robot. 
Fig. 14 shows an overview of the experiment and Fig. 15 shows a schematic diagram of its 
environment. The dimension of the hole was set to 880[mm] (width) x 400[mm] (height) x 
600[mm](depth). Eight reference points were given at each corner of the hole to estimate 
actual errors. 

Fig. 14. Overview of an experiment for measurement of a side hole configuration under the 

The robot stayed at one position as previous experiments and the sensor was located in front 
of the hole by the arm-unit. Each rotation angles of joints except for the last joint was fixed 
to keep the sensor position. The sensor angle, 64, was only varied from degree to 70 
degrees every 1.8 degrees. Each scanning was performed for each sensor angle. Fig. 16 
shows the measurement result. This result also showed almost same configuration to the 
actual environment. The measurement position values for reference points are also denoted 


Robot Arms 

in the figure. Table 3 shows actual and measured distance with error ratio values on the 
eight reference points. The error ratios demonstrated accurate sensing in this system; the 
maximum was 4.4% and average was 1.9% for all points. 

Fig. 15. Schematic diagram of experimental environment for a side hole configuration under 
the robot with reference points 

(-450, -396, -200) 



(120, -426, -131) 



Laser Range Finder || 

-300 ^^5SS^^'.^<^{ 



y / - ""^ i , V--.. >^ 

(158,-439,-450) | (-425,-400,-541) 

[ mm ] 

(-400, 450, -200) 




(168, 409, -140) 

(-400, 409, -524) 

(175, 436, -500) 

Fig. 16. Measured configuration of a side hole under the robot with measurement position 
values for reference points (unit: [mm]) 

3D Terrain Sensing System using Laser Range Finder with Arm-Type Movable Unit 


distance [mm] 





error ratio [%] 









































Table 3. Measured distances and error ratios on reference points for a side hole 
configuration under the robot 

5. 3D mapping 

A basic experiment of 3D mapping for wide area was employed by this sensing system. In 
the experiment, robot moved in a flat corridor shown in Fig. 17. The robot moved forward in 
the environment for every 40 [cm] distance and made a 3D sensing on each location. The 
robot obtained 3D data by moving the LRF vertically from the upper surface of the robot to 

Fig. 17. Experimental environment for 3D mapping 


Robot Arms 

the height of 340[mm] in every 68[mm] for respective scanning at each sensing location. In 
order to build 3D map, all sensing data at the sensing locations were combined using 
odometry information of the robot. We put additional several obstacles in the environment 
to estimate how this system can detect these shapes and positions. The obstacles are put in 
the areas labeled by Q and © as shown in Fig. 17. 

Fig. 18 shows the result of 3D mapping. This result shows valid 3D shapes of the environment 
including added obstacles within the appropriate height. The areas of the obstacles are 
denoted by ellipse with each label. The built data for each sensing location were described by 
individual different color. Note that this result clearly shows the top surface detection for each 
obstacle. This sensing can be made by the mechanism of this system. 

Fig. 19 shows the upper view of the built map in the left panel and actual map in the right 
panel. Obstacles were detected at almost correct location in the result. 

400 .. 

20D O ' . ■ 

J D M -- "" 

-'-'-'■. | 

4000 ^ 






-1000 -2( 


Fig. 18. Experimental result of 3D mapping 

6. Discussions 

We have employed fundamental experiments for sensing complex terrains: upward stairs, 
downward stairs, valley configuration, and side hole configuration under the robot. From 
Fig. 7, Fig. 10, Fig. 13, and Fig. 16, we can see that the almost same configuration was 
measured respectively. We therefore confirm that this sensing system has basic ability of 3D 
sensing and useful for more complex environment. 

The result of sensing for upward stairs, as shown in Fig. 7, provided that the sensing by 
lifting the LRF vertically with equal interval was effective for getting whole 3D shape in the 
sensing area. We confirmed that the acceleration sensor was useful for this kind of sensing. 
This sensing method is also able to avoid a problem on accumulation point in conventional 
method which uses a rotating mechanism. 

The result of sensing for downward stairs, as shown in Fig. 10 and Table 1, suggested that 
this system is possible to perform 3D mapping effectively even if the terrain has many 

3D Terrain Sensing System using Laser Range Finder with Arm-Type Movable Unit 







.... '•____ JJ 

L.-J i 

t : 

■ L L 

i i 

i 1 



i i 
1 1- 


4000 3000 


3000 2000 

2000 1000 ( 


Fig. 19. Upper view of built map (left) and actual environment (right) 

1000 o 









occlusions. The error ratio of distance was about 5% at a maximum. This error may be 
derived from mechanical errors of the unit in addition to original detection errors of the 
sensor device itself. It is necessary to develop the unit with mechanical stability. We 
however consider this error value is acceptable for a mapping for the purpose of movement 
or exploration by a tracked vehicle or a rescue robot. 

The 3D shape of measurement result for a valley terrain, as shown in Fig. 13, indicated 
another advantage of the proposed sensing method. This sensing system is able to do 
sensing deep bottom area without occlusions. In addition, a robot can do it safely by this 
method because the robot does not have to stand at close to the border. We consider that the 
error ratio of 7.6% for the reference point e, shown in Table 2, occurred because the position 
was acute angle for the sensor. This error could be improved if the sensor is located properly 
so that it can face to the right position to the point. This sensing system can correspond to 
variety of terrain because the arm-type sensor movable unit can provide a lot of positions 
and orientations of the sensor. 

The result of 3D measurement for a side hole under the robot also demonstrated further 
ability and strong advantage of the sensing system. Fig. 16 showed that this system enables 
us to obtain 3D information for such a shape which any conventional sensing system has 
never been able to measure. Moreover, the experimental result showed accurate sensing due 
to less error ratios, as shown in Table 3. This sensing system must be useful for 3D shape 
sensing specially in rough or rubble environments such as disaster area. 

The experimental results for 3D mapping described in Section 5 indicated that this robot 
system was capable of building 3D map in wide area using odometry information. Fig. 18 
showed almost actual shapes and positions of obstacles in the areas © and © . The sensing of 
top-surface of the obstacles also demonstrated one of advantages of this proposed system 
because such a sensing would be difficult for conventional method. Some errors however 
occurred in the far area from the beginning sensing location. We consider these errors may 
come from some odometry errors due to slip of tracks in the movement. More accurate 
mapping would be possible by solving this problem using external sensors with more 
sophisticated calculation method such as ICP (Nuchter et al., 2005) (Besl & Mckay, 2002). 

174 Robot Arms 

7. Conclusions 

This chapter proposed a novel 3D sensing system using arm-type sensor movable unit as an 
application of robot arm. This sensing system is able to obtain 3D configuration for complex 
environment such as valley which is difficult to get correct information by conventional 
methods. The experimental results showed that our method is also useful for safe 3D 
sensing in such a complex environment. This system is therefore adequate to get more 
information about 3D environment with respect to not only Laser Range Finder but also 
other sensors. 

8. References 

Besl, P. J. & Mckay, N. D. (1999). A method for registration of 3-d shapes, IEEE Transactions 

on Pattern Analysis and Machine Intelligence, 14(2), pp.239-256, August 2002 
Hashimoto, M.; Matsui, Y. & Takahashi, K. (2008). Moving-object tracking with in-vehicle 

multi-laser range sensors, Journal of Robotics and Mechatronics, Vol.20, No.3, pp. 367- 

Hokuyo Automatic Co., Ltd., Available from 
Iocchi, L.; Pellegrini, S. & Tipaldi, G. (2007) Building multi-level planar maps integrating 

LRF, stereo vision and IMU sensors, Proceedings of IEEE International Workshop on 

Safety, Security and Rescue Robotics 2007 
Nemoto, Z.; Takemura, H. & Mizoguchi, H. (2007). Development of Small-sized Omni- 
directional Laser Range Scanner and Its Application to 3D Background Difference, 

Proceedings of IEEE 33rd Annual Conference Industrial Electronics Society (IECON 2007), 

pp. 2284-2289 
Nuchter, A.; Lingemann, K. & Hertzberg, J. (2005). Mapping of rescue environments with 

kurt3d, Proceedings of IEEE International Workshop on Safety, Security and Rescue 

Robotics 2005, pp. 158-163 
Ohno, K. & Tadokoro, S. (2005). Dense 3D map building based on LRF data and color image 

fusion, Proceedings of 2005 IEEE/RS] International Conference on Intelligent Robots and 

Systems, 2005. (IROS 2005), pp. 2792-2797 
Poppinga, J.; Birk, A. & Pathak, K. (2008). Hough based terrain classification for realtime 

detection of drivable ground, Journal of Field Robotics, Vol. 25, No. (1-2), pp. 67-88 
Sheh, R.; Kadous, M.; Sammut, C. & Hengst B. (2007) Extracting terrain features from range 

images for autonomous random stepfield traversal, Proceedings of IEEE International 

Workshop on Safety, Security and Rescue Robotics 2007 
Ueda, T.; Kawata, H.; Tomizawa, T.; Ohya, A. & Yuta, S. (2006). Mobile SOKUIKI Sensor 

System-Accurate Range Data Mapping System with Sensor Motion, Proceedings of 

the 2006 International Conference on Autonomous Robots and Agents, pp. 309-304, 

December 2006 


Design of a Bio-Inspired 3D Orientation 

Coordinate System and Application in 

Robotised Tele-Sonography 

Courreges Fabien 

Universite de Limoges 

1. Introduction 

In designing a dedicated robotised telemanipulation system, the first approach should be to 
analyse the task targeted by such a teleoperation system. This analysis is essential to obtain 
cues for the robot mechanical, human-system interface, and the teleoperation control 
designs. In this chapter we will focus mainly on orientation-based tasks. That is to say, tasks 
consisting in orienting the remote robot's end-effector in 3D space. One major application 
considered here is the robotised telesonography medical examination. In this application a 
medical expert can pilot the orientation of an ultrasound (US) probe to scan a remote patient 
in real-time by means of a robot arm handling the probe. We have focused our approach on 
the telesonography application in order to analyse the task of setting the orientation of an 
object in space around a fixed centre of motion. For this analysis, several points of view have 
been taken into account: perceptual and psychophysical analysis, experimental tracking of 
the orientation applied by the hand, and the analysis of medical sonography practices 
recommendations. From these studies we have developed a new frame of three angles 
enabling the definition of an orientation. Indeed to define an orientation in 3D space (also 
said attitude), a representation system with at least three degrees of freedom or coordinates 
is required. This new frame was designed in such a way that its three degrees of freedom 
are decoupled with respect to the human psychophysical abilities. That is to say that each 
angle of this frame can be easily assessed and varied by hand without changing the value of 
the other angles of the frame. Hence the so-called hand-eye coordination can be improved 
with such a system of representation for interfaces design. We name this new system "H- 
angles" where the H recalls the Human-centred design of this system. We will also show that 
standard rotation coordinate systems such as the Euler and quaternions systems cannot 
offer such properties. Thereby our new frame of angles can lead to several applications in 
the field of telerobotics. Indeed we will provide cues indicating that the considerations used 
to design our new frame of angles are not limited to the context of the telesonography 
application. This chapter is devoted to present the foundations which led to the design of a 
new bio-inspired frame of angles for attitude description but we will also present one major 
application of this frame of angles such as the design of a mouse-based teleoperation 
interface to pilot the 3D orientation of the remote robot's hand-effector. This main 
application has arisen from the fact that the task of orienting an object in 3D space by means 

176 Robot Arms 

of a computing system requires the use of specific man-machine interfaces to be achieved 
fast and easily. Such interfaces often require the use of sophisticated and costly technologies 
to sense the orientation of the user's hand handling the interface. The fields of activity 
concerned are not limited to robot telemanipulation; we also find the computer-aided- 
design, the interaction with virtual reality scenes, and teleoperation of manufacturing 
machines. When the targeted applications are related to the welfare of the whole society, 
such as medical applications, the cost and availability of the system raises the problem of 
fair access to those high-tech devices, which is an ethical issue. The proposed system of 
angles enables the development of methods to perform advanced telemanipulation 
orientation tasks of a robot arm by means of low-cost interfaces and infrastructures (except 
probably the robot). Thus the most expensive element in such a teleoperation scheme will 
remain the robot. But for a networked-robot accessible to multiple users, we can imagine 
that the bundle of its cost could be divided up among the several users. In this chapter we 
will show a new method for using a standard wheeled IT mouse to pilot the 3D orientation 
of a robot's end-effector in an ergonomic fashion by means of the H-angles. In the context of 
the telesonography application, we will show how to use the aforementioned method to 
teleoperate the orientation of a remote medical ultrasound scanning robot with a mouse. 
The remaining of the chapter will be structured as follows: the second section coming next 
will provide our analysis in three parts to derive some cues and specifications for the design 
of a new frame of angles adapted to human psychophysical abilities. The third section is 
dedicated to our approach relying on the preceding cues to derive a new frame of angles for 
attitude description. It will also be shown that the new proposed system exhibits a much 
stronger improvement of decorrelation among its degrees of freedom (DOF) compared to 
the ZXZ Euler system. An analysis of the singularities of the new system is also proposed. 
The fourth section will address our first application of the new frame of angles that is to say 
the setting of 3D rotations with the IT mouse; this section will start with a review of the 
state-of-the-art techniques in the field and will end with experimental psychophysical 
results given in the context of the telesonography application. We show the large superiority 
of our frame of angles compared to the standard ZXZ Euler system. Last section concludes 
with an overview of further applications and research opportunities. 

2. Design and analysis of a psychophysical^ adapted frame of angles for 
orientation description 

The sensorimotor process of a human adult for achieving the task of orienting a rod by hand 
can be modelled according to the following simplified scheme from perception to action: 
This figure is a simplified scheme and may be incomplete but it reflects the present common 
trend of thought in the field of neuroscience concerning the information encoding and 
transformation from perception to action. 

As it is reported in the neuroscience literature, the human brain can resort to several 
reference frames for perceptual modalities and action planning (Desmurget et al., 1998). 
Moreover, according to Goodale (Goodale et al., 1996), Human separable visual systems for 
perception and action imply that the structure of an object in a perceptual space may not be 
the same one in an interactive space which implies some coordinates frames 
transformations. This figure proposes the integration of multimodal information in the 
sensorimotor cortex to generate a movement plan into one common reference frame. This 

Design of a Bio-Inspired 3D Orientation Coordinate 
System and Application in Robotised Tele-Sonography 


Variation updating in 
encoding visual frame 



encoding . ,- c 

^ haptic frame 


Kinesthetic Variation U pd; 
proprioception encoding propriocept 

Fusion and 



mental frame 


planning in 
mental frame 

a reference 


11 or system 

T.rget p 

encoding in 
visual frame 

Target vial 


"""" \ 

encoding in 
haptic frame 





Target / 

encoding in 



Fig. 1. Simplified Human perception to action process 

concept comes from neurophysiological evidences reported by Cohen and Andersen (Y.E. 
Cohen & Andersen, 2002). Some research works (Paillard, 1987) also report the existence of 
two parallel information processing channels: cognitive and sensorimotor, which is reflected 
in figure 1. The idea of perception as action-dependent has been particularly emphasized by 
motor theories of perception, i.e. those approaches claiming that perceptual content depends 
in an essential way on the joint contribution of sensory and motor determinations (Sheerer, 
1984). The theory underlies that action and perception are not independent cognitive 
domains and that perception is constitutively shaped by action. This idea is accounted in 
figure 1 by considering that motor variations are programmed in several frames of reference 
associated with each perceptual channel. Likewise, an inverse kinematics model learned by 
trials and errors in the infancy has been shown to be implemented by the central nervous 
system for the motor control (Miall & Wolpert, 1996). As depicted by figure 1, the task of 
handling a rod and making it rotate in space about a fixed centre of motion involves three 
perceptual modalities: visual, haptic, and kinaesthetic proprioception. The meaning of 
visual perception is unambiguous and this modality is essential for a precise motor control 
(Norman, 2002). The haptic modality involved here should be understood as "active touch" 
as defined by Gentaz (Gentaz et al., 2008): "Haptic perception (or active touch) results from the 
stimulation of the mechanoreceptors in skin, muscles, tendons and joints generated by the manual 
exploration of an object in space... Haptic perception allows us, for example, to identify an object, or 
one of its features like its size, shape or weight, the position of its handle or the material of which it is 
made. A fundamental characteristic of the haptic system is that it depends on contact" . Haptics is a 
perceptual system, mediated by two afferent subsystems, cutaneous and kinaesthetic. Hence 
this perceptual system depends on spatio-temporal integration of the kinesthetics and tactile 
inputs to build a representation of the stimulus that most typically involves active manual 
exploration. The purely kinaesthetic proprioceptive perceptual system is a neurosensorial 
system providing the ability to sense kinaesthetic information pertaining to stimuli 
originating from within the body itself even if the subject is blindfolded. More precisely 
kinaesthetic proprioception is the subconscious sensation of body and limb movement with 
required effort along with unconscious perception of spatial orientation and position of 
body and limbs in relation to each other. Information of this perceptual system is obtained 
from non-visual and non-tactile sensory input such as muscle spindles and joint capsules or 
the sensory receptors activated during muscular activity and also the somato-vestibular 
system. Our aim in this section is to present our methodology to design an orientation frame 
comprehensible for both perception and action in performing a task of 3D orientation. We 
want a new frame of parameters whose values can be easily assessed from a perceived 
orientation, and easily set in orienting a rod by hand. Our approach was to seek for a system 
exhibiting three independent and decoupled coordinates when humans perform a planned 
trajectory in rotating a rod about a fixed centre of motion. For that purpose we have carried 

178 Robot Arms 

out an analysis in three parts given below. Before tackling this analysis we will provide 
some background and notations on orientation coordinate systems such as the quaternions 
and Euler angles. 

2.1 Background on standard orientation coordinate system 

We give in this section an insight on the most frequently used orientation representation 
systems in the field of human-machine interaction, namely quaternions and Euler systems. 

2.1.1 The quaternions 

The quaternions were discovered by Hamilton (Hamilton, 1843) who intended to extend the 
properties of the complex numbers to ease the description of rotations in 3D. A quaternion is 
a 4-tuple of real numbers related to the rotation angle and the rotation axis coordinates. 
Quaternions are free of mathematical singularities and enable simple and computationally 
efficient implementations for well-conditioned numerical algorithm to solve orientation 
problems. Quaternions constitute a strong formalization tool however it is not a so efficient 
mean to perform precise mental rotations. Quaternions find many applications especially in 
the field of computer graphics where they are convenient for animating rotation trajectories 
because they offer the possibility to parameterize smooth interpolation curves in SO(3) (the 
group of rotations in 3D space) (Shoemake, 1985). 

2.1.2 Euler angles 

Euler angles are intuitive to interpret and visualize and that's why that they are still widely 
used today. Such a factorization of the orientation aids in analyzing and describing the 
different postures of the human body. An important problem with using Euler angles is due 
to an apparent strength, it is a minimal representation (three numbers for three degrees of 
freedom). However all minimal parameterizations of SO(3) suffer from a coordinates 
singularity which results in a loss of a rotational degree of freedom in the representation 
also known as "gimbal lock". Any interpolation scheme based on treating the angles as a 
vector and using the convex sum will behave badly due to the inherent coupling that exists 
in the Euler angles near the singularity. Euler angles represent an orientation as a series of 
three sequential rotations from an initial frame. Each rotation is defined by an angle and a 
single axis of rotation chosen among the axes of the previously transformed frame. 
Consequently there are as many as twelve different sequences and each defines a different 
set of Euler angles. The naming of a set of Euler angles consists in giving the sequence of 
three successive rotation axes. For instance XYZ, ZXZ,... The sequences where each axis 
appears once and only once such as XYZ, XZY, YXZ, YZX, ZXY, ZYX are also named 
Cardan angles. In particular the angles of the sequence XYZ are also named roll (rotation 
about the x-axis), pitch (new y-axis) and yaw (new z-axis). The six remaining sequences are 
called proper Euler angles. In the present work it will be given a particular focus on the 
sequence ZXZ whose corresponding angles constitute the three-tuple noted (\|/,6,q>). Angle \\i 
is called precession (first rotation about Z-axis), 6 is the nutation (rotation about the new X- 
axis) and cp is named self-rotation (last rotation about the new Z-axis). 

2.2 Neuroscience literature review 

This section is dedicated to providing a comprehensive review of the neuroscience literature 
related to our purpose of identifying the 3D orientation encoding in the perceptual and 

Design of a Bio-Inspired 3D Orientation Coordinate 

System and Application in Robotised Tele-Sonography 179 

sensory-motor systems. As indicated in figure 1 we have to investigate the orientation 
encoding in the following three perceptual systems: visual, haptic and proprioceptive. But 
we also have to consider the cognitive and the motor levels since Wang (Wang et al. 1998) 
argue that an interface design should not only accommodate the perceptual structure of the 
task and control structure of the input device, but also the structure of motor control 
systems. In the following the perceptual abilities (vision, haptic, proprioception) along with 
the mental cognition and motor control system will be indifferently denoted as modalities. 
We shall at first identify a common reference frame for all the modalities. 

2.2.1 Common cross-modalities reference frame for orientations 

As indicated previously, the reference frame may vary from one perceptual modality to 
another (Desmurget et al. 1998). Furthermore numerous studies have reported that for each 
modality its reference frame can be plastic and adapted to the task to be performed leading 
to conclude that several encodings of the same object coexist simultaneously. Importantly, 
the framework of multiple interacting reference frames is considered to be a general 
principle in the way the brain transforms combines and compares spatial representations 
(Y.E. Cohen & Andersen, 2002). In particular the reference frame can swap to be either 
egocentric (intrinsic or attached to the body) or allocentric (extrinsic to the body). This 
duality has been observed for the haptic modality (Volcic & Kappers 2008), the visual 
perception (Gentaz & Ballaz, 2000), the kinaesthetic proprioception (Darling & Hondzinski, 
1999), the mental representation (Burgess, 2006) and the motor planning (Fisher et al., 2007; 
Soechting & Flanders, 1995). It is now a common opinion that both egocentric and 
allocentric reference frames coexist to locate the position and orientation of a target. In most 
of the research work it was found that whatever the modality, when the studied subjects 
have a natural vertical stance, the allocentric reference frame is gravitational or geocentric. It 
means that one axis of this allocentric reference frame is aligned with the gravitational 
vertical which is a strong reference in human sensorimotor capability (Darling et al. 2008). 
The allocentric reference frame seems to be common to each modality whereas this is not the 
case for the egocentric frame. It was also found for each modality that because of the so 
called "oblique effect" phenomenon the 3D reference frame forms an orthogonal trihedron. 
On a wide variety of tasks, when the test stimuli are oriented obliquely humans perform 
more poorly than when oriented in an horizontal or vertical direction. This anisotropic 
performance has been termed the "oblique effect" (Essock, 1980). This phenomenon was 
extensively studied in the case of visual perception (Cecala & Garner, 1986; Gentaz & 
Tschopp, 2002) and was brought to light also in the 3D case (Aznar-casanova et al. 2008). 
The review from Gentaz (Gentaz et al., 2008) suggests the presence of an oblique effect also 
in the haptic system and somato-vestibular system (Van Hof & Lagers-van Haselen, 1994) 
and the haptic processing of 3D orientations is clearly anisotropic as in 2D. 
In the experiments reported by Gentaz the haptic oblique effect is observable in 3D when 
considering a plane-by-plane analysis, where the orientation of the horizontal and vertical 
axes in the frontal and sagittal planes, as well as the lateral and sagittal axes in the 
horizontal plane, are more accurately reproduced than the diagonal orientations even in the 
absence of any planar structure during the orientation reproduction phase. The oblique 
effect is also present at the cognitive level (Olson & Hildyard 1977) and is termed "oblique 
effect of class 2" (Essock, 1980). The same phenomenon has been reported to occur in the 
kinesthetic perceptual system (Baud-Bovy & Viviani, 2004) and for the motor control 
(Smyrnis et al., 2007). According to Gentaz (Gentaz, 2005) the vertical axis is privileged 


Robot Arms 

Frontal Plane 

Transverse Plane 


reference frame 

Fig. 2. Body planes and allocentric reference frame (picture modified from an initial public 
domain image of the body planes). 

because it gives the gravitation direction and the horizontal axis is also privileged because it 
corresponds to the visual horizon. The combination of these two axes forms the frontal 
plane. A third axis is necessary to complete the reference frame and we will follow Baud- 
Bovy and Gentaz (Baud-Bovy & Gentaz, 2006a) who argue that the orientation is internally 
coded with respect to the sagittal and frontal planes. The third axis in the sagittal plane 
gives the gaze direction when the body is in straight vertical position (see figure 2). It should 
also be noticed that when the body is in normal vertical position, the allocentric and 
egocentric frames of most of the modalities are congruent. From now on, as it was found to 
be common to all modalities, it will be considered that the orientations in space are given 
with respect to the allocentric reference frame as described in figure 2. 

2.2.2 Common cross-modalities orientation coordinate system 

From (Howard, 1982) the orientation of a line in 2D should be coded with an angle with 
respect to a reference axis in the visual system. When considering the orientation of a rod in 
3D space, two independents parameters at least are necessary to define an orientation and it 
seems from Howard that angular parameters are psychophysically preferred. It can be 
suggested from the analysis in the previous section about the common allocentric reference 
frame that the orientation encoding system should be spherical. For instance, the set of 
angles elevation-azimuth could well be adapted to encode the orientation of a rod in the 
allocentric reference frame of figure 2. Indeed the vertical axis constitutes a reference for the 
elevation angle and the azimuth angle can be seen as a proximity indicator of an oriented 
handled rod with respect to the sagittal and frontal planes. It should be noticed that the sets 
of spherical angles can carry different names but all systems made up of two independent 
spherical angles are isomorphic. We find for instance for the first spherical angle the 
naming: elevation, nutation, pitch,... and for the second angle : precession, yaw, azimuth,... 
Soechting and Ross (Soechting & Ross, 1984) have early demonstrated psychophysically that 
the spherical system of angles elevation-yaw, is preferred in static conditions for the 
kinaesthetic proprioceptive perception of the arm orientation. Soechting et al. have 
concluded that the same coordinate system is also utilized in dynamic conditions (Soechting 

Design of a Bio-Inspired 3D Orientation Coordinate 

System and Application in Robotised Tele-Sonography 181 

et al. 1986). According to Darling and Miller (Darling & Miller, 1995), perceived orientations 
of the forearm in the kinaesthetic proprioception modality are preferably coded in spherical 
coordinates (elevation-yaw) with respect to an ego-centric body-centered reference frame. 
This frame coincides with the allocentric gravitational frame when the body trunk is in 
natural vertical position. The spherical system in orientation encoding is also supported by 
Baud-Bovy and Gentaz for the haptic perceptual system (Baud-Bovy & Gentaz, 2006b). 
Another interesting study in the context of the ultrasound scanning application is about 
visual perception of the orientation of a plane surface in a 3D space. Gibson (Gibson, 1950) 
has early proposed that the visual orientation of a surface in space is internally coded in 
spherical slant-tilt form, which was supported by Stevens psychophysical experiments 
(Stevens, 1983). The slant-tilt angles system is a spherical orientation encoding of the vector 
normal to the plane. This angles system is exactly the same as the elevation-azimuth system. 
From the previous discussion it can be stated that the orientation of a rod in 3D space is 
coded in spherical coordinates made up of two angles. But the orientation of a complete 
frame of three axes requires at least three parameters. For the consistency of the coordinate 
system the third parameter should preferably be an angle. To our knowledge very few 
psychophysical or neurophysiological studies have been carried out to identify a full set of 
three angles, coding the orientation of a frame in space. In the proprioceptive kinaesthetic 
context, Darling and Gilchrist (Darling & Gilchrist, 1991) confirm the finding of Soechting 
and Ross (Soechting & Ross, 1984) that the angles elevation and yaw are parts of the 
preferred DOF system for hand orientation. They also suggest from their experimental 
results that the roll angle in the ZXY Cardan system could constitute the third preferred 
DOF to define a complete orientation of the hand. This suggestion was contradicted by 
Baud-Bovy and Viviani (Baud-Bovy & Viviani, 1998) who have shown that the last angle in 
the ZXY Cardan system is strongly correlated with both first angles of that system and also 
with the reaching length. This result lets think that the six sets of Cardan angles are 
improper to code the orientation of a frame in a biomimetic way. 

2.2.3 Discussion for orientation coding system design 

This literature review enables to establish that a psychophysically and sensorimotor adapted 
coordinate system to encode the orientation of a rod in 3D space should be made-up of a set 
of two spherical angles with respect to an allocentric gravitational reference frame. As a 
matter of fact the quaternions of Hamilton whereas elegant and efficient in interpolating 
orientations doesn't seem to be the most appropriate system of orientation coding to fit with 
the psychophysical human abilities. In return even the most recent researches in the field are 
unable to identify a third necessary degree of freedom to define completely the orientation 
of a frame of three axes. This failure is probably due to the fact that this third DOF may be 
dependent on the task to perform and the kinematics postures of the acting arm and wrist 
during this task. Indeed the singularity arising in a minimal-coding system may be 
incompatible with the task to perform. For the task of handling a rod, a natural axis is given 
by the direction of the rod itself. Hence a spherical coordinate system such as (nutation, 
precession) should be used to code the orientation of the rod. Concerning the singularities it 
should be noticed that when orienting a rod the spherical coordinate system exhibits 
intrinsic singularities. Indeed when the nutation reaches or re radians, the precession angle 
is undetermined which may lead to discontinuities in this angle. However, since there seems 
to be a consensus in favour of the spherical coordinate system in the field of the 
neurosciences it should be considered that those singularities truly reflect the human 


Robot Arms 

functioning mode and a biomimetic 3D orientation coding coordinate system should also 
probably exhibit such kind of singularity. 

The ultrasound plane 

(a) (b) (c) 

Fig. 3. (a) Ultrasound plane generated by the transducer and (b) corresponding generated 
image during a medical ultrasound examination (computer generated image). In (c) we have 
a real ultrasound slice of the hepativ vein. 

When considering the application of ultrasound scanning, the rod is in fact the transducer 
generating an ultrasound (US) plane (figure 3) and a 3-axis frame should be attached to the 
US-plane to define its 3D orientation in space. Indeed the full 3D orientation description is 
important in the case of an ultrasound plane since the notions of right and left of the plane 
have a meaning in such an application: when the transducer is rotated around its own axis 
with an angle of n radians, it generates an US-plane which geometrically speaking remains 
the same plane in space, but the image obtained does not remain the same, right and left are 
inverted. It will be considered in the remaining of this chapter that the axes of the frame 
attached to the plane are arranged as given in figure 3. Axis Z is chosen to correspond with 
the longitudinal axis of the transducer. Among the Euler angles systems excluding the 
Cardan systems, only the sets ZXZ and ZYZ can offer spherical angles to code the direction 
of vector Z. Those two sets are perfectly equivalent and only the ZXZ system will draw our 
attention in the forthcoming sections. Next section will provide a deeper experimental 
analysis of this system with respect to the sonography application. 

2.3 Experimental correlation analysis 

This section's aim is to study the coupling within the angles of the ZXZ Euler system 
defining the orientation of the US plane when a medical expert performs a sonography 
examination on a real patient. This frame is preferably used in the robotized tele- 
echography context (Courreges et al. 2005; Gourdon et al. 1999; Vilchis et al. 2003) because it 
was found to be the one among existing standard frames that best suits the required 
mobilities during an US examination according to medical specialists (Gourdon et. al 1999). 
It is recalled that the DOF of the ZXZ Euler system is the triplet of angles: (ijj, 0, cp), where 14/ 
is the precession, is the nutation and cp the self-rotation. We have set-up an experimental 
protocol in order to assess the dependencies of the degrees of freedom of the ZXZ Euler 
system and analyze the task to be performed by a medical tele-sonography robot. For that 
purpose we have captured the 6 DOFs movements of a real US specialist performing an 
abdominal examination of a healthy patient. The acquisition duration is about 5 minutes. 
During this examination the ultrasound (US) probe trajectories have been captured and 
recorded using a 6D magnetic localization sensor « Flock Of Bird » settled on the US probe 

Design of a Bio-Inspired 3D Orientation Coordinate 
System and Application in Robotised Tele-Sonography 


(Fig. 6). This kind of examination is frequently performed in routine and especially in 
emergency situations. The trajectories applied to the US probe by any expert would be 
roughly the same since these gestures come from the learning of recommended medical 
practices and not only from individual experience and is also subject to the human hand 
kinematics limitations (Tempkin, 2008). To better identify the correlation between angles 
and to be independent of the angles range and rollover we have considered the angles 

2.3.1 Correlation in the angles 

dcp/dt versus dw/dt m c = 0.99771 

<P 15 
























-15 -10 -5 5 10 15 . 20 

V (rad/s) 

(a) (b) 

Fig. 4. (a) Phase plot of cp versus 14/ and (b) correlation measures m c 

To emphasize the dependencies among the Euler angles for this kind of application, we 
have analyzed the phase plots of each angle derivative versus the other ones (Courreges et 
al. 2008b). We can easily obtain a correlation measure by considering the absolute value of 
the Pearson correlation coefficient (J. Cohen et al., 2002). Let us name this measure m c which 
is null for uncorrelated signals and is equal to 1 when the signals are linearly dependent. 
Figure 4b reports the correlation measures. From the plots obtained and correlation 

measures one can conclude that ljr and are uncorrelated, <p and are also uncorrelated, 

but \\s and (j> are strongly correlated (see also figure 4a). Consequently it is clear that the 

ZXZ Euler system is not perfectly suited for this application, as it can't provide decoupled 
DOF to describe the US scanning task. 

2.3.2 Data analysis 

These experimental data show that the spherical coordinates (i|r, 9) are uncorrelated DOF 
which is in agreement with the previous neuroscience literature review. The previous data 
also clearly reveal that the ZXZ Euler system exhibits a strong correlation between the 
precession and self rotation angles. In other words applying a variation on angle 14/ should 
induce a near proportional variation on angle <p according to figure 4(a). Since this 
proportionality applies whatever the value of 9, one can notice that the correlation of the 
angles is not related to the singularity of this Euler system (when 6=0). Thereby we conclude 
that the standard ZXZ Euler system is not the most appropriate system to represent the 
human privileged rotations directions when handling a rod. This analysis shows the need 

184 Robot Arms 

for the definition of a new non standard frame capable of providing decoupled DOF for this 
kind of task. Since according to figure 4, 14/ and (p angles are strongly correlated, a principal 
component analysis (PCA) (Joliffe, 2002) of the phase plots of the moves expressed in the 
ZXZ Euler system should provide us with decorrelated DOF. Indeed we can define a new 
coordinate system by using the Karhunen-Loeve transform (Loeve, 1978) which provides a 
very good decorrelation of the DOF. Let us name (a,9,p) this DOF triplet. According to the 
PCA, 9 is the same as the Euler nutation. Variables a and p are linear combination of the 
Euler angles 14/ and q> (see equation 1). Whereas this transformation is simple, this PCA 
based system doesn't provide meaningful variables: a and p are not intuitive for the hand- 
eye coordination. Moreover this transformation is optimal only for the particular conditions 
chosen for this experiment and may not be appropriate in other circumstances. 

2.4 Sonography practice analysis 

To obtain enough information to build a complete orientation coordinate system we studied 
the practice of sonography. More specifically we have analysed the way a 3D rotation is 
decomposed into simpler moves for pedagogical purpose in teaching the technique of 
medical US scanning. An US transducer works by generating a planar wave of ultrasounds. 
Waves reflected by the tissues are measured by the probe along with their time of flight, 
which enables to build a map of the density of the tissues (fig. 3c). Hence a medical expert 
has to think to rotate a plane in a 3D space to visualize the desired slice of the patient's 
body. In fact sonographers are used to describe their scan orientation by reference to three 
basis rotations (Tempkin, 2008; Block, 2004): probe angulation, probe rocking (fig.5) and self 
rotation. And in standard medical practice the examination is executed in two phases 
combining these three basis rotations: first, choosing an initial incidence for the ultrasound 
plane combining probe angulation and probe rocking so as to perform a narrow sweep of 
the scanned organ. This first move is intended to grossly identify lesions or cysts. Second 
phase consists in rotating the US plane around the probe axis so as to identify small 
structures as tumors or traumas and precisely locate their extent. A bio-inspired orientation 
frame should exhibit this same combination of movements. Consequently it was found that 
the professional field of medical sonography gives practical guidelines to maneuver the 
orientation of a probe in 3D space. Whereas the conclusions of this analysis are related to the 
specific field of sonography it is interesting to notice that the pragmatical rules for this task 
are consistent with the previous neuroscience conclusions. Indeed the recommended 
movements of probe angulation and probe rocking correspond exactly to the plane slant-tilt 
rotations as indicated in section 2.2.1. Moreover this analysis provides a complete and 
intuitive combination of hand movements to set the full 3D orientation of a frame in space 
since this combination was practiced and taught for ages in the field of sonography. The 
next section takes advantage of this analysis to propose a new frame of orientation 
description with a set of three angles satisfying the human preferences when someone 
performs an intentional orientation tracking. It is reasonable to think that this new frame 
could be satisfying not only for the fields of sonography-related applications but also for 
any other tasks implying the rotations of a rod about a fixed centre of motion. 

Design of a Bio-Inspired 3D Orientation Coordinate 
System and Application in Robotised Tele-Sonography 


' — US probe 

US plane 

(a) (b) 

Fig. 5. Two basic moves in medical US scanning, (a) probe angulation for organ sweeping 
(in this illustration, moves of the US probe remain in the sagittal plane), (b) probe rocking 
used to extend the scanning plane. 

3. H-angles, a new attitude coordinate system 

We will develop here our methodology to design a new frame of angles satisfying the 
criteria of the previous analyses. We call this frame H-angles. We will provide the 
transformations from the H-angles to the rotation matrix and inversely from the rotation 
matrix to the H-angles. We will also conduct an exhaustive analysis of the singularities of 
the rotation matrix. This section will be concluded with the excellent decorrelation results 
brought by our new system compared to the Euler system. Notice that in the following the 
angles unit should be understood in radians. 

3.1 Rotations combinations 

From previous analysis we propose a new frame of angles which we name H-angles and 
denoted as (i|/n, d m cp n ) parameterising an orientation obtained by a sequence of two 
consecutives rotations as for medical practice. Let's give some notations: let Ro = (O, XO, YO, 
ZO) be the fixed main reference frame with centre O, axis (XO, YO, ZO) and basis Bo = 

(x ,y ,Z ). The basis obtained by the first transform on basis Bo is denoted by 

Bi = (x 1 ,y 1 ,Z i ). The framework with basis Bi and origin O is noted Rj. The first movement 
is a complex rotation. According to the previous conventions the moving vector z gives the 
direction of the handled rod and vector x is normal to the moving plane corresponding to 
the US plane in sonography. This first rotation has two main functions: 

• defining vector z 1 by its nutation n e[0; n] and precession i|/ n s]- n; n], which is 
consistent with the neuroscience requirements depicted in §2.2.1; 

• forcing vector y 1 to stay in the plane ( z 1 O y ) so as to constrain the first move to be 
only a combination of probe angulation and probe rocking as for medical practice 
(§2.4). This constraint implies x, • y = 0. 


Robot Arms 

Given the previous constraints the orientation of basis Bi is not totally determined and the 
sign of the dot product x, . x must be defined according to the kind of application. In order 
to obtain a transformation with the minimum rotation angle (for minimum rotation effort) 
we have chosen to set: sign( x, . x ) = sign(cos(6 n )). Notice that the sign of cos(6 n ) indicates in 
which hemisphere vector z, is. Hence for the typical application of rotating a rod about a 
fixed centre of motion with its workspace located in the North hemisphere of the orientation 
space, we have sign(x, . x ) > indicating angle .v,,!, is acute. Figure 6 provides a 
graphical overview of this first move, where the origin's definition of i|r n angle has been 
chosen in analogy with the precession of the ZXZ Euler angles. 

Fig. 6. First movement from Bo to B%. Vectors zi, yo and yi are in the same plane. 

The second transform is a simple rotation about vector z, of angle cp n e]- n; n\ which we 
name "self rotation". On setting the same value for the precession and nutation angles in the 
ZXZ Euler svstem and in the new H-angles proposed system, we obtain the same position 
for vector z 1 . Hence the difference resides in the self rotation angle and the directions of 
vectors x and y. This modification of the self-rotation can be seen as an anticipation on the 
hand movement considering 6 n and \|/ n as inputs of this anticipator. 

3.2 Rotations matrices 

As indicated in the previous section the proposed orientation description system is 
decomposed into two sequential rotations. For each of these two rotations it is possible to 
write its rotation matrix and then multiply the matrices so as to express the global rotation 
matrix M. For the first rotation we denote Ml the rotation matrix. Let's define (zx, z y , z z ) the 

components of vector z, in basis BO. We have: 

z % = sin a sin y/ t 
z =— sin 0, cosy, 

z =cos# 


Components of vectors x, and y x can then be expressed as a function of (z^ z y , z z ) and we 
derive an expression of matrix Ml as function of (zx, z y , z z ): 

Design of a Bio-Inspired 3D Orientation Coordinate 
System and Application in Robotised Tele-Sonography 



z 7 


V Z v 2 + Z - 2 

V z * 2 + z , 2 

V z » 2 + z / 

— z i 

- zz 

V2 , 2 I 2 ' : 

z x + z z Jz x + z z 


For the second rotation, we note M2 the standard rotation matrix operating a rotation about 
vector z, in the frame Ri with magnitude cp n . The global rotation matrix M in the frame Ro 
can then be computed as M = M1.M2 which leads to the following expression of M as a 
function of the H-angles: 


cos 6^ cos^ +— sin 2 6' /i sin(2y/ /i )sin^ ? 

-cos# sin^ n + — sin 2 # sin(2(y /( )cos^ 


sin t) n cos* y/,, 


sin t) n cos* \ff t 

sin <p n yjl - sin 2 0„ cos 2 y/ n 
sin# (— sin^ cos^ n + cos 6„ cosy/,, sin^ 

cos^ yjl - sin 2 n cos 2 y/ n 
sin# (siny/ „sin(p„ + cos# cosy/,, cos(p„) 


- sin tf cos y/„ 


sin u„ cos* y/,, 


sin cos* ^ 


As a first analysis we can see that matrix M is not defined when 6 n = n/2 and \|/ n = or n. 
When these conditions are met, vectors x, and y l are undetermined. Hence M can be 
rewritten as function of the components of vector x 1 in basis BO since we have x t = (x x , 0, x z ) 
and z x =z z =0 and z y =-cos(\|/ n ) = +1, We find: 

M = 


- x_ cos y/„ sin <p„ 

-x z cos y/ n cos ^ - x y sin ^ 

-cos^ n 


+ x i cosy/ i sin(» n 

— x z sin ^ + x v cos y n cos <p n 



The values of x x and x 2 can be context dependent. In practical applications of rotating a rod 
about a fixed centre of motion, the case 9 n -n/2 is a limit hardly reachable. It can be found 
severable possible reasons to this: 

• the application itself exhibits bounds that avoid reaching such a limit for 6 n such as the 
robotised tele-sonography application (Courreges et al., 2008a); 

• or more simply the centre of motion may be on a plane surface and this surface avoids 
the hand from reaching this limit nutation. 

3.3 Expression of the H-angles (\|/ n ,6 n ,cpn) from the rotation matrix M components; 

singularities analysis 

3.3.1 Extraction of the H-angles from the matrix outside singularities 

The component of matrix M at line i and column j is noted rriij. We find from equation (4): 

188 Robot Arms 

9 n =arccos(m 33 ) (6) 

Outside singular configurations it can be found: 

y/ n = a tan 2 (m 13 ,-m B ) (7) 

cp n = a tan 2 ( m 21 , m 22 ) (8) 

Where "atan2" is an algorithmic function able to compute the arc tangent from two 
arguments so as to determine the quadrant of the angle on the trigonometric circle. 

3.3.2 Singularities analysis 

Two types of singularities can be identified and are studied hereafter. 

1. When mi3=0 and rri23=0 simultaneously. 

This singularity is obtained for 9 n =0 or 9 n =7t. This situation implies: z x =z y =0 and angle \|/ n is 

undetermined. In rewriting the rotation matrix M (equation 9 hereafter), one can see that M 

is independent of v|/ n as z z depends only on 9 n . Hence any value can be set for \|/ n without 

hindering the orientation. In our tele-sonography application we have given v|/ n a value of 

when this configuration is met (Courreges et al. 2008b). 


signizjcosp^ -signizjsirup^ 

sin^ C0S< P„ 



Those singularities seem to be cumbersome as they correspond to some psychophysically 
preferred directions: the vertical axis. However it ensues from the discussion in section 2.2.2 
that these singularities may be well integrated in the human internal orientations encoding 
and indeed the forthcoming results with the H-angles will enforce this conclusion. Moreover 
it can be noticed that those singularities are very different from the singularities of the ZXZ 
Euler system. Indeed in such Euler system its singularities can disrupt two of its degrees of 
freedom namely the precession and self-rotation; whereas with the H-angles only the 
precession angle is affected. 
2. When rri2i=0 and rri22=0. 

When this singularity is met angle <p n is undetermined. This situation implies both z z and z x 
are null simultaneously, hence 6 n = Tt/2 and \|/ n = or n and we meet the case where vector 
x, is undetermined. When matrix M is rewritten according to equation (5), one can find: 

tp n =atan2(-x i m l , -x z m 32 , x x m 11 + x z m 31 ) (10) 

Components x x and x z can be chosen freely according to the context but have to satisfy: 

x v 2 + x z 2 = 1 . 

3.4 Decorrelation results 

We have computed the velocities of the new H-angles attitude system for the same medical 
trajectory than in section 2.3 with the ZXZ Euler angles (Courreges et al. 2008b). As one 
could expect from the definition of the new system, there are no changes on ij/ n versus Q n 

Design of a Bio-Inspired 3D Orientation Coordinate 

System and Application in Robotised Tele-Sonography 189 

compare to the plot ij/ versus 8 of the ZXZ Euler angles velocities, hence they are still 
uncorrelated in our new system. We have obtained a good decorrelation 
between(f) n and ij/ n with a low coefficient m c = 0.0116 which is a great improvement 
compared to the Euler system. The correlation m c = 0.1552 of the variables(p n and 6 n has 
been raised in a relative important way compare to the homologous variables in the Euler 
system. However this value still remains low enough to consider the angles uncorrelated. To 
quantify the decorrelation improvement we can compute the average correlation coefficient 
for each system of angles. Our new system exhibits an average correlation m cn =0.057 
whereas the ZXZ Euler angles system exhibits an average correlation fh cE =0.339 (figure 4b). 
Consequently our new system provides a decorrelation improvement of more than 83% 
with respect to the average correlation measure. For comparison purpose, the average 
correlation measure of the PCA based system given in equation (1) is rh cKL =0.0302 which is 
much closer to our new system than to the Euler system. 

4. Application: ergonomic mouse based interface for 3D orientation in 
robotised tele-sonography 

We will show in this section how to exploit our new frame of angles to render the use of the 
standard IT mouse feasible to pilot efficiently the 3D orientation of a rod. We have tested 
this technique in the context of the particular application of robotised telesonography. In a 
first subsection we will draw some design requirements for 3D rotations techniques with a 
mouse. The following subsection will provide a short overview of existing techniques to set 
a 3D orientation with a mouse. This subsection is focused on reporting the evaluation and 
comparison of the various techniques considered with respect to the design principles 
promulgated in the preceding subsection. Next subsection will present our approach in 
exploiting the new frame of angles. The fourth subsection will describe the chosen 
psychophysical experimental protocol along with quantified results. 

4.1 Design recommendations for 3D rotations techniques with a mouse 

From their experience Bade et al. (Bade et al., 2005) have promulgated a number of four 
general principles as crucial for predictable and pleasing rotation techniques: 

1. Similar actions should provoke similar reactions: the same mouse movement should not 
result in varying rotations. 

2. Direction of rotation should match the direction of 2d pointing device movement. 

3. 3d rotation should be transitive: the rotation technique must not have hysteresis. In other 
words to one pointing location with the interface should correspond one and only one 
3D orientation whatever the trajectory ending to that location. 

4. The control-to-display ratio should be customizable: tuneable parameters must be available 
to find the best compromise between speed and accuracy according to the task and user 
preferences and is therefore crucial for performance and user satisfaction. 

We also add a fifth principle: 

5. The input interface should allow the setting of an orientation by an integrated manipulation: 
Hinckley showed (Hinckley et al., 1997) that the mental model of rotation is an integral 
manipulation in opposition with separable manipulation as defined by Jacob (Jacob 
R.J.K. et al., 1994). From a practical point of view the input interface should be designed 
to enable a simultaneous variation of each degree of freedom of the rotation. 


Robot Arms 

4.2 Overview of 3D rotations techniques with a mouse and evaluations 

This field of research has not much evolved this last decade and the works related to the use 
of the computer mouse to set an orientation in 3D are in applications to the fundamental 
research topic known as "2D interface for 3D orientation". Hence the following review 
reports some techniques where the mouse is considered as an input device with only two 
DOFs and which omit the input of the mouse wheel. The most well known and popular 
techniques because they are preferred (Chen et al., 1988) are based on the virtual trackball 
principle. It consists in surrounding the object to rotate by a virtual sphere fixed with the 
object (but the sphere may not be always displayed). The object is rotated by operating the 
virtual sphere with the mouse pointer. The common principle of these techniques to 
generate a rotation consists in letting the user select two locations with the mouse pointer. 
The first position is validated by a mouse click and remains constant until the next click; the 
second position can be moving. Those two points are then mapped to the virtual sphere and 
the projected points on the sphere enable to define an arc on a great circle. The angle of 
rotation is chosen as the aperture angle of the arc viewed from the sphere centre; and the 
axis of rotation is chosen perpendicular to the plane formed by the centre of the sphere and 
the arc. The virtual trackball-like techniques are preferred among other existing techniques 
with 2D input devices because they enable perform faster for both rotations and inspection 
tasks (Jacob I. & Oliver, 1995). From Henriksen (Henriksen et al., 2004): "Virtual trackballs 
allow rotation along several dimensions simultaneously and integrate controller and the object 
controlled, as in direct manipulation. The main drawback of virtual trackballs is a lack of thorough 
mathematical description of the projection from mouse movement onto a rotation." This class of 
techniques comprise the techniques known as the Virtual sphere of Chen (Chen et al., 1988), 
the Arcball of Shoemake (Shoemake, 1992), the Bell's virtual trackball (Henriksen et al., 
2004), the two-axis valuator (Chen et al., 1988) and the two-axis valuator with fixed up- 
vector (Bade et al., 2005). These techniques essentially differ in their plane-to-sphere 
projection. Not much experimental comparisons and evaluations of these techniques have 
been proposed in the literature. Bade et al. (Bade et al., 2005) have presented a tabular 
comparison of these techniques (excluding Chen's Virtual Sphere) with respect to the first 
four principles reported in the preceding section 4.1. We propose hereafter an extended 
comparison table (table 1 below). 


Design \. 
principles ^\ 

Virtual sphere 

Shoemake' s 

Bell's virtual 


Two axis 
valuator with 
fixed up- 

Principle 1 






Principle 2 






Principle 3 






Principle 4 






Principle 5 






Table 1. Comparison of state of the art rotation techniques with respect to the five design 

For Chen's Virtual sphere we set principle 4 as satisfied since the ratio between the sphere 
radius and the radius of the mouse's workspace on the table can be tuned which will affect 

Design of a Bio-Inspired 3D Orientation Coordinate 

System and Application in Robotised Tele-Sonography 191 

the compromise velocity/ precision. Shoemake's Arcball renounces to this principle to be 
able to satisfy principle 3 (avoid hysteresis) (Hinckley et al., 1997). We set the principle 5 to 
be unsatisfied for each technique despite the preceding quote of Henriksen because once a 
first point is selected with the mouse the possible rotation axes are constraint to lie in a 
bounded space defined by the position selected. Hence every rotation can't be performed 
within a single smooth hand movement. This is supported by Hinckley (Hinckley et al., 
1997) who argues that practically both the Virtual Sphere and the Arcball techniques require 
the user to achieve some orientations by composing multiple rotations each initiated by a 
cursor repositioning and mouse click which breaks the movement smoothness. The study 
from Hinckley did not provide evidence that the Arcball performs any better than the 
Virtual Sphere for both accuracy and completion time. The main usability problem with the 
virtual trackballs compare to free hand input devices was that users were unsure about the 
difference between being inside and outside the virtual sphere. The experiments of Bade et 
al. (Bade et al., 2005) combining inspection and rotations tasks revealed that users 
significantly perform faster with the two-axis valuator technique which was perceived as 
more predictable and comfortable for task completion than other trackball techniques. Bade 
et al. also suggest that these results were expected as the two-axis valuator fulfils most of the 
design principles. In these experiments the Shoemake's Arcball arrives in second position 
outstripping the Bell's virtual trackball and the two-axis valuator with fixed up-vector. A 
strong drawback of these techniques comes from their lack in satisfying principle 5 which 
make these techniques much slower than compared to the natural rotation of object by hand 
in 3D free space (Hinckley et al., 1997; Pan, 2008). The proposed method presented hereafter 
enables to satisfy all four principles within a large continuous range of orientations. 

4.3 Angles coding in setting a rod attitude with a mouse 

To ease the hand-eye co-ordination of the operator, for interface design, it is necessary to 
take care that the operator can easily assess the orientation changes when moving by hand 
the input device (Wolpert & Ghahramani, 2000). That is why we have chosen a biomimetic 
approach for the orientation control with a mouse. As discussed previously, the attitude of a 
US probe is defined by a sequence of two movements where the first movement enables to 
set the nutation and precession of the probe axis. This observation leads to state that 
humans have the sensorimotor ability to easily control the nutation and precession of a rod. 
In fact defining the precession and nutation of a constant length rod is the same task as 
placing a point, representing the top end of that rod on a sphere. This sphere radius is the 
length of the rod, namely in our application, the US probe length, and the sphere centre is 
the probe bottom tip. In a telesonography application only the north hemisphere is to be 
considered. It is possible to make a mental bijective transform from the orientation 
hemisphere to the mouse plane. However such a projection is not unique (Kennedy & Kopp, 
2001). To make a proper choice of a particular sphere-to-plan projection it is necessary to 
account for the human sensorimotor abilities so as to maintain decoupled DOF with respect 
to the nutation and precession. We have chosen the class of projections that preserves the 
precession angle, namely the azimuthal projections, which are projections of the sphere on a 
tangent plane. The chosen tangent point is the North Pole, which defines the so important 
vertical axis (see §.2.2.1), because such transform generates less distortion around the 
tangent point. This choice also allows to visualize the mouse control of the probe from an 
overhead view (fig. 7b), where the origin is the tangent point between the orientation sphere 
and the plane. This transform guarantees the hand-eye coordination since it allows 


Robot Arms 

establishing one to one decoupled relations between the orientation DOF of probe nutation- 
precession and the visually and kinesthetically perceived polar coordinates of the mouse. 
Indeed the precession is growing and linearly dependent on the polar angle of the mouse 
and the nutation is a growing function of the distance from the mouse to the origin. To 
totally determine the projection, we have to set the perspective point. It has been reported 
that, because it is cognitively preferred, the path adopted by hand when moving on a plane 
from an initial position to a target position is a fairly straight-line (Sergio & Scott, 1998; 
Desmurget et al., 1999). Consequently a sphere to map projection that preserves orthodromy 
should be preferred (the shortest path between two points on the sphere - which is a great 
circle - should map to a straight line on the plane). Such a projection is a gnomonic 
projection where the projection center is at the center of the sphere (see figure 7a). Despite 
the drawback of the chosen sphere-to-plane transform implying sending a nutation of it/ 2 
radian to infinity, it is however well suited to tele-sonography application for routine 
examination. Indeed we have shown in a previous work that the nutation remains lower 
than ti/4 radians during 95% of the examination time in routine abdominal US scanning 
(Courreges et al., 2008a). To rotate the probe around its own axis and define the self rotation 
angle, the mouse scrolling wheel is used. 

azimuth al A' B' C D_D' 


(a) (b) 

Fig. 7. (a) Gnomonic projection. Each point of the North hemisphere is projected on the 
azimuthal plane along a radius originating from the sphere center. In (b), use of a computer 
mouse as telerobotic control interface to set the frame of H-angles. 

This point can be a limitation since a computer mouse wheel moves generally in discreet 
steps. Hence a compromise has to be adopted when choosing the increment factor to convert 
the wheel increment to angle increment. A great factor allows driving fast but reduces the 
accuracy whereas a small factor exhibits the contrary. This factor has to be chosen according 
to the mouse wheel's total number of increments and by considering the application needs. 
The representation proposed in figure 7b makes the virtual mouse controlled probe behave 
as if it were of variable length. The bottom tip of this virtual variable length probe is fixed, 
and top corresponds to the mouse position on the plan as is shown in the illustration figure 
7b. The presented experiment revealed that operators could easily adapt to a variable length 
virtual probe (in the range employed for the nutation in this experiment) since it doesn't 
affect the attitude of the probe. To operate the simulated robot, the first step is to fix an 
origin for the mouse pointer. This origin position would correspond to a null calibration of 

Design of a Bio-Inspired 3D Orientation Coordinate 

System and Application in Robotised Tele-Sonography 193 

every angle, where the probe is in vertical position. The mouse controls directly the angles 
in the chosen frame of angles: (lirn/Qn/Cpn) for the new bio-inspired H-angles system envisaged 
and (i|r,0,cp) for the Euler angles. The following equations use the new angles but are the 
same with the Euler angles. Let's define the following notations used in figure 7b: 
Ax : displacement along X axis of the mouse from its origin position. 
Ay : displacement along Y axis of the mouse from its origin position. 

L: length of the projection of the mouse controlled probe in the XY plan. 

Ho: minimal length of the virtual variable length mouse controlled probe. Ho is a tunable 

parameter enabling to set the control sensibility on angle n such that the preceding design 

principle 4 (control-to-display ratio) can be satisfied. 

AW inc : mouse wheel increments variation (in radians) from the origin calibration position. 

Ki _ a : conversion factor from mouse wheel increments to angle in radians. This factor is 

tunable by the user and contributes to satisfy the design principle 4. 

Using the previous definitions and according to figure 7b, the expressions of the orientation 

angles as a function of the mouse inputs are: 

■ atan | — 


V)j n = jt/2 + atan2 (Ax, Ay) (12) 

cp n = K u . AW, t (13) 

One can notice that when making small incremental displacement of the mouse, the mental 
transform from a sphere to a plan is lighten since a spherical surface can be well locally 
approximated to a planar surface if the constant Ho is taken large enough compared to the 
variations of L. By construction and the exploiting of the mouse wheel our approach enables 
fulfilling all five design principles (section 4.1). In particular principle 5 is enforced by the 
fact that there is no need to proceed to multiple mouse button clicks to change an 
orientation. However in its current form our system does not allow to reach all orientations. 
A nutation of n/2 radians can't be attained. Typically we restricted the nutation to lie within 
the range [0; re/4] radians. 

4.4 Experimental assessment protocol 

The experimental setup is made to resemble the actual tele-echography setting that would 
be used in real conditions when using a mouse as interface as depicted in previous section. 
Consequently the setup is made up of a PC workstation displaying in 3D a simulated tele- 
echography robot handling a bright green probe and which end-effector orientation is 
controllable by the computer mouse (fig. 9b). The Robot Simulator has been built within 
Windows XP environment using OpenGL and Microsoft Visual C++. It accurately simulates 
the design and mobility of an actual OTELO tele-echography robot (Delgorge et al., 
2005)(see figure 8). The view chosen for experimentation (shown on fig. 8a) is in accordance 
with the actual scenario in a tele-echography operation (Canero et al. 2005). In figure 8a, the 
red rings on the right side of the screen, which look like a target, work as a guide to move 
the mouse. Its centre is chosen as the origin for the mouse. And the farthest ring defines the 
region of maximum bending in nutation of the probe. These circles where useful for the 


Robot Arms 

users during their learning phase only. Human-Machine interfaces (HMI) are generally 
assessed with static targets, which give no information on their dynamic capabilities. Hence 
we have imagined an original way of interface evaluation consisting for the subjects to 
track the moves of an opaque red dummy probe which is overlaid on screen and animated 
from a previously recorded datafile during a real abdominal US examination. Some parts of 
the robot have been given transparency to make it easier to visualize both the probes 
simultaneously. We only have considered the orientations in this experiment; hence both the 
dummy probe and the simulated robot probe are fixed in translation. 

(a) (b) 

Fig. 8. (a) The OTELO tele-echography robot simulated in OpenGL within a virtual-reality 
simulator for psychophysical assessment, (b) Actual OTELO robot in action. 

A three-axis framework with differently coloured axis was also attached to this dummy 
probe and displayed for a better visualization of its orientation. Better telepresence could be 
achieved with a HMD (Head Mounted Display) for depth perception. However this would 
annihilate the interest in using a computer mouse for proposing simple low cost control 
interface, so we preferred using a standard 2D screen displaying 3D graphics. This 
teleoperation is simulated with no time-delay to avoid interfering effects on the assessment 
of the new H-angles frame. It should be noticed that this protocol induces a cognitive load 
on the test users that is heavier than on a medical specialist who would perform a real tele- 
echography examination by means of the mouse as input device. This is due to the fact that 
in our protocol users have a few prior knowledge of the trajectory to be tracked whereas the 
medical expert imposes his desired trajectory that he is used and trained to plan to navigate 
through the human body with a US image as feedback. In other words in real practice the 
movements are intentional and performed in a know environment with known landmarks 
whereas in the proposed experimental protocol the trajectory to track is imposed. 
Nevertheless it is not desirable to try filling this workload gap by providing the test users 
with a trajectory representation in the angles space. Indeed this trick would unpredictably 
lighten the cognitive load compare to the medical expert who has to make mental rotation in 
3D space which is known to be a heavy mental load (Shepard & Metzler, 1971). Six different 
non-medical test users were solicited to carry out the experiment. They were all used to 
mouse manipulation and computer interaction. Each untrained user was shown the 
animation once just to get accustomed to the trajectory. Then he had an unlimited training 

Design of a Bio-Inspired 3D Orientation Coordinate 
System and Application in Robotised Tele-Sonography 


session to understand how to control the robot orientation by the mouse and to have a 
preview of the trajectory to track. No more than five minutes of training was sufficient for 
every test user. The medical reference trajectory duration is three minutes long. Each test 
user had three trials to track this trajectory by using the H-angles coordinate system 
associated to the mouse, and next they had three other trials using the standard ZXZ Euler 
system, for comparison purpose. The session of orientation matching with the Euler system 
is intended to assess the performance improvement provided with the H-angles system. For 
each smallest possible turn of the mouse wheel we have set an increase of 10° for angle cp n . 
This causes a limitation to the accuracy. However, whereas a smaller increment in the angle 
would have increased the precision, this would have make the robot probe difficult to rotate 
fast enough, to be able to track the animated probe rotations. We needed to strike a balance 
between, good rotation speed and higher precision, so as to obtain the optimum results. 
With the Euler system it was noticed that allowing faster rotations gave better results. 

4.5 Psychophysical results 

The orientation tracking error is computed as the minimum rotation angle between the 
frameworks of the controlled probe and dummy probe which is known to be a good metric 
in the rotations space. Let us notice this angle as £1. 

— a 

12+3 cr 


10 15 20 25 30 35 40 45 50 55 60 65 70 75 

n n+3cr ii 3a 

10 15 20 25 30 35 40 45 50 55 60 65 70 75 


Fig. 9. Observed variations in average Q, values with bounding curves at Q, plus or minus 
three times the standard deviation o. 

Figure 9 reports the average of Q, orientation error among the users versus time of trajectory 
tracking. First plot is for the mouse used to set the Euler angles and second plot for the 
mouse used to set the H-angles. Plots of figure 9 reveal practically an indisputable 
superiority of our new system compared to standard Euler system. With our new system the 

196 Robot Arms 

tracking error remains most of the time lower than 10°, whereas with the Euler system the 
error rarely drops below 10°. Whatever the experimentation time considered, the error with 
the new system is at least two times lower than with the Euler system. From testimony of 
the test users the new system acts as if the self- rotation were anticipated. Whereas with the 
Euler system the trackings were confusing mainly because of the singularity of this system 
tending to produce fast variations of the X and Y axis when the nutation is close to zero. 
Notice that the presented results integrate the human response time lag. The simulator 
measures the difference in the framework angles in real time but the user takes some time to 
perceive and react to the animated moves. Hence there is always a lag in the controlled 
probe's movements compared to the animated probe. This lag is not a constant in time, but 
perhaps is a function of various other unaccounted parameters. For example the probe 
velocity, the visual angle and so on. The overall average values of Q, obtained for the two 
systems can also be used to compare the degree of effectiveness of the two systems. For the 
Euler system average Q, value was found 46.28° while for our new attitude coordinate 
system it was observed to be 8.69°. The values of standard deviation can be understood as 
the inconsistency, in being able to accurately orient the probe. Its averaged values over time 
where observed as 1.85° for Euler system and 0.347° with the new system. 

5. Conclusion 

We designed a new coordinate system called H-angles; to parameterize the attitude of an 
object in 3D space such as the Human central nervous system would do when rotating the 
object about a fixed centre of rotation. The final cue to derive this system was obtained from 
the analysis of the medical sonography practice. In the practical case considered we showed 
experimentally that our system largely outperforms the Euler systems in the decorrelation of 
the DOFs and in practical usability of the mouse as input device for 3D rotations. The design 
considerations lead to think that the H-angles system should theoretically maintain its good 
properties in a large range of applications where the task is to rotate an object about a fixed 
point. Some more experimental evaluations will have to be carried out to verify this claim. 
We have exploited the H-angles to design an interface from the computer mouse to the 
attitude parameterisation, which satisfies the hand-eye co-ordination needs for the purpose 
of poly-articulated robot orientation telecontrol through computer network. Our 
psychophysical results in the context of a simulated robotised tele-sonography are very 
promising and should lead to some more experimental evaluation in comparison with the 
virtual trackballs techniques. Our system allows imagining the performing of 6D mouse- 
based teleoperation by using switching modes between orientation and translation control 
with a standard wheeled mouse. Some further application of the H-angles system could be 
for hand orientation prediction which should lead to a new approach of predictive control. 

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Object Location in Closed Environments for 
Robots Using an Iconographic Base 

M. Pena-Cabrera 1 , 1. Lopez-Juarez 2 , R. Rios-Cabrera 2 
M. Castelan 2 and K. Ordaz-Hernandez 2 

1 \lniversidad National Autonoma de Mexico 
2 Centro de Investigation y de Estudios Avanzados del IPN (CINVESTAV) 


1. Introduction 

In order to have an efficient use of digital image processing and pattern recognition 
techniques, it is necessary to understand the human visual system behaviour. The human 
visual system comprises the human eye and some areas of the human brain which performs 
neurological processing information. Human eye and brain together convert the optical 
information in a visual scene perception, the eye functions as the human visual system 
camera, and his function is to transform light electromagnetic waves to small electrical 
signals carrying the information to the brain to achieve data analysis and build a structured 
high resolution image. 

For a machine vision, this process represents a very complex task to implement; adequate 
elements have to exist like: environment perception, information processing and primitive 
base knowledge generation to achieve specific actions for decision making based on 
interpretation of such information. Artificial vision then might be defined as: machine 
implementation with capabilities of visual perception of the surrounding environment, 
extraction of region of interest, analysis, scene interpretation and decision making. In this 
regard some authors like Haralick & Shapiro have defined machine vision as the science which 
study and develop the theoretical bases and algorithms to obtain information of the real world 
from one or some images (Haralick&Shapiro, 1992). Another notable definition is proposed by 
Pajares et al. that define it as a machine capability to see its rounded world, to understand the 
structure and properties of a 3D world based in the analysis of one or more 2D images. 
The system described in this article resembles the above capability in automated industrial 
applications using manufacturing machinery or robots, which in some moment of the 
industrial process need to obtain its position and location within their working space, so to 
accomplish their labour tasks efficiently. A real example is locating and positioning working 
pieces within a manufacture area during assembly, painting, sorting or storage operations. 
The goal is to identify the working piece and make reference to a visual scene, then with a 
geometrical model, the working piece position is calculated and so the necessary path to 
reach the pieces using a robot-arm as described by de Lope (de Lope, et al., 1997) typically in 
an eye-in-hand configuration. 

A frequent requirement that we have found during manipulative tasks using mobile robots 
or multiple collaborative robot arms is to accurately locate objects within the work space or 

202 Robot Arms 

in the case of mobile robots, the robot itself. In this chapter we present the design and 
implementation of an artificial vision system capable of obtaining the position of an object 
(working piece), tool or end-effector device of a robot manipulator, or the camera's position 
during eye-in-hand configuration for robot arms within an enclosed environment by the 
way of "wall marks" recognition. 

An iconographic base formed by icon symbols containing enough information for the object 
is employed in order to obtain the robot's spatial position (end-effector location, for 
instance). All the information is obtained in one camera shot. The systems comprises a 
digital camera with pan-tilt movements attached to the object to get its location, the object 
might be a working piece, the end-effector of a robot-arm or any other mobile object. The 
system obtains the location and position of the camera by way of performing an observation 
around the walls, the system finds "wall marks" which indicates where the object is and its 
position. The camera gets enough information to obtain sufficient parameters to allow 
calculations from the exact position of an object; the information allows the graphical 
representation of the closed environment and the camera location. 

Pattern recognition techniques are widely used in computer vision with excellent results in 
industrial inspection and automation applications; however, some of the algorithms are not 
for practical use in real time applications. Using a visual system based on the recognition of 
symbols makes the possibility to design and implement a visual system to get working 
pieces locations by way of using an iconographic descriptive symbol set with real time 
interpretation, and making possible to build a basic geometrical symbol recognition method, 
which interpretation, might indicate information of location. By making analysis of depicted 
symbols, the object camera within an enclosed environment might know where it is, what its 
direction, and what its inclination is. 

The used symbols represent movements, paths in 2D and potentially 3D and allow 
establishing references to fixed zones within the scene; the symbols are extracted from 
general scene and from its analysis, the location information is obtained. 

2. Scope 

The scope of this research is to design a methodology implementing an artificial vision 
system capable to obtain in real time the exact position of an object within an enclosed 
working environment by using symbols and icon recognition. The system comprises a 
camera located in the object, working piece or end-effector segment of a robot-arm. The 
customized software communicates with the camera to get visual information of its 
environment in the same place where the object, working piece or end-effector segment is 
located. The parameter acquisition is carried out by image analysis and the exact location of 
the camera is obtained. Enough information is obtained as well to have a graphical 
representation of the environment. Applications for autonomous agents and robot 
manipulators are natural for this methodology. 

The possibility of using this methodology involves using closed environments, the walls are 
decorated with symbols (icons) in specific areas, the symbols contain by itself graphical 
information about where they are located; in this sense, by image processing and 
interpretation is possible to know the location of the object by looking at the icons. 

3. Design and implementation 

The design and implementation of the system, as well as the experimental platform is 
integrated with the following operation modules. 

Object Location in Closed Environments for Robots Using an Iconographic Base 


Software and Hardware (camera and dedicated algorithms). - The camera is a digital solid 

state wireless camera with pan/ tilt movement support, used to look for the appropriate icon 

in a specific working area. The wireless communication with the control computer is 

achieved via a wireless access point. 

Iconographic set. - For environment representation, the icons are basic geometric forms 

implemented within the environment (walls). 

Dummy enclosed environment for experiment. - is a specific environment to carry out test 

experimentation with the system, this dummy model, has practical dimensions to acquire 

enough data to match the digital camera specifications and satisfy the application 


Image acquisition. - It is the module in the system to capture the image and produce a 

standard format to be implemented in an image model. 

Region of Interest (ROI) algorithm.- It detects the icon presence within the scene, and 

extracts it from scene. 

Detection, interpretation and camera location algorithm.- This algorithm performs data 

processing of an image structure of the extracted icon, and together with a geometrical 

model obtain the camera coordinates within the scene. 

3.1 Software 

Visual Basic 6.0 and Matlab 7.0 platforms were used to implement the system, software 
application development was basically developed in Visual Basic 6.0, interaction with 
Matlab 7.0 were carried out by way of "Matlab Automation Server" component. The main 
purpose of the system is to obtain the coordinates of a video camera, within a pre-establish 
coordinate reference system in a known working space. The system has to have recalibration 
capabilities in order to work in different working space dimensions for different 
applications. Figure 1 shows the software modules. 


Main screen 






Fig. 1. General configuration of software modules. 

3.2 Hardware 

Hardware components of the developed system are: a digital camera, a connectivity 
network card and a PC computer. The used camera is a "Veo Wireless Observer", it has 
communication with computer through a wireless local network using a wireless access 
point and a wireless network card "3Com® 11 Mbps Wireless LAN PCI Adapter" PCI with 
protocol 802.11b of IEEE and using a point to point configuration (figure 2). 


Robot Arms 

R B 

* ■ m v v ■ 

X — @ — K* 




pDSjhcn Output 

Fig. 2. General configuration of the system. 

3.3 Iconographic base 

Simple figures were used to implement the iconographic base, combination of these figures 
have to get enough features to obtain the location information, a symbol set was created to 
conform what we have called "iconographic base", this symbols were physically painted in 
the wall of the closed environment for different zones within the working place. For testing 
purposes this was implemented in a dummy model of practical dimensions. Four "working 
icons" were proposed and each icon has to be located in the specific area of each working 

Fig. 3. Camera and network card. 

Object Location in Closed Environments for Robots Using an Iconographic Base 


area zone in order to get correlated information with physical real world location. Each 
"working icon" is conformed by four small squares, their centroid conform the corner points 
of a bigger imaginary square. The position of the icons allow subsequent analysis of 
displacements due to the perspective factors, by using geometrical models, according with 
this, it is possible to get the distance between the camera lens and the centroid of imaginary 
square as well as the angle between optical axis of the camera and the normal line crossing 
the imaginary square centroid. Dimensions for different square, were obtained by "try and 
error" in order to reach optimum resolution and view area for a particular used camera. 

3.4 Specifications for the design of the dummy model environment 

For the design of the model environment, a camera resolution of 320 x 240 pixels was used 
in order to achieve an image size suitable for fast processing and acquisition. Several test 
shots to look for ranges of distance between the camera lens and the centre of the icon to get 
an acceptable work experiment were carried out, resulting a maximum distance of 60 [cm] 
and a minimum distance of 20 [cm] for the experiment, for distances outside of this range 
was impossible to obtain the necessary parameters to obtain the required distance and angle 
of vision to get a real situation of experimental measurements of objects locations within the 
dummy model enclosed environment. The maximum value of angle of vision to guarantee 
the experiment was 55 °. Once established these parameters it can be an operational region 
for each icon, in which the system will be able to obtain the position of the camera, this 
region is applicable for each of the four icons and will be called the Operating Region of the 
working space (Figure 4a and 4b). 


Operating Region 





( I 1 I I ! 

i [ 

(a) (b) 

Fig. 4. (a) Icon's Operating Region, (b) Operating Region of the Working Space. 

Each icon frame was designed as depicted in Figure 5a and they were located in each wall as 
indicated in Figure 5b. 

In addition to the above parameters, it was also necessary to obtain the minimum height in 
order to avoid external scenes images to the working environment being acquired , which 
will act in the form of noise causing failures in image processing, the height was established 
as 50 [cm]. The resulting dimensions proposed for the construction of model tests were: 
50cm x 120cm x 120cm, as shown in figure 6. 


Robot Arms 

i i fl i 

■ g Li ■ 

Fig. 5. (a)icon's frame design, (b) icon's wall location 

Fig. 6. Dummy model environment for experimental test. 

3.5 Working icon detection 

In order to detect just one of the four working icons at a time in a particular scene, we used a 
"minimum and maximum distances" defined for each operation region, so if they are out of 
an specific range the icon is not considered for analysis. For "icon" detection, we first select 
a "merit number" so to know which region of interest has been associated for each "icon", 
this is the "P2A" number, which is a known factor between perimeter and area used in 
digital image processing on binary images, in our case we define a black object (icon zone) 
on a white background. 

3.6 Icon identification 

The main objective in having the icon set is to get enough parameters to find the camera- 
icon distance, vision angle and the region being used within all enclosed environment. 
Three algorithms were designed to perform each task, they are: 

a. Algorithm to get camera-icon distance (CIDA), 

b. Algorithm to get the angle of vision (AVA), 

c. and algorithm to find which of the four working icons is being used within the current 
image in the application and is called icon identification algorithm (II A). 

Object Location in Closed Environments for Robots Using an Iconographic Base 207 

One important requirement is to have some of the working icons as a complete image 
within the image frame; otherwise, the camera had to be moved to find it in order to have 
enough confidence on data values before calculations were made. 

Icon identification algorithm (II A). It uses a binary image and tag process, so that to get the 
difference among different elements comprising the working icons, it is necessary to obtain 
the "centroid" of each element to calculate distances as given by equation (1) with a 
reference element (triangle) and with a different orientation for each working region in 
order to obtain the identification of the working icon (see figure 5 and 7.) 

d^ B = J(x A -x B y + (Y A -Y B y (i) 



.'" dci< 

Fig. 7. Centroid distances representation. 

Once all distances are obtained, the algorithm finds which is the smaller distance so to get the 
criteria to determine which "working icons" has been founded and which "working region" 
(geometrical square) is being used within the environment. This information tells the system 
where the camera is because the "working icon" center, has been draw in the very border of 
each geometrical working region in the environment walls as shown in figure 8. 

3.7 Calculation of distance to the icon 

The algorithm to get the camera-icon distance (CIDA), is useful to obtain the distance 
between the camera lens and the center of the icon, which make use of the following 

/iconheight * ' 


d = distance 

k = proportional constant 

icon height = icon height in pixels 

The procedure is to get the distance from the analysis of the image perspective, which relate 

the magnitudes of objects with the distance being considered, such as the figures appear to 

be smaller with a greater distance and vice versa, this relationship is given by equation (2). 

To make use of the equation, the first step is proceed to a labelling process of icon elements 

(see figure 9), to identify them within the icon working area. 

The centroid of each element is calculated in order to qualify for two points: one point 

corresponds to a midpoint between the centroid of element 2 and the centroid of element 4, 


Robot Arms 

■ ■ ■ ■ 

^ A 

■ ■__■ 1 

■ ■ ■ ■ 

Fig. 8. Working icons for different working regions 




Fig. 9. Icon elements identification on image. 

the second corresponds to the midpoint between centroid of element 1 and centroid of 
element 5. It important to notice that for the calculation of the distance, the centroid of 
element 3 (triangle) is not used, and calculation of previous defined midpoints points, 
equations 2 and 3 are used, once obtained these points, it is calculated the magnitude than 
exists among them (called "apparent height") as illustrated in Figure 10. 

midpoint between 
C2 and C4 


midpoint between CI and C5 


Fig. 10. Original Icon image 

The distance a is given by equation 7 as follows: 

Object Location in Closed Environments for Robots Using an Iconographic Base 


a = ^/(PM2 v - PM1,) 2 + (PM2„ - PM1,,) 2 



a = icon height (apparent height) 

PMlx, PMly corresponds to the x-y coordinates of the mid point centroid between centroid 

of element 2 and centroid of element 4. 

PM2x, PM2y corresponds to the x-y coordinates of the mid point centroid between centroid 

of element 1 and centroid of element 5. 

They are calculated as: 


PMlx = (xC2 + xC4) /2 
PMly = (yC2 + yC4)/ 2 

PM2x = (xCl + xC5) / 2 




PM2y = (yCl + yC5) / 2 (6) 


xC2 and yC2 are coordinates of centroid 2 

xC4 and yC4 are coordinates of centroid 4 

xCl and yCl are coordinates of centroid 1 

xC5 and yC5 are coordinates of centroid 5 

A similar procedure is made when an image is obtained from another point in the scene 

with different image perspective as shown in Figure 11. 



C2 and C4 





Midpoint between 

CI and C5 

Fig. 11. Icon image at 55° with reference to the perpendicular line 

At this point, we have only obtained a parameter of the equation (2), the next step is to 
obtain the proportionality constant (k), which depends on the lens characteristics and each 
visual scene, therefore the best practical way to get it is by means of laboratory tests. 
Tests for obtaining "k" were carried out in the following way: 


Robot Arms 

1. Place the camera at a known distance "d" to obtain a focused and central positioned 
image of the icon. 

2. With the acquired image get the parameter "apparent height". 

3. Values "d" and "apparent high" were replaced in the equation (2) to obtain a 
proportionality constant "ki". 

4. Repeat steps 1 to 3 for 5 different distances from the operational region of a specific icon 
(see table 1). 

5. We calculated the average of the "ki" in order to find a constant "k" allowing us to get 
the value of "d" at any point within the operating region of each icon. The obtained 
values of Ki obtained in laboratory tests are provided in Table 1. 

3.8 Camera position 

The following actions were performed to obtain a final position of the camera: First, a 
reference position is found by a system initialization (HOME), then camera will begin to 
make a PAN movement to find some available "working -icon" and checking validated 
distances, once the icon is found, the system moves the camera to get the icon in the image 
centre of current image, calculates the criteria distances explained before to know which 
icon is within the scene. 

Once the icon is in the centre, the system calculates the distance between camera and icon 
and the vision angle (angle between optical camera axis and normal line to icon centroid). 
The distance and the angle are shown in Figure 12 and the corresponding values obtained in 
laboratory tests are given in Table 1. 

icon symbol 


\ ■ 


V distance 




\ camera 






Fig. 12. Position vector 

With this information a geometrical model is used to get the (x,y) coordinate of the camera 
in the working region being used and a graphical representation of the camera and the 
environment interaction is obtained. In order to get this situation, three parameters are 
obtained and used to get the final position of the camera: 

Object Location in Closed Environments for Robots Using an Iconographic Base 


a. Icon identification (minimum, maximal distance), 

b. camera-icon distance and 

c. Angle of vision 0, 


K t 

Icon height 
















Average K = 2997.2 

Table 1. Values obtained with laboratory measurements. 

4. Experimental results 

Experimental tests were made to obtain the performance of the system in real conditions, to 
this purpose an enclosed squared environment was built, the iconographic symbols set as 
described before were and painted on the four different walls of the environment, which 
represents four working icons areas. 

4.1 Experimental method 

Experimental tests showed very good results with real time performance of the system. Once 
the system was implemented, and the practical operation checked, the precision of the system 
was verified. In order to achieve this task, we used different working regions for each working 
icon. For experiment purposes, the area of the enclosed environment for each icon was divided 
in three zones with 20, 40 and 55cms distances from each icon as shown in figure 13 

/ \ 

*■ \ 

* \ 

/ V, 

/ \ ions n 

aZ A 


4* : \j5ii2pi: " 

/-■^ i- - ,--■ - /\*.one o 

• - -■ -- -rT r>r 

s ^ — -"\ MP 

^> V K = ^^ ** 

'<- ^--.. ^^"^"^ J.'"' 

^ s " ,5" : 

f = .^,_. ,. = i^ 

Fig. 13. Divided zones for each working icon. 


Robot Arms 

The difference between desired and real locations was measured and the results are showed 
in Table 2. Eight points were selected in a random manner for each zone and real desired 
physical coordinates were obtained. The camera was positioned on each selected point and 
the system calculated the positions to compare its results (table 1). 

The experiment was made for all points in all different regions for all different icons a 
graphical representation was made with the obtained values to get a better feedback of the 
system performance, Figures 14 and 15 shows a graphics for two different icon working 

Testing of the complete system with software and hardware integrated was done by 
selecting ten random points inside of the workspace, then the camera along with a driver 
support which performs the pan/ tilt movements was located in real points and compare the 
response given by the system against the actual position values. The results of the tests were 
as follows: 

Measurements [cm] 

Measurements [cm] 

Time [s] 























































Table 1. Real and System calculated positions. 


■ ■ 








e * 



1 1 



































Fig. 14. Graphical representation of measured and real points for icon zone 1. 

Object Location in Closed Environments for Robots Using an Iconographic Base 
















J 1 





■ 'l ■ 




♦ ' 










- 10 





Fig. 15. Graphical representation of measured and real points for icon zone 2. 

Experimental testing was repeated ten times and average measurements were registered. 
Figure 16, shows a graphic for the error in x axis for 3 zones of a working icon. 

Error zone, comparative data 




















Fig. 16. Error for x coordinate 

Average error in x and y can be established and for each of the measurements zones , in 
order to see in which of the three zones the system's behavior is more precise, resulting as 

Zone 1 

Zone 2 

Zone 3 

x= 0.58 cm 

x= 0.82 cm 

x= 1.19 cm 

y= 0.79 cm 

y= 0.89 cm 

y= 1.47 cm 

Table 2. Average error for x and y for three different zones. 

Previous data indicates through analysis and comparison of the obtained test results that: 
the precision of results that provides the system are directly proportional to the distance that 
the icon is captured, in addition also we can see from figure 12 that the greater the view 
angle, the greater error value too. 

214 Robot Arms 

5. Conclusions 

A system capable to obtain real time position of an object using a pan/ tilt camera in hand as 
the sensor was developed. An iconographic symbol set was used to identify different 
working areas within an enclosed simulated working environment. Iconographic symbols 
projected or draw in the environment walls can be used to the purpose of get a calculated 
camera position. The camera has automated icon search capabilities, experimental 
measurements show feasible practical use in manufactured and assembly applications to 
find real-time positions in working tools for robot manipulators. Experimental test were 
carried out with some optimal laboratory conditions to get images such as good 
illumination, good contrast and specific sizes of experimental environment in order to assess 
the system. However, future work envisages an automated recalibration so for real 
applications in an arm robot manipulator with a camera mounted onto the arm in a hand-in- 
eye configuration. It is intended to preserve the use of basic geometric figures as it resulted 
very useful in this investigation and it can speed up the distance calculation in more 
complex scenarios. 

6. References 

Pajares M., Gonzalo., de la Cruz G, Jesus (2002). Vision por computador Ed. Ra-Ma. 

Colombia.R.M. Haralick and L.G. Shapiro (1993). Computer and robot vision. Ed. 

Addison-Wesley Publishing Co., New York. 
J. de Lope, F. Serradilla, J. Zato (1997). Sistema de localization y posicionamiento de piezas 

usando vision artificial. Inteligencia Artificial l(l):57-64 
Pressman, Roger S. (2002). Ingenieria del software. Un enfoque prdctico. Ed. McGraw-Hill. 

Madrid .Matlab Automation Server 

7470 .html, visited Feb. 03, 2011 . 
Malvino Albert p, Leach Donald P. (1993). Principios y aplicaciones digitales. Ed. Marcombo 

Boixareu editores. 


From Robot Arm to Intentional Agent: 

The Articulated Head 

Christian Kroos, Damith C. Herath and Stelarc 
MARCS Laboratories, University of Western Sydney 


1. Introduction 

1.1 Robots working together with humans 

Robot arms have come a long way from the humble beginnings of the first Unimate robot at a 
General Motors plant installed to unload parts from a die-casting machine to the flexible and 
versatile tool ubiquitous and indispensable in many fields of industrial production nowadays. 
The other chapters of this book attest to the progress in the field and the plenitude of 
applications of robot arms. It is still fair, however, to say that currently industrial robot 
arms are primarily applied in continuously repeated manufacturing task for which they 
are pre-programmed. They are known for their precision and reliability but in general use 
only limited sensory input and the changes in the execution of their task due to varying 
environmental factors are minimal. If one was to compare a robot arm with an animal, even 
a very simple one, this property of robot arm applications would immediately stand out as 
one of the most striking differences. Living organisms must sense changes in the environment 
that are crucial to their survival and must have some flexibility to adjust their behaviour. In 
most robot arm contexts, such a comparison is currently at best of academic interest, though it 
might gain relevance very quickly in the future if robot arms are to be used to assist humans 
to a larger extend than at present. If robot arms will work in close proximity with and directly 
supporting humans in accomplishing a task, it becomes inevitable for the control system of 
the robot to have far reaching situational awareness and the capability to adjust its 'behaviour' 
according to the acquired situational information. In addition, robot perception and action 
have to conform a large degree to the expectations of the human co-worker. 
Countless situations can be imagined (and are only a step away from current reality while 
fully autonomous mobile robots might still be far off): 

• A robot arm lifting and turning a heavy workpiece such as a car engine for human 
inspection and repair; 

• A robot arm acting as a 'third hand' for a human worker for all kinds of construction and 
manufacturing work that is yet too complex to be fully automated; 

• A robot arm assisting a temporarily or permanently bedridden person and/or the nurses 
taking caring of the person. For the latter, one of the most important tasks would be again 
the careful lifting of the person; 

• An intelligent robotic device assisting people with walking difficulties replacing the 
current clunky walkers; 

216 Robot Arms 

• A robot arm assisting elderly people at home with all tasks that require considerable force 
(from opening a jar to lifting heavy items) or involve difficult to reach places (which might 
be simply the room floor). 

To assess the social and economical impact that such a development would have, one might 
draw a parallel to the revolution that heavy machinery meant for construction and agriculture 
and with this for society at large. Within one generation, one might speculate, it could become 
inconceivable to imagine many workplaces and the average home in industrialised countries 
without assisting robot arms. 

1.2 Joint action 

Humans collaborate frequently with each other on all kinds of tasks, from jointly preparing a 
meal to build a shelter to write a book about robot arms. Even if the task is very simple such 
as carrying a load together, the underlying coordination mechanism are not. Collaborations 
with physical co-presence of the actors require a whole gamut of perceptive 'cues' to be 
observed and motor actions to be adjusted. This might be accomplished during execution 
or already during planning taking into account predictions of the co-workers' actions. In 
almost all situations so-called joint attention (to which we will return shortly) is an additional 
prerequisite. The emerging field of joint action research in psychology (Sebanz et al., 2006) 
tries to unravel the perceptive, cognitive and motor conditions and abilities that allow the 
seemingly effortless coordination of human action to accomplish a common goal. Sebanz 
et al. (2006) suggest an operational definition of joint action as 'any form of social interaction 
whereby two or more individuals coordinate their actions in space and time to bring about 
a change in the environment'. In this regard, the requirement for joint action builds on the 
concept of joint attention and extends it by requiring the prediction of actions of another. Joint 
action therefore depends on the abilities to (1) share representations, (2) predict actions, and 
(3) integrate predicted effects of one's own and the other's actions. These requirements do 
not change if the other is a machine or - narrowed down given the topic of this book - a robot 
arm. Admittedly, one could offload all the coordination work to the human co-worker by 
'stereotyping' the action of the robot arm, i.e. reduce the movement vocabulary and make 
it easily predictable in all situations, but one would at the same time also severely limit the 
usefulness of the robot arm. 

Arguably, we humans excel in joint actions because we perceive other humans as intentional 
agents similar to ourselves. Whether or not this would apply to robots is at the current 
state of research an unanswered question and, moreover, a question that poses difficulties 
to any investigation as there is no direct access to the states of the human mind. Some 
studies, though, provided partial evidence in favour of this using sophisticated experiment 
designs. Participants have been found to attribute animacy, agency, and intentionality to 
objects dependent on their motion pattern alone (Scholl & Tremoulet, 2000) and studies 
in Human-Robot Interaction (HRI) confirmed that robots are no exceptions (though clear 
differences remain if compared to the treatment of motor actions of other humans; see 
Castiello, 2003; Liepelt et al., 2010). Humans might also attribute emotions and moods to 
robots (e.g. Saerbeck & Bartneck, 2010). An important aspect of considering a robot as an 
intentional agent is the tacitly included assumption that the actions of the robot are neither 
random nor fully determined (as both would exclude agency), but a more or less appropriate 
and explainable response to the environment given the current agenda of the robotic agent. 
Note that 'intentional agent' does not equate with human-like: animals are intentional agents 
as well, and there is long history of collaboration of humans with some of them, one of the 

From Robot Arm to Intentional Agent: The Articulated Head 21 7 

most perspicuous examples being shepherds and their dogs. While high-level understanding 
of conspecifics as intentional beings like the self (so called 'theory of mind', see Carruthers 
& Smith (1996) for a theoretical review) might be a cognitive competency that is limited to 
humans and maybe (Tomasello, 1999) - or maybe not (Call & Tomasello, 2008) - other primates, 
understanding others as intentional beings similar to oneself is not a capability that emerged 
ex nihilo. Over the last two decades, research concerned with the development of this capacity 
has indicated that it is closely tied to what is now generally called joint attention (Tomasello 
et al., 2005). 

1.3 Joint attention 

The concept of joint attention refers to a triadic relationship between two beings and an 
outside entity (e.g. an object like an apple) whereby the two beings have a shared attentional 
focus on the object. Joint attention has been seen as a corner stone in the development 
of social cognition and failure to achieve it has been implicated in Autistic Spectrum 
Disorders (Charman, 2003). As pointed out by Tomasello (1999), for joint attention to be 
truly joint, the attentional focus of the two beings must not only converge on the same 
object but both participants must also monitor the other's attention to the object (secondary 
inter-subjectivity). This should be kept in mind when thinking about a robot arm collaborating 
with humans as it basically requires some kind of indicator that the control system is aware 
of the current human actions and - at least potentially - is able to infer the intention of the 
human co-worker. This indicator might be a virtual or mechatronic pair of eyes or full face. 
In previous research on joint attention, a variety of different definitions have been used, not 
all of them as strict as Tomasello's. This is because applying his definition poses substantial 
difficulties in verifying whether joint attention has occurred in an experimental set-up, in 
particular when investigating infants or non-humans, and by extension also makes modelling 
it in a machine more difficult. 

Its link to understanding other people as intentional beings notwithstanding, joint attention is 
not uniquely human; it has been observed in monkeys (Emery et al., 1997) and apes (Carpenter 
et al., 1995). In the latter study, joint attention was heuristically defined in terms of episodes 
of alternating looks from an ape to the person and then to the object. This way of quantifying 
joint attention through gaze switching has become the one most frequently used, even though 
gaze alternation is not always a reliable indicator of joint attention as mentioned above. 
Furthermore, gaze alternation constitutes neither a sufficient nor a necessary condition for 
joint attention. On the one hand, it is very common among animals to use another animal's 
gaze direction as a clue to indicate important objects or events in the environment but the fact 
that the other animal paid attention to this event is of no consequence and not understood 
(Tomasello, 1999); on the other hand establishing joint attention, for instance, through the use 
of language is a much more powerful mechanism than just gaze following (since it includes 
the aspect of the object or event on which to focus). All of this will have an impact on designing 
a robot arm control system that is able to seamlessly and successfully cooperate with a human. 
Not surprisingly, joint attention in robotics poses challenges not to be underestimated (Kaplan 
& Hafner, 2004). 

2. A virtual agent steps into the physical world 

We went into some details with regard to joint action and attention to explain some of the basic 
motivations driving our use of a robot arm and shaping the realisation of the final system, the 
Articulated Head. Because of its genesis as a work of art, many of our aims and many of 


Robot Arms 

Fig. 1. The Articulated Head. 

the properties of the Articulated Head are probably far beyond the ordinary in robot arm 
research and development. On the hardware side, the Articulated Head consist of a Fanuc LR 
Mate 200iC robot arm with an LCD monitor as its end effector (see Figure 1). The Articulated 
Head represents the robotic embodiment of the Prosthetic Head (Stelarc, 2003) by Australian 
performance artist Stelarc, an Embodied Conversational Agent (ECA) residing only in virtual 
reality, and is one of the many faces of the Thinking Head developed in the Thinking Head 
Project (Burnham et al., 2008). 

The Prosthetic Head (Figure 2) is a computer graphic animation based on a 3D laser scan of 
the head of the artist. Through deforming its underlying 3D mesh structure and blending 
the associated texture maps a set of emotional face expressions and facial speech movements 
are created. A text-to-speech engine produces the acoustic speech output to which the face 
motion are synchronised. Language input from the user is acquired through a conventional 
computer keyboard. Questions and statements from the user are sent to the A.L.I.C.E. chatbot 
(Wallace, 2009) which generates a response utterance. The Prosthetic Head has been presented 
at numerous art exhibitions, usually as a projection of several square meters in size. 
The Articulated Head was born as a challenge to the traditional embodiment of ECAs in 
virtual reality. No matter how convincing the behavioural aspects and cognitive capabilities 

From Robot Arm to Intentional Agent: The Articulated Head 


Fig. 2. The Prosthetic Head. 

of a conventional ECA might be, it would always fall short of sharing the physical space with 
the interacting human. As physical co-presence is of great importance for humans (e.g. infants 
do not learn foreign language sounds from television; see Kuhl et al., 2003), transgressing the 
boundaries of virtual reality would enable a different quality of machine-human interaction. 
The robot arm enables the virtual agent to step out into the physical space shared with its 
human interlocutor. The sensory capabilities of the Articulated Head in the form of cameras, 
microphones, proximity sensors, etc. (Kroos et al., 2009) allow it to respond to the user's action 
in the physical world and thus engage the user on a categorically different level compared to 
interfacing only via written text and the 2D display of an animated face. 

2.1 Problems of the physical world 

With the benefits of the step into the physical world, however, come the difficulties of the 
physical world. Not only becomes perfect virtual perception noisy real world sensing, precise 
and almost delay-free visual animation imprecise and execution time-adherent physical 
activation, but also the stakes are set higher to achieve the ultimate goal of creating a 
believable interactive agent. The virtual world is (at least currently) much sparser than the 
physical world and thus offers substantially less cues to the observer. Less cues mean less 
opportunities to destroy the user's perception of agency which is fragile no matter how 
sophisticated the underlying control algorithms might be given the current state of art of 
artificial intelligence. In other words, compared to the virtual-only agent, many more aspects 
of the robotic agent must be modeled correctly, because failure to do so would immediately 

220 Robot Arms 

expose the 'dumb' nature of the agent. This might not constitute a problem in some of the 
applications of human-robot collaboration we discussed above since the human co-worker 
might easily accommodate to shortcomings and peculiarities of the machine colleague but 
it can be assumed that in many other contexts the tender fabric of interactions will be torn 
apart, in particular, if the interactions are more complex. Statements in this regard are 
currently marred by their speculative nature as the appropriate research using psychological 
experiments has not been done yet. This is equally due to the lack of sufficiently advanced 
and interactive robots as to the difficulties to even simulate such a robot and systematically 
vary experiment conditions in so-called Wizard-of-Oz experiments where unknown to the 
participants a human operator steers the robot. 

In our case of a robotic conversational agent, the overall goal of the art project was at stake: 
the ability to engage in a conversation, to take turns in a dialogue, to use language and speech 
more or less correctly, requires as a prerequisite an intentional agent. Thus, if the robot's 
actions had betrayed the goal of evoking and maintaining the impression of intentionality 
and agency, it would have compromised the agent as a whole: either by unmasking the 
integrated chatbot as a 'shallow' algorithm operating on a limited database with no deeper 
understanding of the content of the dialogue or by destroying the perception of embodiment 
by introducing a rift between the 'clever' language module and the failing robot. 

2.2 Convincing behaviour 

The cardinal problem encountered is the requirement to respond to a changing stimulus-rich 
environment with reasonable and appropriate behaviour as judged by the human observer. 
Overcoming this problem is not possible, we propose, without integration of the plenitude of 
incoming sensory information as far as possible and selection of the most relevant parts taking 
into account that (for our purposes) the sensory information is not a sufficiently complete 
description of the physical environment. Therefore, as a first step after low-level sensory 
processing, an attention mechanism is necessitated that prioritises information relevant to the 
current task of the agent over less important incoming data. An attention model not only 
takes care of the selection process, it also implicitly solves the problem of a vastly incomplete 
representation of the environment. For any control system that receives the output of the 
attention model, it is per se evident that it receives only a fragment of the available information 
and that, should this information not be sufficient for the current task, further data need to 
be actively acquired. In a second step then, the selected stimuli have to be responded to with 
appropriate behavior, which means in most cases with motor action though at other times 
only the settings of internal state variables of the system might be changed (e.g. an attention 

There is another important issue here: when it comes to the movements of the robot not only 
the 'what' but also the 'how' gains significance. 'Natural' movements, i.e. movements that 
resemble biological movements, contribute crucially to the overall impression of agency as 
the Articulated Head has a realistic human face. Robot motion perceived as 'mechanical' 
or 'machine-like' would abet the separation of the robot and the virtual agent displayed 
on the LCD monitor, and thus create the impression of a humanoid agent being trapped 
in the machine. Again, if we allow a little bit of speculation, it can be hypothesised that 
robot arms engaging in joint action with humans will need to generate biological motions in 
order to make predictions of future actions of the robot arm easier and more intuitive for the 
collaborating human. 

From Robot Arm to Intentional Agent: The Articulated Head 









Motion range (deg) 







Motion speed (deg/s) 







Table 1. Robot joint motion range and speeds. 

3. The robot arm 

The robot arm employed is a Fanuc LR Mate 200iC used typically in industrial applications. It 
has six degrees of freedom. Table 1 shows the speed and the motion range of each individual 
joint of the robot arm with joint 1 being the closest to the mounting base. The robot is mounted 
on a custom made four-legged heavy steel structure which does not require fixing it to the 
floor for stable operation. The robot's work envelop is protected from inadvertent entry 
by human users through a series of glass structures and interlocks. The robot is controlled 
through an external control box using a proprietary handling application program language. 
In standard usage the robot is pre-programmed with the necessary movement instructions 
using a 'teaching pendant' with target points entered 'online' during the teaching phase 
prior to commissioning of the robot. However, in order to accommodate realtime interactive 
behavior desired by the Articulated Head, the robot interface was customised such that target 
points (i.e. motor goals, see section 7) could be created during the execution phase. This also 
meant that an additional layer of safety checks were needed to prevent the robot from trying 
to reach unreachable locations resulting in collisions and/or singularity conditions. 

4. Inter-Component Communication and Sensing 

4.1 Software Architecture 

The communication framework (Herath et al., 2010) for our system combines approaches from 
open agent-oriented systems previously used for multimodal dialogue systems (e.g. Herzog & 
Reithinger, 2006) and frameworks for high-performance robotic platforms (e.g. Brooks et al., 
2005; Gerkey et al., 2003). The driving motivation is to enable easy integration of components 
with different capabilities, written in different programming languages and potentially 
running on different platforms (including distributed platforms). A specific requirement 
for our application is realtime performance under massive data processing over streaming 
audio and video; this ruled out the existing multimodal dialogue platforms, and also led us 
to eschew standards-based APIs which incur overheads on message-passing to components. 
In common with other dialogue platforms, we use an event-driven framework, which has 
a number of desirable properties, such as: naturally modelling the non-linear nature of 
human interaction; providing the flexibility required for easy integration of components into a 
distributed architecture; dynamically prioritising software components and event types; and 
optimising the system via inter-component configuration commands for particular interaction 

4.2 Sensors 

We have adopted two commercially available camera systems for tracking people in 3D 
and faces in close proximity. A stereo camera mounted rigidly high on a wall opposite 
the Articulated Head looking downwards into the interaction space of the robot provides 
information about human movement. The commercial people tracking algorithm is based 

222 Robot Arms 

on an assumed depth profile of an average human and uses disparity images produced by a 
calibrated camera pair. It provides the localisation and height information of all people within 
the camera's field of view to the robot. The tracking system is capable of tracking multiple 
persons with considerable tolerance to occlusion and occasional disappearance from the field 
of view. 

A monocular camera mounted above the top edge of the LCD screen provides fine grain 
information about humans directly interacting with the robot. Data from this camera feeds 
on to a face tracking algorithm that is capable of detecting and tracking a single face in 
the camera's field of view. The used algorithm has a high degree of accuracy withstanding 
considerable occlusion, scale variance and deformations. 

Stereo microphones mounted on the back panel of the robot enclosure coupled to an auditory 
localiser provides accurate information of the instantaneous locations (azimuth) of a moving 
interlocutor in a noisy and reverberant environment. Localisation is limited to the half sphere 
in front of the robot and provides azimuth angle from about —90° to +90°. The localisation 
is based on Faller & Merimaa (2004) which has been modified and adapted to the Articulated 
Head setup. 

In addition to above components, various ancillary components such as proximity 
detectors, keyboard input device, gesture recognition system, text-to-speech system, dialogue 
management system, monitoring and a data logging systems are also implemented to support 
the various interactive aspects of the Articulated Head. Figure 3 shows the overall component 

5. Attention model 

Human attention is a heavily researched area with thousands of scholarly articles. In 
general, attention is investigated in controlled psychological experiments focusing on specific 
aspects of the overall phenomenon of attention, say, visual attention activated by certain 
types of motion perceived in peripheral vision. A substantial amount of knowledge has 
been accumulated, though sometimes disparate or conflicting. One of the most important 
findings for attention systems in machines (Shic & Scassellati, 2007) is that attention is 
driven by two sources, saliency in the perceptual input (bottom-up or exogenous attention) 
and task-dependent attention direction (top-down or endogenous attention). The former 
is comparatively easier to handle and evaluate (e.g. data from human participants can be 
acquired using eye-tracking technology). Top-down attention mechanisms, on the other 
hand, pose severe difficulties as they typically involve high-level world knowledge and 
understanding. Unfortunately, however, top-down mechanisms appear to be more decisive: 
Even for a barn owl only 20% of attentional gaze control could be explained by low-level 
visual saliency (Ohayon et al., 2008). 

Compared to human attention, modelling attention in artificial agents is less studied. 
Attention models have been primarily investigated and applied in virtual environments (Kim 
et al., 2005), avoiding the largely unsolved problem of real world object recognition. The 
identity of objects placed in a virtual environment can be made known directly to the attention 
model of the agent; an option that is clearly not available when dealing with a robot and real 
world sensing. In addition, the sensory input in real world sensing is always affected by 
considerable noise. The majority of the attention models for virtual agents is biologically 
inspired and thus complex (e.g. Bosse et al., 2006; Itti et al., 1998; Peters & Itti, 2006; Sun et al., 
2008), though others amount to not much more than a fixed selection process of an input 
source based on the value of a single (or a few) parameter. 

From Robot Arm to Intentional Agent: The Articulated Head 


-Physically Coupled- 

Animated Head 

MAX Interface 


MAX Audio 

Event Manager 

Data Logger 

Text Input Client 

Text Display Client 

Matlab Interface 

Sonar Proximity 

Robot Interface 

Audio Localiser 

Fig. 3. Component topology. THAMBS' in the rightmost box stands for the Thinking Head 
Attention Model and Behavioural System and is described in section 5 to 8. 

5.1 Attention models in robotics 

A few attempts have been made to develop attention models for robots. One of the first 
implementations, named FeatureGate, used an artificial neural network that operated on 
2D feature maps (Driscoll et al., 1998), specifically - in the tests presented in the paper 
- feature maps derived from synthetic images. In its handling of how the features were 
weighted, it allowed changes depending on the task, that is, top-down attention was partially 
established. However, despite its sophisticated algorithms, FeatureGate corresponds more 
to a target-detection system than an attention system as it does almost nothing other than 
find a given target among distractors in an efficient manner. This is in line with many of 
the experiments studying visual attention in humans, but these experiments use a simplified 
controlled experiment set-up to isolate aspects of the complex human attention system; they 
do not indicate that the human attention can be reduced to an efficient search method (e.g. 
Cavanagh, 2004). We would argue that when it comes to work with a robot, attention truly 
starts when there are several potential targets and the system has to make a choice: discard 
(temporarily) all but one target (the most relevant one given the current task) and focus on it. 

224 Robot Arms 

Only bottom-up attention mechanisms were considered in the attention model developed in 
Metta (2001). The study focused on log-polar vision which simulates the distribution of the 
photoreceptors in the primate eye. They showed that this type of space variant vision is well 
suitable for implementing an attention system and controlling robot movements through it. 
It addresses implicitly the reduction of previous attention systems to target detection systems 
by having different sensory resolutions in the periphery and the foveal area. Thus, attention 
coincides with the fixation point, but events registered in the periphery could still attract 
attention and command fixation. 

Like the model of Driscoll and colleagues, the visual attention system of Breazeal & Scassellati 
(1999) used with the robot Kismet was based on the guided search model of Cave & Wolfe 
(1990) and Wolfe (1994), but it went beyond it. The attention system did not only combine 
several different feature maps, but also modeled the influence of habituation effects and 
integrated the impact of the robot's motivational state on the generated attention activation 
map. In this way, their attention system became context-dependent and Kismet's behaviour 
emerged from the interaction of its own state and the state of the environment. For 
instance, the attentional gain for faces was increased during Kismet's seek people behaviour 
and decreased during its avoid people behaviour. Kopp & Gardenfors (2001) postulated 
the imperative of an attention system for perceived intentionality of a robotic agent, but, 
unfortunately, they did not implement one. Their robot arm equipped with two cameras, 
one for peripheral vision (above arm) and one for central/focal vision (at arm), would have 
been, as they indicated, a very suitable platform for it. 

A multimodal attention system to guide an interactive robot was proposed in Deniz et al. 
(2003). The researchers used feature and saliency maps to model bottom-up attention 
combining visual and acoustic features, but did not include top-down processes. They also 
did not treat visual and acoustical events equally: acoustic events could not change the focus 
of attention, they only reinforced the visual event closest to the acoustic event. 
Attention models based on salience maps (the majority of those mentioned above) can be 
computationally very costly, particularly if an increasing number of features and larger 
feature and salience maps are used. Ude et al. (2005) demonstrated that with proper parallel 
processing in a distributed implementation, sufficient speeds were achieved to steer the visual 
system of the humanoid robot they used in realtime. The model of Ude et al. (2005) was further 
developed in (Moren et al., 2008) by strengthening the top-down aspects and exploring a new 
way of integrating bottom-up and top-down mechanisms. The authors combined the use of 
saliency maps from Itti and Koch's (Itti et al., 1998) model with a more flexible version of the 
feature-specific top-down mechanism of Cave's FeatureGate (Cave, 1999). 
In this vein it appears as if attention models in robots have been recently recognised as a 
way to tackle problems with visual segmentation. As we mentioned earlier, this view seems 
at times to be more inspired by psychological experiments investigating visual attention than 
biological attention itself; they seem to model aspects of those experiments. As a consequence, 
robotic attention does not only fail to model the complexity of human attention - something 
which is expected and generally unavoidable given the current state of technology - but also 
reduces attention to an auxiliary function of the robot's perceptual system while it should be, if 
anything, its 'guide'. Nevertheless useful results can be obtained. Yu et al. (2007), for instance, 
devised an attention-based method to segment specified object contours from the image 
motion produced by the egomotion of a mobile robot. They employed a pre-attentive state for 
contour segmentation and competing motion-based bottom-up and contour-based top-down 

From Robot Arm to Intentional Agent: The Articulated Head 225 

salience maps. Using Bayesian inference top down saliency biased the final probabilistic 
attention distribution toward the task-dependent object contour. 

Developing and applying attention models is usually motivated with the proposed 
requirement that robots interacting with humans should possess an attention system similar 
to that of humans. More specifically, joint attention is argued to be a conditio sine qua non for 
cooperative human-robot action, machines learning from human instructors, 'theory of mind' 
in robots (the ability to predict what another can and cannot perceive), and similar high-level 
cognitive social capabilities. However, joint attention of robot and human is trivially not 
possible if the robot does not have the capacity of attention in the first place (at least in the form 
of being able to select specific elements of the input over others). In the literature reviewed 
above attention systems sometimes seem more to be a means to an auxiliary end than being an 
integral and essential part of the robots behavioural system. A different and more immediate 
motivation is brought forward in Bachiller et al. (2008) in their 'attention-based control model'. 
The authors view the attention system as an essential mediator between visual perception and 
action control that is needed to handle two important tasks: to select perceptual information 
relevant for action execution and to limit potential actions based on the perceived situational 
context. In the context of autonomous navigation of robots they employ bottom-up and 
top-down attention processes and also model overt and covert attention. Covert attention 
refers here to regions of interest that are pre-activated within the attention system through 
target selection, but are currently not the focus of attention (overt attention). 
Finally, a method to switch autonomously between bottom-up and top-down attention in a 
mobile robot was introduced by Xu et al. (2010). The different attention modes are activated 
dependent on the state the robot is in (exploring, searching, or operating) which links attention 
back to behaviour - something that in our view is essential for attention: attention cannot be 
seen as a passive input information selection mechanism since it is tied to action and also 
actively changes what is perceived. The benefits of the latter was demonstrated in Xu et al. 
(2010) through steering the active stereo camera of the robot they used towards target area 
identified by the bottom attention system as relevant for the task and then apply the top-down 
attention to keep the target in the focus of attention. 

5.2 The attention model of the Articulated Head 

In the Articulated Head, the attention model is part of the Thinking Head Attention 
and Behavioural System (THAMBS) that manages all high-level aspects of the interaction 
including the generation of response behaviour (see next section) except for conversational 
matters that are taken care of by the chatbot. THAMBS goes beyond straightforward action 
selection insofar as it is also concerned with determining the specific characteristics of the 
motor behaviour associated with the response (see section 7 ) and in that behaviours can 
interact with each other and can change the way the sensory input is processed. It consists of 
four modular subsystems: (1) a perception system, (2) an attention system, (3) a central control 
system, and (4) a motor system. Figure 4 shows a diagram of THAMBS, with its subsystems 
and flow of information. THAMBS is currently implemented in Matlab (The Math Works, 
Inc) and following object-oriented programming principles, its subsystems are represented as 
classes to ensure their strict modularity. 

Despite the array of sensing devices, the robot's sensing of the world is relatively sparse since 
the sensing devices and their software are specialised on particular tasks (e.g. people tracking, 
face detection). It is multimodal, however, and complex enough to allow sophisticated 
interactions with human users. Nevertheless, the difference in the input compared to almost 


Robot Arms 


Central control 


Evaluates own state and state 
of environment; makes decisions; 
generates task representations; 
derives motcw goals 






Perceptual system 

Modules: People tracking, face 
tracking, movement detection, 
acoustic localisation, etc. 

Attention system 

Selects perceptual information 
according to salience while 
taking into account current task; 
winner-takes-all approach; 
generates motor goals 








Motor system 

High-level representation of 



Fig. 4. Schematic of THAMBS and its subsystems. 

all other attention models has implications on architecture and functionality of the attention 
system within THAMBS. First an upstream perception system transforms the information 
from the sensing modules into a standardised perception event making it possible to process 
very different events (e.g. a person being detected within the visual field of the Articulated 
Head as well as a character string being sent from the keyboard) with respect to their 
attentional importance not their detail characteristics. An attentional weight is assigned to 
the incoming perceptual event computed using a base weight assigned as a parameter value 
to the type of the perceptual event (e.g. acoustic localisation, people tracking) and an attention 
weight factor derived from the specific event instance, usually confidence values (the default 
value is 1): 

w p {i) = b p {i) w base (1) 

Note that both values might be changed during run time according to changes of the active 
task, for instance the base weight of face tracking might be increased to favour face-to-face 
interactions with a single person over 'distracting' other people in the area covered by the 
stereo camera. The resulting attention weight is checked against a threshold dependent again 
on the type of perceptional event. If the event passes, an attention focus is created (covered 

From Robot Arm to Intentional Agent: The Articulated Head 227 

attention) which is characterised by three properties: its weight, its decay function, and the 
spatial location in the real world it is referring to. 

The weight is the attentional weight described above, however, for an already existing focus 
it is modified based on the duration of its existence. 

w p (i,t)=w p (i)k(t) (2) 

where k(t) is a decay function assigned to the attention focus that decides about its lifetime 
and its impact over time. It ensures that the attention focus outlives the potentially very short 
instance of the perceptual event that created it (e.g. a loud startling impulse-like noise) but at 
the same time that its strength is fading even if registration of the perceptual event is sustained 
(habituation). We found a generalisation of the simple exponential functions, the Kohlrausch 
function, preferable to the simple exponential functions employed in other attention models. 
The Kohlrausch function is often called a 'stretched exponential' and is known to be able to 
describe a wide range of physical and biological phenomena (Anderssen et al., 2004). It is 
given by: 

M*) = e ~ { * y ® 

It is the additional parameter /3 that stretches or compresses the function. Thus with an 
appropriate setting a plateau at around zero is formed that guarantees in our implementation 
a high activation for a certain period immediately after the attention focus has been 
established ensuring that the focus can 'fend off lower weight foci for some time. The decay 
function parameters are initialised dependent on the type of perceptual event, but again, they 
are modified dynamically during run time (in fact, the entire function can be replaced with a 
different one if e.g. a discontinuous function is needed. However, this is currently not used). 
The last and most important defining property of an attention focus is the segment of 
3D space it is referring to: the location of the event that attracted attention. Thus, the 
attention foci are spatially organised (compare space versus object-based attention in models 
of human attention; review in Heinke & Humphreys, 2004). This plays a decisive role in the 
identification of a new perceptual event as identical - per definition - to one of the already 
existing attention foci. Locations in spherical coordinates of the new event and all old foci are 
compared whereby underspecification always produces a positive value. If an incoming event 
is considered to be identical to one of the old attention foci, the old focus is kept. Its weight, 
however, is updated by combining of the new and old weights in supra-additive manner: 

w c = Wold + w new a p with < flp < 1 (4) 

where a„ is a parameter specific to the perceptual event type of the new event. The decay 
function of the focus is not reset, which causes a slow but steady decline of the weight values 
even if new events are constantly reinforcing an old attention focus, for instance, a person 
standing still within the visual field of the Articulated Head. The procedure has a similar 
effect as the habituation modeled in Breazeal & Scassellati (1999). 

A perceptual event might have several different features depending on its type. There are 
always the obtained sensory data values themselves, typically some form of tracking data 
(though in case of the keyboard 'sense', it is only a binary on/off signal and a character string) 
but in addition there might be velocities and, potentially, accelerations or other properties 
computed over the input values such as statistical and spectral moments or energy measures. 
Each feature on its own is able to invoke an attention focus: If one feature fails to create an 
attention focus because it can not pass the threshold, another feature might do so. For instance, 


Robot Arms 

an avoidance behaviour might have blocked tracked people to become an attention focus, but 
very fast movements of a person might nevertheless 'break through' via the velocity feature. 
After all attention foci are generated and their weights computed, one of them is chosen as 
the single event that is attended by the system using a winner-takes-all-strategy based on the 
highest weight. Recently, we added an alternative, a persistence strategy. After a perceptual 
event providing an identification marker (currently only people tracking) has become the 
attended event through the default strategy, the attention system locks on the event based on 
its ID for a limited time - independent of its decaying weight - unless there is a very powerful 
distractor. The trigger for the persistence strategy is random at the time, but it was devised to 
be replaced with a trigger based on familiarity once face recognition is integrated in THAMBS. 
The attended event is passed on to the central control system (see next section) together 
with the information whether or not it is considered new by the attention system. To 
model pursuit movements based on a close attentional link between perception and action 
(Schneider & Deubel, 2002), the attention system is able to send a specific motor command, 
named look_there, directly to the motor system. It steers the robot arm to orient the normal 
to the monitor display plane (and with this the optical axis of the monovision camera) toward 
the spatial location of the attended event. This serves a two-fold purpose: to create the 
impression as if the virtual face displayed on the monitor is looking at the location of the 
event which attracted its attention and to provide the Articulated Head with more information 
about the source of the event via the monovision camera (see Figure 5). 

(a) Idle (b) Idle (c) Focussing 

Fig. 5. The Articulated Head being idle (a,b) and focussing on an interlocuter (c). 

6. Behavioural system 

The response behaviour of the Articulated Head is generated by the central control subsystem 
of THAMBS (except for verbal interactions). It is the highest-level processing stage for 
information about the environment that arrives from the sensors after being evaluated by 
the attention system. This information itself, however, is not independent from the behaviour 
(perception-action link) as the behaviour affects the sensing information either directly (e.g. 
the position and orientation of the monovision camera) or indirectly as the behaviour might 
cause a change in task priorities which in turn might trigger a modification in the attentional 
weights assigned to perceptual event types or single attention foci. The central control system 
is essentially still a stimulus-response system based on a set on conditional rules, but it is 
non-trivial since the rules are modified during run time and are at some points subject to 
probabilistic evaluation. 

From Robot Arm to Intentional Agent: The Articulated Head 229 

6.1 Behaviour triggers 

In THAMBS the conditional rules are called behaviour triggers and realised as small decision 
trees. In most current triggers, however, only one branch leads to the activation of a 
behaviour while the remaining ones cause a termination of the trigger evaluation. This 
is bound to change with more complex behaviour options being implemented at future 
development stages. The trigger evaluation is implemented to be able to handle trees of 
arbitrary complexity fast and efficiently while requiring only a few lines of code in Matlab. 
The basic idea is to collect the test results as '0' or '1' characters in a single string while moving 
down the tree following the active branch and then treat the resulting string as representing 
a binary number and convert this number into an index into the possible actions associated 
with the terminal nodes. In pseudo-code: 

(1) Collect all tests associated with the nodes of the decision tree 
in a one-dimensional array of expressions (named 'conditions' here) 
that evaluate to a Boolean value. Move from top to bottom and left to 
right . 

(2) Collect from left to right all possible actions associated with 
the terminal nodes in a one-dimensional array of function handles 

(named 'actions' here) . 

(3) Initialise an indicator variable 'indTest' with value 1, 
another indicator variable 'indAction' with 0, and an empty string 
array ' collectedTests' . 

Note: It is assumed that array indexing starts with 1 not 0. 

%%% loop through all (relevant) tests of the decision tree. 
WHILE indTest <= SIZE (conditions ) 

%%% evaluate the indicated condition 
isTrue = EVALUATE (conditions [ indTest ] ) 

%%% if condition evaluates to true, add a ' 1' else a '0' to the string 
%%% array that collects the test results 
IF isTrue 

add ' 1' to collectedTests 

add '0' to collectedTests 

%%% stop further testing in case of a branch type tree 

%%% (only one branch of the tree has a behaviour assigned) 

IF tree is of type branch 


%%% split according to type of tree: determine the index for the 

%%% next test 

IF tree is of type branch 
indTest = indTest + 1 


%%% treat the collected test string buffer as a binary number and 
%%% use it to update the index. In this way tests that belong to 
%%% branches, which have been already discarded, will be ignored 
indTest = 2 A (SIZE (collectedTests) ) + BINARY_TO_DECIMAL (collectedTests) 


230 Robot Arms 

%%% treat the collected test string buffer as a binary number 
%%% and convert it to a decimal number: the result is an index in the 
%%% possible actions arranged according to the last level of the tree 
indAction = BINARY_TO_DECIMAL (collectedTests ) + 1; 

%%% for type 'branch' only one branch has an action assigned: do we have 
%%% it? If yes, set indAction to 1 
IF tree is of type branch 

IF indAction == 2"SIZE (conditions) 

indAction = 1 

indAction = 

%%% Call the appropriate action function 
IF indAction > 

EXECUTE (actions [indAction] ) 

Behaviour triggers have a priority value assigned to them which decides about the order of 
their evaluation. A trigger with a higher priority is evaluated after a trigger with a lower 
priority since the associated behaviours of both might modify state variables of THAMBS and 
the changes made by the behaviour with the higher priority trigger should take precedence, 
that is, these changes should be the ones that persist and should not be overwritten. Typically 
behaviours specify a motor goal to be achieved. Motor goals are abstract representations 
of motor actions to be executed by the robot arm or the virtual head displayed on the 
monitor. They are context independent and thus no sensory information is required at this 
stage, though several attributes control their processing by the motor system later on. Other 
behaviours only modify values of state variables and with this cause a change in how future 
sensory information is processed or in how other motor goals are executed. The behaviours 
themselves are implemented as independent routines and their function handles are passed 
to the trigger evaluation routine. 

6.2 Behaviour disposition 

Two other important aspects of the central control system besides the generation of response 
behaviour need to be mentioned. First there is a set of subroutines that model endogenous 
processes. These are changes in THAMBS' state variables that are not activated - directly 
or indirectly - by stimuli from the environment. An example would be the spontaneous 
probability-driven awakening that happens sooner or later if the Articulated Head has fallen 
asleep (due to lack of stimuli in the environment; see section 9 for an overview of the behaviors 
of the Articulated Head). It contrasts with the awakening activated by a loud sound event via 
an ordinary behaviour trigger (Kroos et al., 2010). The endogenous processes would be more 
accurately assigned to a system other than the central control system as they emulate low-level 
functions of the mammalian brain located, for instance, in the brain stem. Future versions of 
THAMBS will parcel out these processes and subordinate them to a new subsystem. 
Secondly, there is a preparatory phase. THAMBS currently employs a master execution 
loop running usually at 10 Hz through all the necessary tasks of its systems, starting with 
the endogenous processes, then handling perception, continuing with attention and so on. 

From Robot Arm to Intentional Agent: The Articulated Head 231 

However, we mentioned in section 5 that attention has a strong top-down component. This is 
specifically accounted for in the preparatory routine which is executed before the perception 
system becomes active in each evaluation cycle of the master loop. The routine can change 
thresholds of perception and attention, and in this way it can steer perception and attention 
toward stimuli relevant for its current task and its current inner state (active perception and 
active attention). Moreover, it is able to insert new behaviour triggers in the set of active 
behaviour triggers. For instance, the behaviour trigger attend_close activates a behaviour 
with the same name if a sizable number of people are in the visual field of the Articulated 
Head. The attend_close behaviour changes the weight of attention foci that are based on 
people-tracking to favour people closer to the Articulated Head over people further away. The 
trigger has limited lifetime and is currently inserted randomly from time to time. In future 
versions this will be replaced by an insertion based on the values of other state variables, 
e.g. the variable simulating anxiety. Note that the insertion of an behaviour trigger is not 
equivalent with activation of the associated behaviour. Indeed, taking the example above, the 
attend_close behaviour might never be activated during the lifetime of the trigger if there 
are no or only few people around. An Articulated Head made 'anxious' through the detection 
of a reduction in computational resources might insert the behaviour trigger fearing a crowd 
of people and dealing with this 'threatening' situation in advance. 

The distinction between preemptive behavior disposition and actual response triggers is 
important because it constitutes an essential element in the differentiation of a simple 
context-independent stimulus-response system with the classical strict division of input and 
output from an adaptive system where the interaction with the environment is always 
bi-directional. Note also that the preparatory phase de-facto models expectations of the 
system about the future states of its environment and that contrary to the claims in Kopp 
& Gardenfors (2001), this does not necessarily require full internal representations of the 

7. Motion generation 

The motor subsystem of THAMBS is responsible for converting the abstract motor goals 
transmitted both from the attention system and the central control system into concrete 
motor primitives. At first, the motor system determines which one of the two motor goals 
- if both are in fact passed on - will be realised. In almost all cases the 'deliberate' action 
of the central control system takes precedence over the pursuit goal from the attention 
system. Only in the case of an event that attracts exceptional strong attention the priority 
is reversed. In humans, this could be compared with involuntary head and eye movements 
toward the source of a startling noise or toward substantial movement registered in peripheral 
vision. A motor goal that cannot currently be executed might be stored for later execution 
depending on a specific storage attribute that is part of the motor goal definition. For pursuit 
goals originating from the attention system the attribute is most of the time set to disallow 
storage as it makes only limited sense to move later toward a then outdated attention focus. 
On completion of the goal competition evaluation, the motor systems checks whether the 
robot is still in the process of executing motor commands from a previous motor goal and 
whether this can be interrupted. Each motor goal has an InterruptStrength and an 
InterruptResistStrength attribute and only if the value of the InterruptStrength 
attribute of the current motor goal is higher than the InterruptResistStrength of the 
ongoing motor goal, the latter can be terminated and the new motor goal realised. Again, if 
the motor goal cannot currently be executed it might be stored for later execution. 

232 Robot Arms 

Motion generation in robot arms might be considered as a solved problem (short of a few 
problems due to singularities maybe) and as far as trajectory generation is concerned we 
would agree. The situation, however, changes quickly if requirements on the meta level 
of the motion beyond desired basic trajectory properties (e.g. achieving target position 
with the end effector or minimal jerk criteria) are imposed. In particular in our case, as 
mentioned in section 2.2, the requirement of the movements to resemble biological motion. 
Since there exists no biological model for joint system such as the Fanuc robot arm, an 
exploratory trial-and-error-based approach had to be followed. At this point a crucial problem 
was encountered: if the overall movement of the robot arm was repeated over and over 
again, the repetitive character would be quickly recognised by human users and perceived 
as 'machine-like' even if it would be indistinguishable from biological motion otherwise. 
Humans vary constantly albeit slightly when performing a repetitive or cyclical movement; 
they do not duplicate a movement cycle exactly even in highly practised tasks like walking, 
clapping or drumming (Riley & Turvey, 2002). In addition, the overall appearance of the 
Articulated Head does not and cannot deny its machine origin and is likely to bias peoples' 
expectations further. Making matters worse, the rhythmical tasks mentioned above still 
show a limited variance compared to the rich inventory of movement variation used in 
everyday idle behaviour or interactions with other people - the latter includes adaptation 
(entrainment) phenomena such as the adjustment of one's posture, gesture and speaking style 
to the interlocutor (e.g. Lakin et al., 2003; Pickering & Garrod, 2004) even if it is a robot 
(Breazeal, 2002). These situations constitute the task space of the Articulated Head while 
specialised repeated tasks are virtually non-existent in its role as a conversational sociable 
robot: one more time the primary difference between the usual application of a robot arm 
and the Articulated Head is encountered. Arguably, any perceivable movement repetition 
will diminish the impression of agency the robot is able to evoke as much as non-biological 
movements if not more. 

To avoid repetitiveness we generated the joint angles for a subsets of joints from probability 
density function - most of the times normal distributions centred on the current or the target 
value - and used the remaining joints and the inherent redundancy of the six degrees of 
freedom robot arm to achieve the target configuration of the head (the monitor). Achieving a 
fixed motor goal with varying but compensating contributions of the participating effectors is 
known in biological motion research as motor equivalence (Bernstein, 1967; Gielen et al., 1995). 
The procedure we used not only resulted in movements which never exactly repeat but also 
increased the perceived fluency of the robot motion. 

Idle movements, small random movements when there is no environmental stimulus to 
attract attention, are a special case. No constraint originating from a target configuration 
can be applied in the generation of these movements. However, completely random 
movements were considered to look awkward by the first author after testing them in the 
early programming stages. One might speculate that because true randomness is something 
that never occurs in biological motion, we consider it unnatural. As a remedial, we drew our 
joint angle values from a logarithmic normal (log normal) distribution with its mean at the 
current value of the joint. As can be seen in Figure 6, this biases the angle selection toward 
smaller values than the current one (due to a cut-off at larger values forced by the limited 
motion range of the joint; larger values are mapped to zero), but in general keeps it relatively 
close to the current value. At the same time in rare cases large movements in the opposite 
direction are possible. 

From Robot Arm to Intentional Agent: The Articulated Head 233 

30 45 60 

joint angle (degree) 

Fig. 6. Log normal probability distribution from which the new joint angle value is drawn. 
The parameters of the distribution are chosen so that the mean coincides with the current 
angle value of the robot joint. In this example it is at 24.7 degree indicated in the figure as 
dotted line and the cut-off is set to 90 degree. 

The generation of the motor primitives realising an abstract motor goal is handled by 
specialised execution routines. The handles to these functions are stored as motor goal 
attributes and can be exchanged during runtime. The subroutines request sensory information 
if required such as the location of a person to be 'looked at' and transduce the motor goal in the 
case of the robot arm into target angle specifications for the six joints, and in case of the virtual 
head into high-level graphic commands controlling the face and eye motion of the avatar. 
The joint angle values determined in this way are sent to the robot arm after they have passed 
safety checks preventing movements that could destroy the monitor by slamming it into one 
of the robot arm's limbs. 

8. State variables and initial parameters 

We described THAMBS from a procedural point of view which we deemed more appropriate 
with respect to the topic of evoking agency and more informative in general. However, this 
does not mean that there is not a host of state variables that provide the structure of THAMBS 
beyond the subsystems described in the previous section. In particular, the central control 
system has a rich inventory of them. They are organised roughly according to the time 
scale they operate on and their resemblance to human bodily and mental states. There are 
(admittedly badly named) 'somatic' states which constitute the fastest changing level, then 
'emotional' states on the middle level and 'mood' states on the long term level. Except for the 
somatic states such as alertness and boredom those states are very sparsely used for the time 
being, but will play a greater role in further developments of THAMBS. 

Although the behaviour of the Articulated Head emerges from the interplay of environmental 
stimuli, its own actions, and some pre-determined behaviour patterns (the behaviour triggers 
described in section 6.1), a host of initial parameter settings in THAMBS influences the overall 
behaviour of the Articulated Head. In fact, very often changing individual parameter settings 
creates patterns of behaviour that were described by exhibition visitors in terms of different 

234 Robot Arms 

personalities or sometimes mental disorders. To investigate this further, however, a less 
heuristically driven approach is needed for modelling attention and behaviour control and 
rigorous psychological experiments. At the time of the writing both are underway. 

9. Overview of most common behaviour patterns 

If there is no environmental stimulus strong enough to attract the attention of THAMBS, the 
Articulated Head performs idle movements from time to time and the value of its boredom 
state variable increases. If it exceeds a threshold, the Articulated explores the environment 
with random scanning movements. While there is no input reaching the attention system, 
the value of the alertness state variable decreases slowly such that after prolonged time the 
Articulated Head falls asleep. In sleep, all visual senses are switched off and the threshold for 
an auditory event to become an attention focus is increased. The robot goes into a curled-up 
position (as far as this is possible with the monitor as its end effector). During sleep the 
probability of spontaneous awakening is very slowly increased starting from zero. If no 
acoustic event awakens the Articulated Head it wakes up spontaneously nevertheless sooner 
or later. If its attention system is not already directing it to a new attention focus, it performs 
two or three simulated stretching movements. 

If there is only a single person in the visual field of the Articulated Head, it focuses in 
most instances on this person and pursues his or her movements. There might be, however, 
distractions from acoustic events if they are very clearly localised. If the person is standing 
still, the related attention focus gains for a short time a very high attentional weight, but if 
nothing else contributes, the weight fades, making it likely that the Articulated Head diverts 
its attention. Alternatively, the face detection software might register a face as the monovision 
camera is now pointing toward the head of the person and the person is not moving 
anymore. This would lead to a strong reinforcement of the attention focus and in addition 
the Articulated Head might either speak to the person (phrases like T am looking at you!', 
'Did we meet before?', 'Are you happy?' or 'How does it look from your side?') or mimic the 
head posture. The latter concerns only rotations around the axis that is perpendicular to the 
monitor display plane in order to be able to maintain eye contact during mimicry. 
If a visitor approaches the information kiosk (see Figure 7) containing the keyboard, the 
proximity sensor integrated into the information kiosk registers his or her presence. The 
Articulated Head turns toward the kiosk with a high probability because the proximity sensor 
creates an attention focus with a high weight. If the visitor loses the attention of THAMBS 
again due to inactivity or sustained typing without submitting the text, the Articulated Head 
would still return to the kiosk immediately before speaking the answer generated by the 

If there are several people in the vicinity of the Articulated Head, its behaviour becomes 
difficult to describe in general terms. It now depends on many factors which in turn depend 
on the behaviour of the people surrounding the installation. THAMBS will switch its attention 
from person to person depending on their movements, whether they speak or remain silent, 
how far they are from the enclosure, whether it can detect a face and so on. It might pick 
a person out of the crowd and follow him or her for a certain time interval, but this is not 
guaranteed when a visitor tries to actively invoke pursuit by waving his or her hands. 

From Robot Arm to Intentional Agent: The Articulated Head 


Fig. 7. The information kiosk with the keyboard for language-based interactions with the 
Articulated Head. 

10. Validation 

The Articulated Head is a work of art, it is an interactive robotic installation. It was designed 
to be engaging, to draw humans it encounters into an interaction with it, first through its 
motor behaviour, then by being able to have a reasonably coherent conversation with the 
interlocutor. Because of the shortcomings of current automatic speech recognition systems 
(low recognition rates in unconstrained topic domains, noisy backgrounds, with multiple 
speakers) a computer keyboard is still used for the language input to the machine but 
the Articulated Head answers acoustically with its own characteristic voice using speech 
synthesis. It can be very entertaining but entertainment is not its primary purpose but a 
consequence from its designation as a sociable interactive robot. In terms of measurable goals, 
interactivity and social engagement are difficult to measure, in particular in the unconstrained 
environment of a public exhibition. 

So far the Articulated Head has been presented to the public at two exhibitions as part of 
arts and science conferences (Stelarc et al., 2010a;b) and hundred of interactions between the 
robotic agent and members of the audience have been recorded. At the time of the writing, 
a one year long exhibition in the Powerhouse Museum, Sydney, Australia, as part of the 
Engineering Excellence exhibition jointly organised by the Powerhouse Museum, Sydney, and 
the New South Wales section of Engineers Australia has just started (Stelarc et al., 2011). A 
custom-built glass enclosure was designed and built by museum staff (see Figure 8) and a 
lab area immediately behind the Articulated Head installed allowing research evaluating the 
interaction between the robot and members of the public over the time course of a full year. 


Robot Arms 

Fig. 8. The triangular-shaped exhibition space in the Powerhouse Museum, Sydney. 

This kind of systematic evaluation is in its earliest stages, preliminary observations point 
toward a rich inventory of interactive behaviour emerging from the dynamic interplay of 
the robot system and the users. The robot's situational awareness of the users' movements 
in space and its detection of face-to-face situations, its attention switching from one user and 
one sensory systems to the next according to task priorities that is visible in its expressive 
motor behaviour, all this entices changes in the users' behaviour which, of course, modify 
again the robots' behaviour. At several occasions, for instance, children played games similar 
to hide-and-seek with the robot. These games evolved spontaneously despite that they were 
never considered as an aim in the design of the system and nothing was directly implemented 
to support them. 

11. Conclusion and outlook 

Industrial robot arms are known for their precision and reliability in continuously repeating a 
pre-programmed manufacturing task using very limited sensory input, not for their ability to 
emulate the sensorimotor behaviour of living beings. In this chapter we have described our 
research and implementation work of transforming a Fanuc LR Mate 200iC robot arm with 
an LCD monitor as its end effector into a believable interactive agent within the context of a 
work of art, creating the Articulated Head. The requirements of interactivity and perceived 
agency imposed challenges with regard to the reliability of the sensing devices and software, 
selection and integration of the sensing information, realtime control of the robot arm and 
motion generation. Our approach was able to overcome some but certainly not all of these 
challenges. The corner stones of the research and development presented here are: 

From Robot Arm to Intentional Agent: The Articulated Head 237 

1. A flexible process communication system tying sensing devices, robot arm, software 
controlling the virtual avatar, and the integrated chatbot together; 

2. Realtime online control of the robot arm; 

3. An attention model selecting task-dependly relevant input information, influencing action 
and perception of the robot; 

4. A behavioral system generating appropriate response behaviour given the sensory input 
and predefined behavioral dispositions ; 

5. Robot motion generation inspired by biological motion avoiding repetitive patterns. 

In many respects the entire research is still in its infancy, it is in progress as on the artistic side 
the Articulated Head is a work in progress, too. It will be continuously further developed: 
for instance, future work will include integrating a face recognition system and modelling 
memory processes allowing the Articulated Head to recall previous interactions. There are 
also already performances planned in which the Articulated Head will perform at different 
occasions with a singer, a dancer and its artistic creator. At all of these events the robot 
behaviour will be scripted as little as possible; the focus will be on interactivity and behaviour 
that instead of being fixated in few states emerges - emerges from the interplay of the 
robot's predispositions with the interactions themselves leading to a dynamical system that 
encompasses both machine and human. Thus, on the artistic side we will create - though only 
for the duration of the rehearsals and the performances - the situation we envisioned at the 
beginning of this chapter for a not too distant future: robots working together with humans. 

12. References 

Anderssen, R. S., Husain, S. A. & Loy, R. J. (2004). The Kohlrausch function: properties and 
applications, in J. Crawford & A. J. Roberts (eds), Proceedings of 11th Computational 
Techniques and Applications Conference CTAC-2003, Vol. 45, pp. C800-C816. 

Bachiller, P., Bustos, P. & Manso, L. J. (2008). Attentional selection for action in mobile robots, 
Advances in Robotics, Automation and Control, InTech, pp. 111-136. 

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Robot Arm-Child Interactions: A Novel 
Application Using Bio-Inspired Motion Control 

Tanya N. Beran and Alejandro Ramirez-Serrano 

University of Calgary 

1. Introduction 

Robot arms were originally designed in the 1960s for intended use in a wide variety of 
industrial and automation tasks such as fastening (e.g., welding and riveting), painting, 
grinding, assembly, palleting and object manipulation). In these tasks humans were not 
required to directly interact or cooperate with robot arms in any way. Robots, thus, did not 
require sophisticated means to perceive their environment as they interacted within it. As a 
result, machine type motions (e.g., fast, abrupt, rigid) were suitable with little consideration 
made of how these motions affect the environment or the users. The application fields of 
robot arms are now extended well beyond their traditional industrial use. These fields 
include physical interactions with humans (e.g., robot toys) and even emotional support 
(e.g., medical and elderly services). 

In this chapter we begin by presenting a novel motion control approach to robotic design 
that was inspired by studies from the animal world. This approach combines the robot's 
manipulability aspects with its motion (e.g., in case of mobile robots such as humanoids or 
traditional mobile manipulators) to enable robots to physically interact with their users 
while adapting to changing conditions triggered by the user or the environment. These 
theoretical developments are then tested in robot-child interaction activities, which is the 
main focus of this chapter. Specifically, the children's relationships (e.g., friendship) with a 
robotic arm are studied. The chapter concludes with speculation about future use and 
application of robot arms while examining the needs for improved human-robot 
interactions in a social setting including physical and emotional interaction caused by 
human and robot motions. 

2. Bio-inspired control for robot arms: simple and effective 

2.1 Background: human robot interactive control 

There are many different fields of human-robot interaction that have been developed within 
the last decade. The intelligent fusion scheme for human operator command and 
autonomous planner in a telerobotic system is based on the event based planning 
introduced in Chuanfan, 1995. This scheme integrates a human operator control command 
with an action planning and control for autonomous operation. Basically, a human operator 
passes his/her commands via the telerobotic system to the robot, which, in turn, executes 
the desired tasks. In many cases both an extender and material handling system are 
required during the implementation of tasks. To achieve proper control, force sensors have 

242 Robot Arms 

been used to measure the forces and moments provided by the human operator [e.g., Kim, 
1998]. The sensed forces are then interpreted as the desired motion (translational and 
rotational) while the original compliant motion for the robot remains effective. To improve 
previous works, video and voice message has been employed, [e.g., Wikita, 1998], for 
information sharing during the human-robot cooperation. The projection function of the 
video projector is to project the images of the messages from the robot into an appropriate 
place. The voice message has the function to share the event information from the robot to 
the human. Fukuda et al. proposed a human-assisting manipulator teleoperated by 
electromyography [Fukuda, 2003]. The works described above simplify the many different 
applications in the field of human-robot interaction. The control mechanism presented 
herein allows robots to cooperate with humans where humans practically employ no effort 
during the cooperation task (i.e., minimal effort during command actions). Moreover, in 
contrast to previous work, where the human-robot cooperation takes place in a well 
structured engineered environment, the proposed mechanism allows cooperation in 
outdoor complex/ rough terrains. 

Human-robot arm manipulator coordination for load sharing 

Several researchers have studied the load sharing problem in the dual manipulator 
coordination paradigm [e.g., Kim, 1991]. Unfortunately, these results cannot be applied in 
the scope of the human-arm-manipulator coordination. The reason is that in the dual 
manipulator coordination, the motions of the manipulators are assumed to be known. 
However, in the human-arm-manipulator coordination, the motion of the object may be 
unknown to the manipulator. A number of researchers have explored the coordination 
problem between a human arm and a robot manipulator using compliant motion, predictive 
control and reflexive motion control [Al-Jarrah, 1997; Al-Jarrah and Zheng, 1997; Iqbal, 1999]. 
In such scenarios the human-arm, by virtue of its intelligence, is assumed to lead the task 
while the manipulator is required to comply with the motion of the arm and support the 
object load. The intelligence of the arm helps perform complex functions such as task 
planning and obstacle avoidance, while the manipulator only performs the load sharing 
function. By coordinating the motions of the robotic arm with the user's arm, the uncertainty 
due to the environment can be reduced while load sharing can help reduce the physical 
strain in the human. 

Complaint control 

The basic ability for a robot to cooperate with a human is to respond to the human's 
intentions. Complaint motion control has been used to achieve both load sharing and 
trajectory tracking where the robot's motion along a specific direction is called complaint 
motion. This simple but effective technique can be used to guide the robot as it attempts to 
eliminate the forces sensed (i.e., precise human-robot interaction). However, diverse 
problems might occur that require different control approaches. 

Predictive control 

The problem in the framework of model-based predictive control for human-robot 
interaction has been addressed in numerous papers [e.g., Iqbal, 1999]. First, the transfer 
function from the manipulator position command to the wrist's sensor force output is 
defined. Then, the desired set point for the manipulator force is set to equal the gravitational 
force. Numerous results reported in the literature indicate that predictive control allows the 

Robot Arm-Child Interactions: A Novel Application Using Bio-Inspired Motion Control 243 

manipulator to effectively take over the object load, and the human's forces (effort) stays 
close to zero. Moreover, manipulators have been shown to be highly responsive to the 
human's movement, and relatively small arm force can effectively initiate the manipulation 
task. However, difficulties still remain when sudden large forces are exerted to the robot to 
change the motion of the shared object (load) as the robot arm acts as another automated 
load to the human. 

Reflexive motion control 

Al-Jarrah [1997] proposed reflexive motion control for solving the loading problem, and an 
extended reflexive control was shown to improve the speed of the manipulator in response 
to the motion of the human. The results show that the controller anticipated the movements 
of the human and applied the required corrections in advance. Reflexive control, thus, has 
been shown to assist the robot in comprehending the intentions of the human while they 
shared a common load. Reflexive motion is an inspiration from biological systems; however, 
in reflexive motion control it is assumed that the human and the manipulator are both 
always in contact with an object. That is, there is an object which represents the only 
communication channel between the robot and the human. This is not always possible. 
Thus, mechanisms that allow human-robot cooperation without direct contact are needed. 
In an attempt to enhance pure human-robot arm cooperation, human-mobile manipulator 
cooperation applications have been proposed [e.g., Jae, 2002; Yamanaka, 2002; Hirata, 2005; 
Hirata, 2007]. Here the workspace of the cooperation is increased at the expense of the 
added complexity introduced by the navigation aspects that need to be considered. 
Accordingly, humans cooperate with autonomous mobile manipulators through intention 
recognition [e.g., Fernandez, 2001]. Herein mobile-manipulators refer to ground vehicles 
with robot arms (Fig. la), humanoid robots, and aerial vehicles having grasping devices 
(Fig. lb). In contrast to human-robot arm cooperation, here the cooperation problem 
increases as the mobile manipulator is not only required to comply with the human's 
intentions but simultaneously perceives the environment, avoids obstacles, coordinates the 
motion between the vehicle and the manipulator, and copes with terrain/ environment 
irregularities/ uncertainties, all of this while making cooperation decisions, not only 
between human and robot but also between the mobile-base and robot arm in real-time. 
This approach has been designated as active cooperation where diverse institutions are 
running research studies. Some work extends the traditional basic kinematic control 
schemes to master-slave mechanisms where the master role of the task is assigned to the 
actor (i.e., human) having better perception capabilities. In this way, the mobile manipulator 
not only is required to comply with the force exerted by the human while driving the task, 
but also contributes with its own motion and effort. The robot must respond to the master's 
intention to cooperate actively in the task execution. The contribution of this approach is 
that the recognition process is applied on the frequency spectrum of the force-torque signal 
measured at the robot's gripper. Previous works on intention recognition are mostly based 
on monitoring the human's motion [Yamada, 1999] and have neglected the selection of the 
optimal robot motion that would create a true human-robot interaction, reducing robot 
slavery and promoting human-robot friendship. Thus, robots will be required not only to 
help and collaborate, but to do so in a friendly and caring way. Accordingly, the following 
section presents a simple yet effective robot control approach to facilitate human-robot 


Robot Arms 

Arm's pan motion angle 
(rotation w.r.t. thez axis) 

Arm's tilt motion angle 
(rotation w.r.t. they axis) 

**• Robot arm with 4 
degrees of freedom 



Arm' s elbow motion angle 
(rotationw.r.t. they* axis) 

Fig. 1. Schematic diagrams of: a) Mobile manipulator, and b) Aerial robot with robotic arm. 

2.2 Simple yet effective approach for friendly human-robot interaction 

The objective of this section is to briefly present, without a detailed mathematical analysis, a 
simple yet effective human-robot cooperation control mechanism capable of achieving the 
following two objectives: i) Cooperation between a human and a robot arm in 3D 
dimensions, and ii) Cooperation between a human and a mobile-manipulator moving on 
rough terrain. Here the focus is placed on the former aspect as it is directly related to the 
experiments discussed in Section 3. 

Many solutions have been developed for human-robot interaction; however, current 
techniques work primarily when cooperation occurs on simple engineered environments, 
which prevents robots from working in cooperation with humans in real human settings 
(e.g., playgrounds). Despite the fact that the control methodology presented in this section 
can be used in a number of mobile manipulators (e.g., ground and aerial) cooperating with 

Robot Arm-Child Interactions: A Novel Application Using Bio-Inspired Motion Control 245 

humans, herein we focus on the cooperation between a human and a robot arm in 3D 
dimensions. This application requires a fuzzy logic force velocity feedback control to deal 
with unknown nonlinear terms that need to be resolved during the cooperation. The fuzzy 
force logic control and the robot's manipulability are used and applied to the control 
algorithm. The goal of using these combined techniques is to ensure that the design of the 
control system is stable, reliable, and applicable in a wide range of human cooperation 
areas. Herein, we specially consider those areas and settings where the associated 
complexities that humans and their environments impose on the system (robot arm) have a 
significant impact. When interaction occurs, the dynamic coupling between the end-effector 
(i.e., robot arm) and the environment becomes important. In a motion and force control 
scenario, interaction affects the controlled variables, introducing error upon which the 
controller must act. Even though it is usually possible to obtain a reasonably accurate 
dynamic model of the manipulator, the main difficulties occur from the dynamic coupling 
with the environment and similarly with the human. The latter is, in general, impossible to 
model due to time variation. Under such conditions a stable manipulator system could 
usually be destabilized by the environment/ human coupling. Although a number of control 
approaches of robot interaction have been developed in the last three decades the compliant 
motion control can be categorized as the one performing well within the above described 
problems. This is due to the fact that compliant motion control uses indirect and direct force 
control. The main difference between these two approaches is that the former achieves force 
control via motion control without an explicit force feedback loop, while the latter can 
regulate the contact (cooperation) force to a desired value due to the explicit force feedback 
control loop. The indirect force control includes compliance (or stiffness) and impedance 
control with the regulation of the relation between position and force (related to the notion 
of impedance or admittance). The manipulator under impedance control is described by an 
equivalent mass-spring-damper system with the contact force as input. With the availability 
of a force sensor, the force signal can be used in the control law to achieve linear and 
decoupled impedance. Impedance control aims at the realization of a suitable relation 
between the forces and motion at the point of interaction between the robot and the 
environment. This relation describes the robot's velocity as a result of the imposed force(s). 
The actual motion and force is then a result of the imposed impedance, reference signals, 
and the environment admittance. 

It has been found by a number of researchers that impedance control is superior over 
explicit force control methods (including hybrid control). However, impedance control pays 
the price of accurate force tracking, which is better achieved by explicit force control. It has 
also been shown that some particular formulations of hybrid control appear as special cases 
of impedance control and, hence, impedance control is perceived as the appropriate method 
for further investigation related to human-robot arm cooperation. Hybrid motion/force 
control is suitable if a detailed model of the environment (e.g., geometry) is available. As a 
result, the hybrid motion/force control has been a widely adopted strategy, which is aimed 
at explicit position control in the unconstrained task direction and force control in the 
constrained task direction. However, a number of problems still remain to be resolved due 
to the explicit force control in relation to the geometry. 

Control architecture of human robot arm cooperation 

To address the problems found in current human-robot cooperation mechanisms, a new 
control approach is described herein. The approach uses common known techniques and 


Robot Arms 

combines them to maximize their advantages while reducing their deficiencies. Figure 2 
shows the proposed human-mobile robot cooperation architecture that is used in its 
simplified version in human-robot arm cooperation described in Section 3. 
In this architecture the human interacting with the robot arm provides the external forces 
and moments to which the robot must follow. For this, the human and the robot arm are 
considered as a coupled system carrying a physical or virtual object in cooperation. When a 
virtual object is considered, virtual forces are used to represent the desired trajectory and 
velocities that guide the robot in its motion. In this control method the human (or virtual 
force) is considered as the master while the robot takes the role of the slave. To achieve 
cooperation, the changes in the force values, which can be measured via a force/ torque 
(F/T) sensor, must be initialized before starting the cooperation. Subsequently, when the 
cooperation task starts, the measured forces will, in general, be different than the initialized 
values. As a result, the robot will attempt to reduce such differences to zero. According to 
the force changes, the robot determines its motion (trajectory and velocity) to compensate 
the changing in F/T values. Thus, the objective of the control approach is to eliminate 
(minimize) the human effort in the accomplishment of the task. When virtual forces are used 
instead of direct human contact with the robot the need to re-compute the virtual forces is 

Human virtual 
intention force 

Complete motion of th e mobile manipu lator 

Forces (real or virtual) 

to guide 

the robot during 


Robot's ami 

Robot arm position 

Fuzzy Logic 


Velocity model 
-i\ oftherobotic 


robotic arm 



V a = Robot arm motion commands 

V rn = Mobile base motion commands 

Velocity model 
of the robotic 


v n 

robotic vehicle 

Fig. 2. Flow chart of the human-mobile robot cooperation. 
Motion decomposition of the end-effector 

The manipulability (w) of the robot arm captures the relation between the singular point and 
the gripper's end point. Here, the manipulability function of the robot arm (Fig. 2) is used to 
decompose the end-effector's desired motion based on the value of W. First the maximum w 
value of the arm has to be known before it can be used. If the manipulability is small, the 
end point of the robot's gripper is close to the singular point of the manipulator. That is, the 
capability of the robot arm to effectively react to the task while cooperating is reduced. On 
the other hand, if the value of w (manipulability) is large, the end point of the robot is far 

Robot Arm-Child Interactions: A Novel Application Using Bio-Inspired Motion Control 247 

from the its singular point and the manipulator will find it easier to perform cooperating 

Thus the goal is to maintain the manipulability of the arm (and the mobility of the vehicle if 
working with a mobile manipulator) as large as possible, thus allowing the arm (and the 
vehicle when used) to effectively react to the unknown conditions of the environment and 
the cooperation tasks simultaneously. The fuzzy logic controller in Figure 2 is important in 
this case as the fuzzy rules can easily be tuned and used to distribute the robot arm's motion 
based on the manipulability value and the geometry of the environment (e.g., as the robot 
arm overcomes obstacles). 

Control architecture of human-mobile manipulator cooperation 

To finalize this section the cooperation between a human and a mobile manipulator is 
described for completeness. The motion of a mobile base is subject to holonomic or 
nonholonomic kinematics constraints, which renders the control of mobile manipulators very 
challenging, especially when robots work in non-engineered environments. To achieve the 
cooperation between the human and a mobile manipulator, a set of equations to represent 
the changes in forces and torques on the robot's arm caused by the interaction of the mobile 
manipulator on rough terrains is required. These equations can take different forms 
depending on the type or robot systems used (e.g., sensors). However, all forces and torques 
should be a function of the roll, pitch, and yaw angles of the vehicle as it moves. These 
formulations will indicate what portion of the actual sensed force must be considered for 
effective cooperation (i.e., human intention) and which portion is to be neglected (i.e., 
reaction forces due to the terrain or the disturbances encountered by the robot). 
The control system of the manipulator for human-robot cooperation/ interaction was 
designed considering the operational force by the human (operator) and the contact force 
between the manipulator and the mobile robot. The interacting force can be measured by a 
F/T sensor which can be located between the final link of the manipulator and the end- 
effector (i.e., the wrist of the manipulator). The human force and the operational force 
applied by the human operator denote the desired force for the end-effector to move while 
compensating the changing in the forces. The final motion of the manipulator is determined 
by the desired motion by the human force controller. To allow the arm to be more reactive to 
unknown changes (due to the human and the environment) the manipulability of the arm 
must be continuously computed. As the arm approaches the limits of its working 
environment the motion of the mobile manipulator relies more on the mobile base rather 
than the arm. In this way, the arm is able to reposition itself in a state where it is able to 
move reactively. In the experiments used in the next section the mobile base was removed. 
This facilitated the tests while simultaneously enhancing the cooperation. 
The above control mechanism (Fig. 2) not only enhances human-robot cooperation but also 
enhances their interaction. This is due to the fact that the robot reacts not only to the human 
but also to the environmental conditions. This control mechanism was implemented in the 
studies presented in the following section. 

3. Children's relationships with robots 

We designed a series of experiments to explore children's cognitive, affective, and 
behavioral responses towards a robot arm under a controlled task. The robot is controlled 
using a virtual force representing a hypothetical human-robot interaction set a priori. The 

248 Robot Arms 

goal of using such control architecture was to enable the robot to appear dexterous, flexible 
while operating with smooth, yet firm biological type motions. The objective was to enhance 
and facilitate the human-robot cooperation/ interaction with children. 

3.1 Series of experiments 

Experimental setup 

A robot arm was presented as an exhibit in a large city Science Centre. This exhibit was used 
in all the experimental studies. The exhibit was enclosed with a curtain within a 20 by 7 foot 
space (including the computer area). A robot arm was situated on a platform with a chair 
placed .56 meters from its 3D workspace to ensure safety. Behind a short wall of the robot 
arm was one laptop used to run the commands to the robotic arm and a second laptop 
connected to a camera positioned towards the child to conduct observations of children's 
helping and general behaviors. 

All three studies employed a common method. A researcher randomly selected visitors to 
invite them to an exhibit. The study was explained, and consent was obtained. Each child 
was accompanied behind a curtain where the robot arm was set up, with parents waiting 
nearby. Upon entering an enclosed space, the child was seated in front of a robot arm. Once 
the researcher left, the child then observed the robot arm conduct a block stacking task 
(using the bio-inspired motion control mechanisms described in Section 2). After stacking 
five blocks, it dropped the last block, as programmed. 

Design and characteristics of the employed robot arm 

The robot arm used in these experiments was a small industrial electric robot arm having 5 
degrees of freedom where pre-programmed bio-inspired control mechanisms were 
implemented. To aesthetically enhance the bio-inspired motions of the robot the arm was 
"dressed" in wood, corrugated cardboard, craft foam, and metal to hide its wires and metal 
casing. It was given silver buttons for eyes, wooden cut-outs for ears, and the gripper served 
as the mouth. The face was mounted at the end of the arm, creating an appearance of the 
arm as the neck. Gender neutral colors (yellow, black, and white) were given to a non- 
specific gender. Overall, it was decorated to appear pleasant, without creating a likeness of 
an animal, person, or any familiar character yet having smooth natural type motions. 
In addition to these physical characteristics, its behaviour was friendly and familiar to 
children. That is, it was programmed to pick up and stack small wooden blocks. Most 
children own and have played with blocks, and have created towers just as the robot arm 
did. This familiarity may have made the robot arm appear endearing and friendly to the 

The third aspect of the scenario that was appealing to the children was that it was 
programmed to exhibit several social behaviours. Its face was in line with the child's face to 
give the appearance that it was looking at the child. Also, as it picked up each block with its 
grip (decorated as the mouth), it raised its head to appear to be looking at the child before it 
positioned the block in the stack. Such movement was executed by the robot by following a 
virtual pulling force simulating how a human would guide another person when 
collaborating in moving objects. Then, as it lifted the third block, the mouth opened slightly 
to drop the block and then opened wider as if to express surprise at dropping it. It then 
looked at the child, and then turned towards the platform. In a sweeping motion it looked 

Robot Arm-Child Interactions: A Novel Application Using Bio-Inspired Motion Control 249 

back and forth across the surface to find the block. After several seconds it then looked up at 
the child again, as if to ask for help and express the inability to find the block. 

Fig. 3. Five degree of freedom robot arm on platform with blocks. 

The child's reactions to the robot arm were observed and recorded. Then the researcher 
returned to the child to conduct a semi-structured interview regarding perceptions of the 
robot arm. In total, 60 to 184 boys and girls between the ages of 5 to 16 years (M = 8.18 years) 
participated in each study. We administered 15 open-ended questions. Three questions 
asked for general feedback about the arm's appearance, six questions referred to the robot's 
animistic characteristics, and six questions asked about friendship. These data formed the 
basis of three separate areas of study. First, we explored whether children would offer 
assistance to a robot arm in a block stacking task. Second, we examined children's 
perceptions of whether the arm was capable of various thoughts, feelings, and behaviours. 
Finally, the children's impressions about friendship with the robot arm were investigated. 

3.2 Background 

Only a generation ago, children spent much of their leisure time playing outdoors. These 
days, one of the favourite leisure activities for children is using some form of advanced 
technological device (York, Vandercook, & Stave, 1990). Indeed, children spend 2-4 hours 
each day engaged in these forms of play (Media Awareness Network, 2005). Robotics is a 
rapidly advancing field of technology that will likely result in mass production of robots to 
become as popular as the devices children today enjoy. With robotic toys such as Sony's 
AIBO on the market, and robots being developed with more advanced and sensitive 
responding capabilities, it is crucial to ask how children regard these devices. Would 
children act towards robots in a similar way as with humans? Would children prefer to play 
with a robot than with another child? Would they develop a bond with a robot? Would they 
think it was alive? Given that humans are likely to become more reliant upon robots in 

250 Robot Arms 

many aspects of daily life such as manufacturing, health care, and leisure, we must explore 
their psycho-social impact. The remainder of this chapter takes a glimpse on this potential 
impact on children by determining their reactions to a robot arm. Specifically, this section 
will explain whether children would offer assistance to a robot, perceive a robot as having 
humanistic qualities, and would consider having a robot as a friend. 

Study 1 : Assistance to a Robot Arm 

Helping, or prosocial behaviours are actions intended to help or benefit another individual 
or group of individuals (Eisenberg & Mussen, 1989; Penner, Dovidio, Pilavin, & Schroeder, 
2005). With no previous research to guide us, we tested several conditions in which we 
believed children would offer assistance (see Beran et al. 2011). The one reported here 
elicited the most helping behaviors. 
Upon sitting in front of the robot arm the researcher stated the following: 

• Are you enjoying the science centre? What's your favorite part? 

• This is my robot (researcher touches platform near robot arm). What do you think? 

• My robot stacks blocks (researcher runs fingers along blocks). 

• I'll be right back. 

The researcher then exited and observed the child's behaviors on the laptop. A similar 
number of children, who did not hear this introduction, formed the comparison group. As 
soon as children in each group were alone with the robot arm, it began stacking blocks. A 
significantly larger number of children in the introduction group (n = 17, 53.1%), than in the 
comparison group (n = 9, 28.1%), helped the robot stack the blocks, X 2 (l) = 4.15, p = 0.04. 
Thus, children are more likely to offer assistance for a robot when they hear a friendly 
introduction than when they receive no introduction. We interpret these results to suggest 
that the adult's positive statements about the robot modeled to the child positive rapport 
regarding the robot arm, which may have created an expectation for the child to have a 
positive exchange with it. Having access to no other information about the robot, children 
may have relied on this cue to gauge how to act and feel in this novel experience. 
Interestingly, at the end of the experiment, the researcher noted anecdotally that many 
children were excited to share their experience with their parents, asked the parents to visit 
the robot, and explained that they felt proud to have helped the robot stack blocks. Other 
children told their parents that they did not help the robot because they believed that it was 
capable of finding the block itself. Overall, we speculate that the adult's display of positive 
regard towards the robot impacted children's offers of assistance towards it. 

Study 2: Animistic impressions of a Robot Arm 

Animism as a typical developmental stage in children has been studied for over 50 years, 
pioneered by Piaget (1930; 1951). It refers to the belief that inanimate objects are living. This 
belief, according to Piaget, occurs in children up to about 12 years of age. The disappearance 
of this belief system by this age has been supported by some studies (Bullock, 1985; Inagaki 
and Sugiyama, 1988) but not others (Golinkoff et al., 1984; Gelman and Gottfried, 1983). 
Nevertheless, the study of animism is relevant in exploring how children perceive an 
autonomous robot arm. 

Animism can be divided and studied within several domains. These may include cognitive 
(thoughts), affective (feelings), and behavioural (actions) beliefs, known as schemata. In 

Robot Arm-Child Interactions: A Novel Application Using Bio-Inspired Motion Control 251 

other words, people possess schemata, or awareness, that human beings have abilities for 
thinking, feeling, and acting. More specifically, thinking abilities may include memory and 
knowledge; feeling abilities include pleasant and unpleasant emotions; and behaviour 
abilities can refer to physical abilities and actions. Melson et al. (2009) provide some initial 
insights into several of these types of beliefs children hold towards a robotic pet (Sony's 
AIBO). Also, Melson et al. (2005) found that many children believed that such a robot was 
capable of the feelings of embarrassment and happiness, as well as recognition. Additional 
evidence of animism towards a robot was obtained by Bumby and Dautenhahn (1999) who 
reported that children may include human characteristics about robots in stories they create. 
The most recent study on animism presents surprising insights about animism. A team of 
researchers from the University of Washington's Institute for Learning and Brain Sciences [I- 
LABS, 2010] found that, "babies can be tricked into believing robots are sentient". The 
researchers used a remote-controlled robot in a skit to act in a friendly manner towards its 
human (i.e., adult) counterpart. When the baby was left alone with the robot, in 13 out of 16 
cases the baby followed the robot's gaze, leaving researchers to conclude that the baby 
believed it was sentient. We extend these insightful findings of animism to children's 
cognitive, affective, and behavioural beliefs about a robot arm in the present study. 
Responses to questions about the arm's appearance and animistic qualities were coded for 
this study. Two raters were used to determine the reliability of the coding, with Cohen's 
Kappa values ranging from 0.87 to 0.98, with a mean of 0.96 indicating very good inter-rater 
agreement. The majority of children identified the robot as male, and less than a quarter of 
the children identified it as female. One child stated the robot was neither, and about 10% 
did not know. The child's sex was not related to their response. About a third of the children 
assigned human names to the robot such as 'Charlie'. About a third gave names that refer to 
machines, such as 'The Block Stacker'. A pet name was rarely assigned, such as 'Spud', or a 
combined human-machine type name 'Mr. Robot'. When asked about their general 
impressions of the robot, a large majority gave a positive description, such as 
cool/ awesome, good/neat, nice, likeable, interesting, smart, realistic, super, fascinating, and 
funny. Two children reported that the robot had a frightening appearance, and three 
children thought it looked like a dog. Another 17 did not provide a valid response. 
Regarding its cognitive characteristics, more than half of the children stated the robot had 
recognition memory due to the ability to see their face, hair, and clothes; and that the robot 
was smart and had a brain (see Table 1). Other children provided a mechanical reason by 
stating it had a memory chip, camera, or sensors, or may have been programmed. Over a 
third of the children stated the robot could not remember them, for various reasons shown 
in the table. Children's perceptions about the robot's cognitive abilities in regards to 
knowledge are also shown in Table 1. About half of the children thought the robot did not 
have this capability, due to reasons such as not having a brain or interactions with them. 
Almost a third indicated that they believed the robot does know their feelings for various 
reasons such as from seeing the child and being programmed with this ability. 
Regarding affective characteristics, the majority of children thought that the robot liked 
them, as shown in Table 2. A few children believed that the robot did not like them. 
Similarly, the majority of children reported that they thought the robot would feel left out if 
they played with a friend. Over a quarter of the children stated the robot would not feel left 
out, but provided explanations that would seemingly protect the robot from harm. 


Robot Arms 

Robot can remember you 

Robot knows your feelings 


97 (52.7%) 


54 (29.3%) 

Can see me 


Can see me 


Has memory chip, sensors 


Has memory chip, sensors 


Smart, has brain 


Smart, has brain 


If has a brain 


Do not know why 


If short duration 


Not coded 


If programmed 


Do not know why 


Not coded 



68 (37.0%) 


103 (56.0%) 

No brain, eyes, or memory 


No brain, eyes, or memory 


Too many people to 


No interaction with me 


Robot does not like me 


If not programmed 


If no brain 


Do not know why 


If long duration 


Not coded 


If not programmed 


Do not know why 


Not coded 


Do not know 

19 (10.3%) 

Do not know 

27 (14.7%) 

Table 1. Number and percentage of children reporting cognitive features of robot (N = 184). 

Robot likes you 

Robot feels left out 


118 (64.0%) 


127 (69.0%) 

Looks/ smiles at me, friendly 


No one to play with 


I was nice/ did something 


Hurt feelings 


Did not hurt me 


I would include robot 


It had positive intentions 


Not fair 


Do not know why 


Do not know why 


Not coded 


Not coded 



16 (8.7%) 


53 (28.8%) 

Ignored me/ didn't let me 


No thoughts /feelings 


No thoughts/feelings 


Would include robot 


Do not know why 


Does not understand 


Not coded 

Do not know why 


Not coded 

Do not know 

50 (27.3%) 

Do not know 

4 (2.2%) 

Table 2. Number and percentage of children reporting affective features of robot (N = 184). 

Robot Arm-Child Interactions: A Novel Application Using Bio-Inspired Motion Control 


In regards to its behavioral characteristics (Table 3), more than a third of the children stated 
the robot was able to see the blocks, with just over half of the children indicating that the 
robot could not see the blocks. A higher endorsement of the robot's ability to show action is 
evident in the table. That is, a large majority stated the robot could play with them, and even 
provided a variety of ideas for play. Examples include block building, and Lego®, catch with 
a ball, running games, and puzzles. 

Robot sees blocks 

Robot plays with you* 


77 (41.8%) 


154 (83.7%) 

Has eyes 






Ball game 


Sensors, camera 


Running game 




Board game 





Do not know why 


Do not know why 


Not coded 

Not coded 



94 (51.1%) 


25 (13.6%) 

Eyes not real 


Physical limitation 


Sensors, camera 




Missed a block 


Do not know why 




Not coded 


Do not know why 


Not coded 


Do not know 

13 (7.1%) 

Do not know 

5 (2.7%) 

Note* Many children provided more than one response. 

Table 3. Number and percentage of children reporting behavioral features of robot (N = 184). 

To further determine whether children considered the robot to be animate or inanimate, we 
analyzed the pronouns children used when talking about the robot arm. Almost a quarter of 
the children used the pronoun "it" in reference to the robot, another quarter stated "he", and 
half used both. 

In summary, children seemed to adopt many animistic beliefs about the robot. Half thought 
that it would remember them, and almost a third thought it knew how they were feeling. 
Affective characteristics were highly endorsed. More than half thought that the robot liked 
them and that it would feel rejected if not played with. In their behavioral descriptions, 
more than a third thought it could see the blocks, and more than half thought the robot 
could play with them. It is evident that children assigned many animistic abilities to the 
robot, but were more likely to ascribe affective than cognitive or behavioral ones. There was 
additional evidence of human qualities according to the names children gave it, their 
descriptions of it, and the pronouns they used to reference it in their responses. These 
animistic responses, moreover, were more apparent in younger than older children. 
Although some responses suggest that children believed the robot held human 
characteristics because of programming and machine design, the majority of statements 
referred to human anatomy (e.g., eyes, facial features, and brain), emotions, and intentions. 
We explain these findings in two ways. First, the robot arm presented many social cues. 


Robot Arms 

That is, the eyes were at the same eye level as the children's, giving the impression of 
'looking' at child, and it returned to this position many times while scanning for the block. 
Children may have interpreted this movement as expression of interest and closeness, which 
is one of the reactions to frequent eye contact among people (Kleinke, 1986; Marsh, 1988). 
Second, children may have projected their own feelings, thoughts, and experiences onto the 
robot arm, which Turkle (1995) has reported may occur with robots. This was particularly 
evident in the surprising finding that so many children believed that the robot would feel 
rejected and lonely if not included in play, as well as that the arm could engage in forms of 
play that clearly it could not (e.g., running). Third, children may have lacked knowledge of 
terms and principles to explain the robot's actions, thereby relying on terms that express 
human qualities such as 'remembering', 'knowing', and 'liking'. Fourth, because the arm 
moved autonomously, children may have developed the impression that it has intentions 
and goals, as is a typical reaction to any independently moving object (Gelman, 1990; 
Gelman and Gottfried, 1996; Poulin-Dubois and Shultz, 1990). 

Study 3: Children's impressions of friendship towards a Robot Arm 

Friendships are undoubtedly important for childhood development, and, as such, set the stage 
for the development of communication skills, emotional regulation, and emotional 
understanding (Salkind, 2008). In this study, and given the animistic responses obtained in the 
previous study, we set out to determine the extent to which children would hold a sense of 
positive affiliation, social support, shared activities, and communication towards a robot; all of 
which exemplify friendship. In addition, we questioned whether children would share a secret 

Robot can cheer you up 

Robot can be your friend 


145 (78.8%) 


158 (85.9%) 

Perform action for me 




Perform action with me 


Being or doing things 


Cheerful appearance 




Connects with me 


Knows me 


Help me 




Do not know why 




Not coded 





27 (14.7%) 

Friend to robot 


Limited abilities 


Do not know why 


Does not like me 


Not coded 



19 (10.3%) 

Limited mobility 


Limited communication 


No familiarity 


No brain, feelings 


Do not know why 


Do not know why 


Not coded 


Not coded 


Do not know 

12 (6.5%) 

Do not know 

7 (3.8%) 

Table 4. Number and percentage of children reporting positive affiliation (N = 184). 

Robot Arm-Child Interactions: A Novel Application Using Bio-Inspired Motion Control 


with a robot, as this behavior may also signify friendship (Finkenauer, En gels & Meeus, 2002). 
As shown in Table 4, more than three quarters of the children stated that the robot could 
improve their mood, with reasons varying from its actions to its appearance. Moreover, 
more than three quarters stated the robot could be their friend. Many reasons were given for 
this possibility. They included enjoying activities together, helping each other, kindness, 
likeability, and shared understanding. 

According to Table 5, the majority of children stated they would talk to the robot and share 
secrets with it. Most children had difficulty explaining their reasons for their answers. 
Rather, they provided answers that described what they would talk about, such as what to 
play together. Interestingly, many children stated that they liked the robot and wanted to 
spend time becoming acquainted. This desire for a greater connection to the robot is also 
exemplified in their responses to sharing secrets. More than a third of the children stated 
they would tell the robot a secret. Some children (n = 24) stated that they thought it was 
wrong to tell secrets, suggesting that of those children who would generally tell secrets (n = 
160), half of them (n = 84, 52.50%) would tell a robot. The most frequent reason given was 
because they believed that the robot would not share it - seemingly because the robot arm 
could not speak. Many of them also stated, however, that they considered the robot arm to 
be friendly. 

Talk to robot 

Tell robot secrets 


124 (67.4%) 


84 (45.7%) 

I like the robot 


Robot will keep secret 


To get to know each 


Friendship with robot 


Robot has mouth 


Positive response to secret 


If robot could talk 




Gave examples 


Do not know why 


Do not know why 


Not coded 


Not coded 



53 (28.8%) 


92 (50.0%) 

Robot cannot talk 


Secrets are wrong 


Robot cannot hear 


Robot has limitations 


Not human 


Robot not trustworthy 


Looks unfriendly 


Robot is not alive 


Do not know why 


Do not know why 


Not coded 


Not coded 


Do not know 

7 (3.8%) 

Do not know 

8 (4.3%) 

*Some children provided more than one reason 

Table 5. Number and percentage of children reporting communication (N = 184). 

The majority of children responded affirmatively to questions about affiliation, receiving 
support, communicating, and sharing secrets, which typically characterize friendship. 
Regarding affiliation, almost two thirds of the children thought the robot liked them, and 

256 Robot Arms 

many explained that it was because the robot appeared friendly. Children also attributed 
positive intentions to the robot, likely because it was moving independently and engaging 
in a child friendly task. More than three quarters of the children did believe that the robot 
could offer them support. The action of stacking blocks was often explained as a means of 
providing this support, perhaps to distract and entertain the child. A large majority of 
children stated that they would play with the robot in a variety of games. It is not surprising 
that many of them suggested building with blocks, considering that they had just observed 
this activity. Finally, about two thirds of the children stated they would talk to the robot and 
more than a third stated that they would share secrets. Again, these results suggest that 
children are willing to develop a bond with the robot. 

Many children in our study stated that they would not engage in these friendship-behaviors 
with a robot and explained that the robot did not have the capabilities to do so. Reasons for 
these different perceptions of the robot have not been explored in the research but may 
plausibly include variation in children's knowledge of the mechanics of robots. In addition, 
a considerable proportion of children did not or could not provide an answer to the 
questions about friendship. It is possible that children were unable to differentiate human 
from robot characteristics, lacked sufficient understanding about the mechanics of robots, or 
were generally confused about the robot's abilities. Our use of terms in the interview, such 
as whether the robot would 'feel left out', describe human characteristics and may have 
mislead children into positively responding. Clearly, the results raise many questions for 
research, not the least of which is whether children actually do develop a friendship with a 
robot. Over time, and as a result of interactions with robots, children may develop a new 
system or schema of understanding, and subsequent vocabulary to articulate their sense of 
friendship with a robot, that is likely distinct from their friendships with children. 

4. Implications for robot design 

The fact that so many children ascribed life characteristics to the robot suggests that they 
have high expectations of them and are willing to invite them into their world. This presents 
a challenge to robot designers to match these expectations, if the purpose of the robot is to 
garner and maintain interest from children. Children may be primed for these interactions. 
In fact, children may become frustrated when a robot does not respond to their initiations 
and may actually persevere at eliciting a response (Weiss et al., 2009). Therefore, the robot 
may not need to be programmed to respond in an identical fashion to a specific initiation, as 
humans certainly do not, which may actually increase the child's engagement with a robot. 
This principle is well known as variable ratio reinforcement according to behaviourism 
learning theory (Skinner, 1969). Of course, children may become discouraged if the robot's 
response is erratic. Instead, we propose that a high, but not perfectly predictable response to 
the child's behaviours will lead to the longest and most interesting interactions. 
In addition, our studies suggest that children can develop a collaborative relationship with a 
robot when playing a game together. This gives some suggestion of the nature of the 
relationship children may enjoy with a robot: one that allows give and take (Xin & Sharlin, 
2007). This may enhance a child's sense of altruism and, hence, increase engagement with it. 
It is, thus, recommended that developers of such robots consider designing them to not only 
offer help, but be able to receive it. 

Robot Arm-Child Interactions: A Novel Application Using Bio-Inspired Motion Control 257 

4.1 Limitations 

The studies and tests reported in this chapter have certain limitations that one must consider 
when interpreting the results. The tests and associated observations made during the study 
can be reproduced by using a variety of robot arms and even include mobile manipulators 
from where more detailed children-robot interaction studies can be made. Although the bio- 
inspired control mechanism used in this study worked well, tests using such control 
approaches should be performed on other robot types including humanoids and mobile 
robots. Such control architecture should also be tested in physical children-robot interaction 
to determine its suitability towards enabling seamless active engagement between children 
(humans) and robots. 

Robot arms have indeed changed from their original industrial and automotive applications 
in the 1960s. Our studies show that children are ready to accept them as social objects for 
sharing personal information, offering mutual support and assistance, and regarding them 
as human in various ways. In the near future, we expect that humans will not only 
frequently and directly interact with and rely on robot arms and robots of diverse types for 
daily activities, but perhaps treat them and regard them as possibly human. Our studies 
cannot begin to address the numerous complex questions about the nature of the 
interactions people will have with robots. We offer a glimpse, however, of children's 
willingness to do so. Overall, the results are rather surprising given that the robot arm did 
not speak, performed only one task, and did not initiate physical interaction with the child. 
Are children merely responding to the robot arm as if it is a fancy puppet, and they are 
presenting their imagination in their responses? Perhaps, but regardless of the explanation, 
children in these studies demonstrated overwhelmingly their predisposition towards active 
engagement for bio-inspired motion control. 

5. Acknowledgments 

We give special thanks to the TELUS World of Science - Calgary for collaborating with us. 
This research would not have been possible without their support. 

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