JAMRIS 2011 Vol 5 No 1

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JOURNAL of AUTOMATION, MOBILE ROBOTICS& INTELLIGENT SYSTEMS

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Hybrid Intelligent Systems for Optimizationand Pattern Recognition - Part II

Guest Editors: Oscar Castillo and Patricia Melin

Mario I. Chacon-Murguia, Rafael Sandoval-Rodriguez,Cynthia P. Guerrero-Saucedo

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Application of the bee swarm optimization BSO to theknapsack problem

2

JOURNAL of AUTOMATION, MOBILE ROBOTICS& INTELLIGENT SYSTEMSVOLUME 5, N° 1, 2011

CONTENTS

REGULAR PAPER

Computational methods for investigation of stabilityof models of 2D continuous-discrete linear systems

Interaction management between social agents andhuman

Bayesian model for multimodal sensory informationfusion in humanoid

Real-time coordinated trajectory planning andobstacle avoidance for mobile robots

A framework for unknown environment manipulatormotion planning via model based realtime rehearsal

Intelligent leader-follower behaviour for unmannedground-based vehicles

Robotic rehabilitation of stroke patients using anexpert system

Considerations on coverage and navigation in wirelessmobile sensor network

Mikołaj Busłowicz

Abdelhak Moussaoui, Alain Pruski, Brahim Cherki

Wei Kin Wong, Chu Kiong Loo, Tze Ming Neoh, Ying WeiLiew, Eng Kean Lee

Lucas C. McNinch, Reza A. Soltan, Kenneth R. Muske,Hashem Ashrafiuon, James C. Peyton

Dugan Um, Dongseok Ryu

Pouria Sadeghi-Tehran, Javier Andreu, Plamen Angelov

Pradeep Natarajan, Arvin Agah, Wen Liu

Tadeusz Goszczynski, Zbigniew Pilat

3

8

16

23

30

36

47

58

68

64

77

85

95

101

Abstract:

1. Introduction

2. Preliminaries and formulationof the problemThe state equation of the Fornasini-Marchesini type

model of a continuous-discrete linear system has the form

[11]

The problem of asymptotic stability of models of 2D

continuous-discrete linear systems is considered. Compu-

ter methods for investigation of asymptotic stability of the

Fornasini-Marchesini type and the Roesser type models,

are given. The methods proposed require computation of

the eigenvalue-loci of complex matrices. Effectiveness

of the stability tests are demonstrated on numerical

examples.

Keywords: continuous-discrete system, hybrid system,

linear system, stability, computational methods.

In continuous-discrete systems both continuous-time

and discrete-time components are relevant and interacting

and these components can not be separated. Such systems

are called the hybrid systems. Examples of hybrid systems

can be found in [6], [8], [9], [16]. The problems of dyna-

mics and control of hybrid systems have been studied in

[5], [6], [16].

In this paper we consider the continuous-discrete

linear systems whose models have structure similar to the

models of 2D discrete-time linear systems. Such models,

called the 2D continuous-discrete or 2D hybrid models,

have been considered in [11] in the case of positive

systems.

The new general model of positive 2D hybrid linear

systems has been introduced in [12] for standard and in

[13] for fractional systems. The realization and solvability

problems of positive 2D hybrid linear systems have been

considered in [11], [14] and [15], [17], respectively.

The problems of stability and robust stability of 2D

continuous-discrete linear systems have been investigated

in [1-4], [7], [18 -20].

The main purpose of this paper is to present compu-

tational methods for investigation of asymptotic stability

of the Fornasini-Marchesini and the Roesser type models

of continuous-discrete linear systems.

The following notation will be used: - the set of real

numbers, = [0, ], - the set of non-negative integers,

- the set of real matrices and = ,

|| (·) || - the norm of (·), { } - -th eigenvalue of

matrix .

�

� �

� � �

�

+ +

+ +

Z

n×m

x x X i

X

n×m n n�1

i

x t,i A Bu

i Z t

A

B

x i x t

x t,i t,i

H s,z szI s z

w s,z H s,z

s z

u t,i

H s,z

�

� �

�

�

�

( +1) = +

, , (1)

where = , and ,

, ,

The Fornasini-Marchesini type model (1) is

called asymptotically stable (or Hurwitz-Schur stable) if

for 0 and bounded boundary conditions

(0, ), ( ,0), , , (2)

the condition lim || ( )|| = 0 holds for .

The characteristic matrix of the model (1) has the form

( ) = . (3)

The characteristic function

( ) = det ( ) = det[ ] (4)

of the model (1) is a polynomial in two independent

variables and , of the general form

(5)

The state equation of the Roesser type model of a con-

tinuous-discrete linear system has the form [11]

(6)

where

are the vertical and the horizontal vectors, respectively,

is the input vector and ,

, , , , .

The boundary conditions for (6) are as follows

The Roesser type model (6) is called asym-

ptotically stable (or Hurwitz-Schur stable) if for ( ) 0

and bounded boundary conditions (7) the conditions

lim and hold for

.

The characteristic matrix of the model (6) has the form

( ) =

0 1 2

+ +

0

1 2

+ +

0 1 2

0 1 2

11 12

21 22 1 2

+ +

x t,i A x t,i A x t,i+ t,i

x t,i x t,i t x t,i u t,i

A A

u t,i

i Z x t t

A A A

szI A sA zA

w s,z a s z a

x t,i x t,i t x t,i x t,i

u t,i A A

A A B B

x t x t t x i x i i i

x t,i x t,i t,i

( ) ( )+ ( 1)+ ( ),

( ) ( )/ ( ) , ( ) ,

( )

, ( ,0)

( ) = , = 1.

( ) = ( )/ , ( ) , ( )

( )

( ,0), ( ,0), , (0, ), (0, ), 1, Z . (7)

|| ( )|| = 0 lim || ( )|| = 0

(8)

�

�

� � � � � �

� � � �

�

� � � �

� �

� � � � � � �

� � � � �

� � � � � � � � �

� � �

�

n m

n×m n×m

k j

h h h v

m n ×

n ×n × × n ×m ×m

h v h v

h v

Definition 1.

Definition 2.

i,t�

n

n

kj nn

i,t i,t

1

� �

.

n n

n

n n n n n

1 2

1

1 2 2 1 2 2 1 2

COMPUTATIONAL METHODS FOR INVESTIGATION OF STABILITY OF MODELS

OF 2D CONTINUOUS-DISCRETE LINEAR SYSTEMS

Mikołaj Busłowicz

Received 9 September 2010; accepted 20 September 2010.th th

Journal of Automation, Mobile Robotics & Intelligent Systems VOLUME 5, N° 1 2011

Articles 3

n

k=0

n

j=0

( )x t,i� h x t,ih ( )A11

sI A 11n1

B1A12

A12

=

t i�� + +, Z ,

+ u t,i( ),x t,i+v ( 1) x t,iv ( )A21

A21

B2A22

zI A 22n2

Using the rules for computing the determinant of block

matrices [10], we obtain that the characteristic function

of the Roesser type model can be

computed from one of the following equivalent formulae

( ) = det( – )det( ( ) ),

(9a)

The characteristic function of the Roesser type model

can be written in the form

From [1], [7] we have the following theorem.

The Fornasini-Marchesini type model (1)

with characteristic function (4) (or the Roesser type model

(6) with characteristic function (9)) is asymptotically

stable if and only if

( ) 0, Re 0, | z | 1. (11)

The polynomial satisfying condition (11) is

called continuous-discrete stable (C-D stable) or Hurwitz-

Schur stable [1].

The main purpose of this paper is to present com-

putational methods for checking the condition (11) of

asymptotic stability of the Fornasini-Marchesini type

model (1) and the Roesser type model (6) of continuous-

discrete linear systems.

w s, z zI A sI A A zI A A

w s, z s

-122 11 12 22 21

11 22 21 11 12

1 2 0

1

1

– – –

( ) = det( – )det( – – ( – ) }.

(9b)

( ) = , = 1. (10)

( )

The condition (11) is equivalent to the follo-

wing two conditions

( ) 0, Re 0, [0, 2 ], (12)

( ) 0, | z | 1, [0, ). (13)

From [7] it follows that (11) is equivalent to the

conditions

( ) 0, Re 0, | z |= 1, (14)

( ) 0, Re = 0, | z | 1. (15)

It is easy to see that conditions (14) and (15) can be

written in the forms (12) and (13), respectively.

From (4) for we have

( ) = det[ ( – ) – – ]. (16)

The condition (12) for the Fornasini-Mar-

chesini type model (1) with ± holds if and only

if all eigenvalues of the complex matrix ( ) have

negative real parts for all [0, 2 ], where

w s, z sI A zI A A sI A A

w s,z a s z a

w s, z

w s, e s

w jy, z y

w s, z s

w s, z s

z = e

w s, e s I e A A e A

A I

S e

-1

� �

� ��

� � � �

�

�

�

� � �

kj

n

n

k j

j

j

j j j

FM j

Theorem 1.

�

3. Solution of the problemTheorem 2.

Proof.

Lemma 1.

�

�

� � �

�

�

3.1. Asymptotic stability of the Fornasini-

Marchesini type model

w s, z H s, z( ) = det ( )

n n n

n n n

n n

2 1 2

1 2 1

1 2

( ) = ( – ) ( + ). (17)

If ± then the matrix – is non-singular

for all [0, 2 ] and

[ ( – ) – – ] = [ – ][ – ( )], (18)

where ( ) has the form (17).

From (16) and (18) it follows that

( ) = det( – )det( – ( )). (19)

This means that for ± the eigenvalues of the

matrix ( ) are the roots of the polynomial ( ).

The condition (13) for the Fornasini-Marche-

sini type model (1) with holds if and only if all

eigenvalues of the complex matrix ( ) have absolute

values less than one for all y 0, where

( ) = ( – ) ( + ). (20)

Substituting in (4) one obtains

( ) = det[ ( – ) – – ]. (21)

If then the matrix – is non-singular for all

y 0 and

[ ( – ) – – ] = [ – ][ – ( )], (22)

where ( ) is defined by (20).


( ) = det( – )det( – ( )). (23)

Hence, if then the eigenvalues of the matrix ( )

are the roots of the polynomial ( ).

The Fornasini-Marchesini type model (1) with

± and is asymptotically stable if and only

if the conditions of Lemmas 1 and 2 hold, i.e.

Re { ( )} < 0, [0, 2 ], = 1,2,..., , (24)

and

| { ( )}|< 1, 0, = 1,2,..., , (25)

where the matrices ( ) and ( ) have the forms

(17) and (20), respectively.

It follows from Theorem 2 and Lemmas 1 and 2.

From (17) for = 0 and = we have

(1) = ( – ) ( + ), (26a)

(–1) = (– – ) (– + ). (26b)

From the theory of matrices it follows that if (–1) det

(1) 0 then not all eigenvalues of the matrix (26a)

have negative real parts. Similar condition holds for the

S e I e A A e A

A I I e A

s I e A A A e I e A s S e

S e

w s,e I e A sI S e

A I

S e w s,e

A I

S jy

S jy jyI A A jyA

s = jy

w jy , z z jyI A A jyA

A I jyI A

z jyI A A jyA jyI A z S jy

S jy

w jy , z jyI A zI S jy

A I S jy

w jy , z

A I A I

S e i n

S jy y i n

S e S jy

S I A A A

S I A A A

S

1 1 2 0

1 1

1 0 2 1 1

1

1 1

1

1

2

2

2 2 0 1

2 0 1

2 1

2 0 1 2 2

2

2 2

2 2

1 2

1

2

1 2

1 1 2 0

1 1 2 0

1

FM j j j

j

j j j FM j

FM j

j j FM j

FM j j

FM

FM

FM

FM

FM

FM

FM j

FM

FM j FM

FM

FM

n

FM

� � �

�

� � � �

�

� � �

� �

�

�

n

n n

n n

n n

n

n

n

n

n n

n n

n n

n

n n

i

i

n

n

-1

-1

-1

-1

Proof.

Lemma 2.

Proof.

Theorem 3.

Proof.

�

� � �

�

�

�

�

� �

� ��

� �

� � �

�

�

�

�

Journal of Automation, Mobile Robotics & Intelligent Systems

Articles4

VOLUME 5, N° 1 2011

n1

k=0

n2

j=0

From (8) for we have

( ) = det (29)

The condition (12) for the Roesser type model

(6) with ± holds if and only if all eigenvalues of the

complex matrix ( ) have negative real parts for all

[0, 2 ], where

( ) = + ( – ) . (30)

If ± then the matrix – is non-

singular for all [0, 2 ] and from (9a) it follows that

( ) = det( – )det( – ( )), (31)

where ( ) has the form (30). This means that for

± the eigenvalues of the matrix ( ) are the roots

of the polynomial ( ).

The condition (13) for the Roesser type model

(6) with holds if and only if all eigenvalues of the

complex matrix ( ) have absolute values less than one

for all y 0, where

( ) = + ( – ) . (32)

If then the matrix – is non-singular

for all y 0. From (9b) for we have

( ) = det( – )det( – – ( – ) }.

(33)


( ) = det( – )det( – ( ), (34)

where ( ) is defined by (32).

If then the eigenvalues of the matrix ( ) are

the roots of the polynomial ( ).

The Roesser type model (6) with ± and

is asymptotically stable if and only if the

conditions of Lemmas 3 and 4 hold, i.e.

Re { ( )} < 0, [0, 2 ], = 1,2,..., , (35)

and

| { ( )}| < 1, 0, = 1,2,..., , (36)

where matrices ( ) and ( ) have the forms (30) and

(32), respectively.

The proof follows from Theorem 2 and Lemmas 3

and 4.

From (30) for = 0 and = it follows that

(1) = + ( – ) , (37a)

(–1) = + (– – ) . (37b)

From the above and theory of matrices we have the

following remark.

3.2. Asymptotic stability of the Roesser type model

z= e

w s, e

A I

S e

S e A A I e A A

A I I e A

w s,e I e A sI S e

S e

A I S e

w s,e

A I

S jy

S jy A A jyI A A

A I jyI A

s = jy

w jy, z jyI A zI A A jyI A A

w jy, z jyI A zI S jy

S jy

A I S jy

w jy, z

A I

A I

S e i n

S jy y i n

S e S jy

S A A I A A

S A A I A A

j

j

R j

R j j

j

j j R j

R j

R j

j

R

R

R

R

R

R j

R

R j R

R

R

�

�

�

� �

�

� � �

�

�

�

�

�

Lemma 3.

Proof.

Lemma 4.

Proof.

Theorem 4.

Proof.

22 2

1

1 11 12 2 22 21

22 2 2 22

2 22 1 1

1

22 2 1

11 1

2

2 22 21 1 11 12

11 1 1 11

1 11 2 22 21 1 11 12

1 11 2 2

2

11 1 2

22 2

11 1

1 1

2 2

1 2

1 11 12 2 22 21

1 11 12 2 22 21

�

� � �

�

� � �

�

�

�

�

�

�

� ��

� �

� � �

n

n

n n

n n

n

n

n

n n

n n n

n n

n

n

n

i

i

n

n

-1

-1

-1

-1

-1

�

�

�

matrix (26b). Hence, we have the following remark.

Simple necessary condition for asymptotic

stability of the Fornasini-Marchesini type model (1) with

± has the form

(–1) det( – )det( + ) > 0 (27a)

(–1) det(– – )det(– + ) > 0. (27b)

Consider the Fornasini-Marchesini type

model (1) with the matrices

(28)

It is easy to check that the necessary conditions (27) hold.

Computing eigenvalues of the matrices ( ), [0,

2 ], and ( ), [0, 100], one obtains the plots shown

in Figures 1 and 2. It is easy to check that eigenvalues of

( ) remain in the unit circle for all >100.

From Figures 1 and 2 it follows that the conditions (24)

and (25) of Theorem 3 are satisfied and the system is

asymptotically stable.

( ), [0, 2 ].

( ), [0, 100].

Remark 1.

Example 1.

A I

I A A A

I A A A

S e

S jy y

S jy y

Fig. 1. Eigenvalues of the matrix S e

Fig. 2. Eigenvalues of the matrix S jy y

1

1 2 0

1 2 0

1

2

2

1

2

�

� �

� �

� � �

�

n

n

n

n

n

FM j

FM

FM

FM j

FM

�

�


Articles 5

VOLUME 5, N° 1 2011

– 0.4

– 0.4

– 0.51

– 1.8

0.10

0

0

0

0.1

00.2

– 0.4

0.10.5

0

– 0.4A0 = ,

A2 = ,

A1 = ,

0

0

0– 0.1

0

0.2– 0.1

– 0.7

– 0.2

sI An1 11– – A12

– A21 I e An2 22j� –

Remark 2.

Example 2.

Simple necessary condition for asymptotic

stability of the Roesser type model (6) with ± are as

follows: (–1) det (1) > 0 and (–1) det (–1) > 0.

Consider the Roesser type model (6) with the

matrices

(38)

Eigenvalues of the matrices ( ), [0, 2 ] and

( ), [0, 100], are shown in Figures 3 and 4. If is easy

to check that eigenvalues of ( ) remain in the unit circle

for all >100.

From Figures 3 and 4 it follows that the conditions (35)

and (36) of Theorem 4 are satisfied and the system is

asymptotically stable.

( ), [0, 2 ].

( ), [0, 100]

Computational methods for investigation of asymp-

totic stability of the Fornasini-Marchesini type model (1)

(Theorem 3) and the Roesser type model (6) (Theorem 4)

of continuous-discrete linear systems have been given.

These methods require computation of eigenvalue-loci of

A I

S S

S e

S jy y

S jy

y

Fig. 3. Eigenvalues of the matrix S e

Fig. 4. Eigenvalues of the matrix S jy y .

22 2

1 1

1

2

2

1

2

�

� � �

�

� � �

�

nn R n R

R j

R

R

R j

R

�

�

4. Concluding remarks

complex matrices (17) and (20) for the Fornasini-Mar-

chesini type model and complex matrices (30) and (32) for

the Roesser type model.

- ³

Faculty of Electrical Engineering, ul. Wiejska 45D,

15-351 Bia³ystok, Poland. E-mail: [email protected].

ACKNOWLEDGMENTS

The work was supported by the Ministry of Science and High

Education of Poland under grant No. N N514 1939 33.

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, vol. 3, 2003, pp. 682-685.

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[3] M. Bus³owicz, “Stability and robust stability conditions

for general model of scalar continuous-discrete linear

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vol. 56, no. 2, 2010, pp. 133-135.

[4] M. Bus³owicz, “Improved stability and robust stability

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[10] T. Kaczorek,

. WNT: Warszawa, 1998, p. 70.

(in Polish)

[11] T. Kaczorek, “ ”, Springer-

Verlag: London, 2002.

[12] T. Kaczorek, “Positive 2D hybrid linear systems”,

, vol. 55, no. 4, 2007, pp. 351-358.

[13] T. Kaczorek, “Positive fractional 2D hybrid linear

systems”, , vol. 56, no. 3, 2008, pp.

273-277.

[14] T. Kaczorek, “

”, COMPEL, vol. 27, no. 3, 2008,

pp. 613-623.

[15] T. Kaczorek, V. Marchenko, £. Sajewski, “Solvability of

AUTHOR

Miko³aj Bus³owicz

References

Bia ystok University of Techno-

logy,

Proc. Int. Symp. on Circuits

and Systems

Bull. Pol. Ac.: Tech.

Measurement Automation and Monitoring

Measurement Automation and

Monitoring

Int. J. Control

Int. J. Appl. Math. Comput. Sci.

Proc. of the IEEE

Techn. Report, Dept. of

Electrical and Computer Eng.

H. Unbehauen (Ed.), Encyklopedia of Life

Support Systems

Vectors and Matrices in Automatics

and Electrotechnics

Positive 1D and 2D Systems

Bull.

Pol. Ac.: Tech.

Bull. Pol. Ac.: Tech.

Realization problem for positive 2D

hybrid systems


Articles6

VOLUME 5, N° 1 2011

– 1

– 0.5

– 0.5

– 0.5

0

– 1

0

0.8

A11 = ,

A12 = ,

A12 = ,

A22 = .

0.1

0

– 1

0.2

– 5

– 1

0

0.4

2D hybrid linear systems comparison of the different

methods”, , vol. 2, no. 2,

2008, pp. 59-66.

[16] D. Liberzon, “ ”,

Birkhauser: Boston, 2003.

[17] £. Sajewski, “Solution of 2D singular hybrid linear

systems”, , vol. 38, no. 7/8, 2009, pp. 1079-

1092.

[18] Y. Xiao, “Stability test for 2-D continuous-discrete

systems”. In:

, vol. 4, 2001, pp. 3649-3654.

[19] Y. Xiao, ”Robust Hurwitz-Schur stability conditions

of polytopes of 2-D polynomials”, In:

, vol. 4, 2001, pp. 3643-

3648.

[20] Y. Xiao, “Stability, controllability and observability of

2-D continuous-discrete systems . In:

, vol. 4, 2003, pp. 468-471.

Acta Mechanica et Automatica

Switching in Systems and Control

Kybernetes

Proc. 40 IEEE Conf. on Decision and

Control

Proc. 40 IEEE

Conf. on Decision and Control

Proc. Int. Symp.

on Circuits and Systems

th

th

”


Articles 7

VOLUME 5, N° 1 2011

Abstract:

1. Introduction

In this paper we address the problem of managing

social interactions between a human user and a set of so-

cial agents in structured environments. These agents have

to regulate a measurable characteristic of the user.

Our study focuses mainly therapeutic, caring for

elderly people and assessment applications. The agent

concept considered in this study is suitable for multi-robot

systems (physical, service, mobile or not), but also to vir-

tual agents (avatars) in a virtual reality (VR) application.

The proposed approach has been adapted for an appli-

cation of social phobia treatment using virtual reality.

Thus, we conducted a case study to show the effectiveness

of our contribution.

Keywords: multi-robot system, multi-agent system, so-

cial interaction, virtual reality, virtual reality exposure

therapy.

Currently, research works on multi-robot systems

(MRS) are one of the most important areas in robotics.

This can be explained by the many benefits of cooperation

of several robots especially for engineering applications.

MRS allows a greater tolerance to faults; it has better

efficiency and performance while having opportunities

for tasks development and expansion.

According to [1] and [2], the research work on MRSs

can be classified in three categories, the first one concerns

reconfigurable robots systems whose idea is inspired by

organization of insects and animals [3-5]. The second

category concerns the trajectory planning of a group of

robots such as the control of aircraft traffic [6]. The third

focuses on cooperative multi-robot architectures, such as,

the project ACTRESS [7] on the maintenance in known

and structured environment.

Unlike conventional control systems, cooperation

must be considered in the modeling, analysis and im-

provement of MRS. Implement this task is not easy. One

of the most used tools for overcoming this difficulty is the

multi-agent systems (MAS). An MRS can be considered

as a robot with a distributed artificial intelligence.

On the other hand, recent years have seen a significant

growth in the development of non-invasive, wearable and

wireless sensors technologies [8-10]. This technique

allows for physiological measures such as HR (Heart

Rate), SC (Skin Conductivity) skin conductance, EMG

(Electromyogram), EEG (electroencephalogram).

These tools have been widely used in clinical, therapeutic

and assistance applications.

The objective of this work is to propose an approach to

manage social interaction between a human user and a set

of agents integrated in structured environment. Their

mission is mainly to regulate a measurable characteristic

of the user. We cite as an example an elderly person in a

structured flat. This person will be assisted by a group of

robots to take his medication in case of falling of his blood

glucose or to be assisted in achieving a daily task (for

example preparing a meal). Agent concept considered in

this paper concerns the physical robots, mobile or not,

service robot, as well as avatars in a virtual environment in

a virtual reality (VR) experience.

Another applications that interests us is treating cog-

nitive disorders using VR, in fact, several research have

documented the effectiveness of exposure therapy using

VR and particularly with phobias [11-16]. The principle of

this technique is to expose the patient to anxiety-pro-

voking situations in a gradual, repeated and complete

manner, in order to desensitize him/her [17],[18]. Our idea

is to integrate virtual actors in a virtual world and to create

anxiety-provoking situations as it would in real-life.

Our contribution is based on three basic concepts:

Make the user live an experience close to the real

world by participating in a story, planned and designed

by an expert;

Let the user free to decide and move in the virtual

environment (VE). He/she is guided by the progress of

events.

Introduce the user's measured state in the control of

actions to activate.

The system consists of the user and the world made of

objects and agents. Our system must satisfy the following

points

Regulate the state of users around a level given by an

expert that depends on the user and application;

Ensure the coherence in the agents behaviors in their

world and hence the cohesion of the story experienced

by the user.

We propose an application of exposure therapy virtual

reality guided by a measure of the patient's state.

To implement our approach, we conducted a pilot

study for treating social phobia that we will detail in this

paper.

There are many research projects to develop coope-

rative MRS. Research work on MRSs in different appli-

cation areas continue to grow since the '80s. Planetary ex-

ploration has received notable attention from researchers.

The experimental project "Robot Work Crew" deve-

loped by NASA [19] addressed the problem of tightly

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2. Related works

INTERACTION MANAGMENT BETWEEN SOCIAL AGENTS AND HUMAN

Abdelhak Moussaoui, Alain Pruski, Brahim Cherki

Received 18 ; accepted 10 .th March 2010 December 2010th


Articles8

coupled coordination of two robots and the cooperation

and coordination problem for heterogeneous and homo-

geneous MRS in difficult environments. The robotic de-

ployment of a modular solar photovoltaic tent using

Campout architecture [20] was also studied.

The robot soccer is also a framework for developing

cooperative SMR, results are of interest for other applica-

tion areas. The soccer robot developed by the team FU-

Fighters [21] is popular. The system is characterized

by a generic and modular architecture composed of layers

provided with 3 elements: sensors (Perceptual Dyna-

mics), behavior (Activation Dynamics) and actuators

(Target Dynamics).

The project Martha is one of the few cooperative MRS

designed to serve human [22]. Its objectives are the

control and the management of a fleet of autonomous

mobile robots for trans-shipment tasks in harbors, airports

and marshalling yards. Experiments were conducted with

a family of 3 mobile robots called Hilare. There are also

other research projects that are conducting laboratory

experiments on more specific issues related to cooperative

MRS, their framework is more general to target specific

applications. In this paper, we conduct a study which finds

application in MRS for therapeutic, evaluation and assis-

tance purposes.

The project eCIRCUS (Education through Characters

with emotional-Intelligence and Rolplaying Capabilities

that Understand Social interaction) proposed a new ap-

proach in the use of ICT to support social and emotional

learning within Personal and Social Education (PSE). To

attain their aim, eCIRCUS studies educational role-play

by the use of autonomous synthetic characters and the

implication of the user through affective engagement [23].

The problem considered in this paper concerns the

management of social interaction between a user and a set

of agents.

The mission of agents aims to control the state of the

user around a value given by an expert; they must also

ensure the coherence of their behavior.

The "state" of the user is a generic term that can repre-

sent measurable characteristics, that is dependent on the

use of the system (a position in space, a physiological va-

lue, a gestural characteristic, the achievement of an action).

For example, in an experience of virtual reality expo-sure

therapy, the state of the user is the measured anxiety levels.

From the user's point of view, the system can be seen as a

control loop of his/her state as shown in Fig. 1.

The “Assessment” bloc informs the system of the

user's state calculated from the recorded measurements.

3. General principle of our approach

1.1. Methodology

Fig. 1. Regulation loop of the human user state.

The “Behaviors Management Engine” bloc is the

regulating element of the system. It compares the state that

is measured on the user with a

ecide on the nature and degree of

actions to

Our solution to the problem discussed in this paper is

based on the theory of MAS that [24] consists of a group of

rational agents interacting with each other. Agents may

sh

al ones. There are several definitions of an agent and

none of them is prevailing [24]. An agent can be anyone

that perceives his/her environment through sensors and

acts on it through effectors [24]. An ideal rational agent

tries to maximize the performance level with regard to

what he perceives and the knowledge at hand [24].

The applications envisaged for our approach consist of

user agents and their entourage made up of cooperating

objects.

We denote by

:

. “

This effect can be “Positive”, “Neutral” or “Negative”

with a degree of relevance given by an expert.

Fig.

reference state fixed by an

expert, in order to d

generate.

are the same interest, conflicting interests or more

gener

agent's actions or behavors. An agent

can have two kinds of actions “Stimulating” actions and

“Background” actions Stimulating” actions are those

which are likely to induce a change in the user's state.

The “help” actions and the “auxiliary” actions belong

to this type. The “help” actions allow the user to follow the

scenario's progress. The “auxiliary” actions indicate the

ones that must be activated simultaneously with another

action for some coherence reasons of the scene. An

“auxiliary” action has an indifferent effect on the user. The

“Background” actions are activated by the agents when

the user is interacting neither with the user nor with

another agent.

represents the set of stimuli and the set of

“Background” actions, where:

“Stimulating” action is mainly characterized by its effect.

Let's remember that the user is an element of the MAS

environment. In order to be able to react correctly, agents

must know, at any time, the user's state. We express the

state with two variables: the magnitude and its derivative.

The user's state to be measured depends on the context

of use. If the goal is to lead the user to a given point, the

measure is the position relative to the target. This measure

determines the progression of the person to the expected

objective state.

The magnitude of the measurement is determined by

three intervals: Low, Normal or High. For derivative, we

decided to divide it into three intervals: “Falling”

(negative), “Stable”(nul) and “Rising”(positive).

In 2, the cells of the table correspond to the user's

states; the arrows represent the possible transitions bet-

ween states.

The set of user's states is denoted by :

1.2. The Behaviors Management Engine

a) Multi-Agent System

c) The MAS Environment

b) The agents and their actions

A A

S

s f


Object

Behaviors Management

EngineHuman

Reactions

Human’s StateAssessment

Agents Actions

State

Articles 9

with

( ) ( )

ch agent calculates his own optimal action that

maximizes the sum over all the products of the probability

to pass into each possible state by its utility ( ).

The utility of state are given by the importance of the

state to achieve the objective of agents.

We are interested in the calculation of the probability

distribution. Suppose that the user is in state . We can say

that the user “Reacts” to action if:

- is “Positive” then the user passes into a higher state

than (according to the order given in the definition of the

set .),

- is “Negative” then the user passes into a lower state

than ,

- is “Neutral”, then the user does not change state.

is the state in which the user goes if he “Reacts” to action

. We suppose that the user either passes into state or

does not change state. From this last characteristic, we can

write that:

( ( ) ( ) + (1 ( ) ( ) )

( )

( )

In case of collaborative agents, coordination allows to

ensure that no agent perturbs the others when he chooses

an action, and his actions are useful to the collective objec-

tive of the group. Assignment of roles to agents is a very

much used tool as a coordination technique. A role can be

seen as a masking operator on all of an agent's actions in

some specific situations. We decided to let only one agent

interact with the user, by introducing the principal agent's

role. The latter will be assigned to the most certain agent

capable of making the user pass into a useful state.

For example, in the case of a virtual reality exposure

therapy, if two virtual agents decide to speak simul-

taneously to the patient, it may interfere with each other.

The stimulus expected utility suits perfectly this end.

Let the capacity of agent to be the principal agent.

is the highest expected utility calculated over all pos-

sible actions of agent . The “Main Agent” role will be

assigned according to the following algorithm:

For eachAgent in parallel

1. Calculate of agent .

2. Transfer to the other agents.

3. Wait till you receive the others' potentials.

4. Attribute the “Main Agent” role to that agent who

maximizes .

End For.

a* = ar g max p s' s, a U s'

a*

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a

s

a s

a* = ar g max p s s, a U s p s s, a U s

p s s, a

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e) Conflicts management

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The probability does not depend only on , and

the nature of , but on certain other parameters as well,

that are related to the effect of on the user and to his

activation conditions. For this reason, depends

on two different elements:

The probability for the user to pass from state to state

if is activated in conditions where the user sees or

hears with certainty;

The activation conditions of in the actual context of

application.

s s

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i

The objective is to bring the user's state back to (Nul-

Stable) denoted: ( , ).

The transition model of an environment expresses the

way it changes state when a given action is activated. The

user's transition model is stochastic. Activating a stimulus

action does not necessarily mean that the user passes into

another state.

Let the user's state at instant and the state in which

he passes after activating action with: , and .

The transition model is a probability distribution ( )

on all possible states .

chastic environments, finding the series of ac-

tions that enable you to reach the objective is not a simple

research on a graph, because transitions between the

different states are non-deterministic and agents must take

into consideration the probabilistic aspect of transitions in

their decisions. The expected utility concept

[24]

( , )

Fig. 3

( ) ( )

s* = N S

s t s'

a s s' S a A

s', s, a

s'

Fig. 2. User's states.

Fig. 3. State Utility.

d) User's state controlling

s* = N S

s

p s', s, a U s'

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In sto

is one of

those used to introduce the transition uncertainty between

states in the agents' decision .

A state's utility , denoted ( ), is a real number that

expresses its importance in the achievement of a given

objective.

( ) :

The objective of an agent is the environment' stat

, a state with the highest utility and desired by

his environment. shows the utility of the user's

different states. We also note that in our case, a state's

utility does not change from one agent to another, because

they cooperate and share the same objective. For an agent

to be rational in state , he must choose an optimal action

* that maximizes the expected utility given by:

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Articles10

f) The story manager

g) The scenario

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h) Reaction to events

M E A N B

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i) Links between actions

Remember that the MAS must regulate the user's state

and generate coherent stories. Our approach consists in

proposing a set of tools that allow people not familiar with

cinematography to transform a story into feasible scena-

rios whose principal element is the user. The actors are

represented by agents.

In some situations, when an action is selected by an

agent, it

The story manager helps the agents to determine all the

possible actions with respect to the situation they are in.

An agent's situation is defined by his geographical loca-

tion and the scenario progress. While evolving in the

world, the user generates events that depend on his geo-

graphical location. These events will help, on one side the

story manager and on the side agents, in order to react to

the user's actions.

For example, go to a specific location to take drugs.

The accomplishment of tasks by the user is indicated by

events. Let the set of events. Each task the user is asked to

fulfill corresponds to a node in the scenario. A scenario is

made of an ordered sequence of nodes from the beginning

to the end of a session. The couple ( ), where: and

represents action that asks the user to accomplish a task

and event indicates that the task has been accomplished

by the user, with the number of agents and the set of

stimuli of agent . A node does not correspond to one

couple ( ) only but to many. In fact, a task can be asked

from the user either in different ways by the same agent,

which corresponds to different actions, or by other agents.

Similarly, a task can be accomplished by the user in

different manners, which corresponds to different events.

Therefore, node is associated to a set = { , , , ...}

of couples = ( ) and so allowing the transition from

node to 1.

Couple represents a condition of passage from node

i of the scenario to node 1. Action asks the user to

accomplish a task, event indicates its accomplishment.

So, a scenario with nodes can be defined by the ordered

series : , , ... .

The events triggered by the user enable agents to react

to his actions. A given event is associated, by an agent, to

one or several actions which are going to be activated.

Let : the matrix that gives the

event-action links, with the number of agents and the

interval of nodes.As an example, indicates that the accom-

plishment of event at the third node of the scenario will

be followed by the activation of .

must be followed by another action, either from

the same agent or another one. The answer to this question

consists in using priority rules represented by a graph of

links between actions. This is valid only in the case of

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a reduced number of actions, as it is in our study.

:

is the matrix that gives the action-action links, with is the

number of agents and the interval of nodes. For instance

( , , 2) = 1 indicates that at the second node of the

scenario, the execution of action will be followed by the

execution of action .

Synthesis on the mechanism of action selection:

This mechanism is given by the following algorithm

where the main steps are summarized:

Find the set of possible actions coming from events

at the actual node of the scenario.

Find the set of possible actions coming from priorities

between actions at the actual node of the scenario.

Find all possible actions .

Calculate the expected utility of stimuli that belong

to and find the optimal action *.

Assign the role

*

M A A N B

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N

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Determine the user's state.

If a node is validated then go to the next one.

of “MainAgent”.

If an agent is the “Main Agent”, then activate stimulus

otherwise choose randomly and activate a “Back-

ground” action.

p

p

4. Application in virtual reality exposuretherapy

4

To implement our approach we have developed an

application in the context of cognitive and behavioral

therapies. During the last two decades, many research

works tried to experiment and evaluate the usage of VR to

treat cognitive and behavioral disorders, and particularly

phobias. The idea consists of making the patient evolve in

a virtual environment (VE) where the she/he gradually

faces situations that are feared in real life in order to

desensitize him/her [25], [26]

We are interested in social phobia which is a pathology

whose fearful conditions can be easily created by an

inductor based on VR using everyday life scenes and at the

same time to have some measurable emotional reactions

from the patients

The virtual actors are represented by the agents of

MAS, the patient will be considered the user.

Video recording of real actors embody the actions of

different agents, these videos are inlaid in the virtual envi-

ronment by chromakey technique. An evaluation of the

effect of these actions has been established by a psy-

chologist.

The virtual environment is a bank. We chose a bank

for many reasons. It offers the possibility to diversify the

anxiety provoking situations, such as talking to strangers,

carrying out tasks while others are looking at you. The

patient will also be motivated by the final objective that is

withdrawing money. The agents present in the VE are:

The secretary “Madeline”, at the reception desk (on the

right in Fig. ), a customer in the hall “Simon” (on the

left), a counter clerk “Guy” (in the middle).

The VE is projected on a wide screen. The patient is

wearing a surfing tool that allows him move around into

the bank.At the beginning of a session, the patient is at the

bank entrance and the therapist explains to him the


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Articles 11

objective without giving him any details. The patient

enters the bank and an electronic display says “Please go

to the reception desk”. At the reception desk, the secretary

asks him what she can do for him. He answers, and then

she phones the counter clerk to have his opinion about the

operation. Next, she tells the patient to go to desk number

2 to withdraw money. Finally, the counter clerk gives him

his banknotes. This scenario corresponds to the main

framework of the story, but even if the patient does not

follow this scenario voluntarily or involuntarily, the

agents help to go back to it.

The patient's state can be “reassured”, “Anxious” or

“Very Anxious” associated respectively to “Low”, “Nor-

mal” and “High”. Similarly, a stimulus may be “reassu-

ring”, “anxiety provoking” or “neutral” associated respec-

tively with “Positive”, “Negative” and “Neutral”. In Fig.

5, three stimuli are shown. These are, respectively from

left to right: “Reassuring”, “Anxiety Provoking” and

“Neutral”. In Fig. 6, three “Background” actions are

illustrated.

The principle of exposure therapy is to put the patient

in front of anxiety provoking stimuli. Therefore, we can

consider that our goal is to bring the patient back to the

“Anxious”-“Stable” state.

We are interested in calculating : the proba-

bility that the patient passes from state to . We said

earlier that depends on the contextual conditions

of activation of . These conditions are summarized with

two parameters: the distance between the patient and the

source of the action and the vision direction of the patient

with respect to the source of action.

Fig. 4. The virtual environment and virtual agents.

Fig. 5. Examples of stimuli.

Fig. 6. Examples of three basic actions..

p s s, a

s s

p s s, a

a

( )

( )

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Let:

According to Bayes' theorem [24], we have:

( ) = ( ( ))/( ( )) =

Remember that we considered action as is the only

stimulation source of the patient in the VE (it comes from

the role assignment to a single agent). Therefore, if he

changes state from , then it is done because of action

. For this reason, we can conclude that .

we have:

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the probability that the patient “Reacts” to stimulus :

the patient reacts to the action if he passes to the state

compatible with the nature of the action.

the probability that the patient hears or sees stimulus ;

the probability that the patient passes from state to

knowing that he has seen stimulus ;

the probability that the patient has seen or heard action

, knowing that he passed from to .

= ( ( ) ( ))/( ( ))

to

( ) = 1

( ) = ( ) = ( ) ( )

( ) is the probability that the patient passes

from à knowing that he has seen or heard action . We

consider that ( ) depends on the effect of action ,

according to the following relations:

( ) = { (3/2( ( ) 1/3}

if is "Anxiety Provoking". @1 3 ( )

if is "Neutral". @ 3/2 ( )+1/3)

if is "Reassuring". )

( ) is the probability that the patient sees or hears

action . A model of this type is not available in literature.

We defined a simplified model based on the following

rules:

The patient hears or sees with certainty the actions that

are in his axis of vision and at a distance less than 4 ;

Actions with a 30 displacement with respect to the

vision axis and a distance less than have a probability

0.7;

Actions with a 90 displacement with respect to the

vision axis and a distance less than have a probability

0.3;

The probability of actions displaced by more than 90

and at distance less than is 0.1;

Beyond 2 , the probability ( ) is inversely propor-

tional to the distance between the action and the

patient.

the story manager and the agent's

behavior, we a pilot assessment on non-phobic

patients. It is difficult to show the effectiveness of our

approach with a small example of three agents, like the one

above, but therapists who tested the developed tool attest

to its usefulness, particularly for the management of virtual

agents. In a first version of the system, no measure of state

anxiety is removed, the system feedback is provided by the

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Articles12


therapist as a Wizard of Oz; he estimates the patient's state

and transmits it to the system through the keyboard. The

sessions were recorded in order to return and consider the

moments of interaction that interest us. Now, we give some

examples:

The situation starts outside the bank, at the entrance.

All agents activate some “Background” actions. The door

is half-opaque and the patient can see what is going on

inside.

In this situation, the patient hesitates before going to

the reception desk and continues his walk in the hall. De-

pending on his anxiety state, the customer in the hall might

look at him to stimulate him (Fig. 9) in case he is

“Reassured”, or omit him if he is already anxious (Fig. ).

5.1. Situation n°1: Outside of the bank. Fig. 7

5.2. Situation n°2: The patient enters the bank

Fig. 8

5.3. Situation n°3: The patient walks in to the

bank.

Now the patient enters the bank. While going through

the main door, the electronic screen displays “Please go to

the reception desk” with a simultaneous beep to attract the

patient's attention, who could either go straight to the

reception desk or to counter 2, or just walk in the hall.

10

Fig. 7. The entrance of the bank.

Fig. 8. The patient enters the bank.

Fig. 9. The customer in the hall stimulates the patient.

Fig. 10. The customer in the hall looks the other way.

Fig. 13. Three ways of reception: “Anxiety provoking”,

“Neutral” and “Reassuring”.

5.4. Situation n° 4: Instead of going to the

reception desk, the patient goes to Counter 2

5.5. Situation n°5: The patient is in front of the

secretary

In this situation, the patient is wrong and instead of

going to the reception, he goes to Counter 2. In this case,

the cashier activates a “help” action and tells the patient to

go to the reception. The way the patient is sent back to the

reception could be “Reassuring” ( 11), “Anxiety Pro-

voking” ( 12) or “Neutral”.

The patient is at the reception. First, the secretary greets

him and then gives him enough time to ask his question.

Three possible ways of reception are shown in 13. The

other agents continue to activate “Background” actions.

Fig.

Fig.

Fig.

Fig. 11. “Reassuring” “help” action.

Fig. 12. “Anxiety Provoking” “help” action.

VOLUME 5, N° 1 2011

Articles 13


5.6. Situation n°6:Aphone conversation

between the secretary and the Counter clerk

5.7. Situation n° 7: The patient is in front of the

secretary but looks at the customer

5.8. Situation n° 8: The patient takes the money

The patient asks the secretary if it is possible to

withdraw money. She telephones the Counter clerk to ask

him if this is possible. She activates an action that stimu-

lates the patient and the counter clerk activates an “auxi-

liary” action that is required for a coherent scene (Fig. 14).

In this situation, the secretary tells the patient to go to

counter n°2 (Fig. 15). The patient, who is looking in the

customer's direction but not at her, hears what she says

(Fig. 16). Though the customer can stimulate him, he does

not do it in order not to disturb the dialog between the

secretary and the patient. Here, the secretary is the “Main

agent”.

The patient is at counter 2. The counter clerk gives him

the money. Here too, the clerk can activate a “Neutral”,

“Reassuring” or “anxiety provoking” action, depending on

the patient's state. Fig. 17 shows the “anxiety provoking”

action.

To implement our approach, we conducted a case

study of a social phobic patient (male, 24 years). Several

subjective scales and objective measure were taken to

assess the effectiveness of our clinical method, including:

Fig. 14. The secretary phones the Counter clerk.

Fig. 15. The secretary talks to the patient.

Fig. 16. What the patient really sees.

6. Real case study

the Liebowitz Social Anxiety Scale (LSAS), the Short

Beck Depression Inventory (BDI-13) The iGroup Pre-

sence Questionnaire (IPQ), the Subjective Unit of Dis-

comfort Scale (SUDS 0-100) and heart rate (HR).

We scheduled the clinical protocol in two phases. The

first phase includes one 45-minute session in which the

patient discovers the therapeutic program and become

familiar with the experimental device. The patient also

completed self-administered questionnaires given above.

The second phase is organized into eight weekly sessions

of 30 to 45 minutes each. The patient experiences in each

session the virtual environment of the bank projected on

a screen. The patient completes auto-questionnaires pre-

viously given before the start of the fifth meeting and at

the end of the eighth.

The results showed that the solution formed by the

automatic control system and the proposed therapeutic

protocol can be applied successfully in therapy for social

phobia. According to the results of measurement of pre-

sence in the environment of the bank (by IPQ), the patient

had a large involvement in the virtual environment. The

psychotherapist has confirmed both, the fluidity of our

therapeutic approach and the easiness of managing this

study; he gave more time to the patient and the therapy.

We have presented our approach to managing social

interactions between a human and a set of social agents in

environments. The proposed approach is based on coope-

rative MAS in which agents share the same objective and

coordinate their actions to achieve it.

.

The effectiveness of the proposed approach has been

proven by adapting our approach to treat social phobia by

VR. The results of social phobic patient case study testify

to the usefulness of the developed application.

The therapist has confirmed both, the fluidity of the

considered therapeutic approach and the easiness to ma-

nage therapy sessions. We are currently studying the deve-

lopment of an application with real robots in order to aid

elderly people to plan and organize daily tasks

Fig. 17. The counter clerk gives the money to the patient.

7. Conclusion

We used coordina-

tion techniques between agents in a MAS to express the

coherence between the activeted actions

.

VOLUME 5, N° 1 2011

Articles14


AUTHORS

Abdelhak Moussaoui, Alain Pruski*

Brahim Cherki

References

- Laboratoire

d’Automatique humaine et de Sciences Comportemen-

tales - Université of Metz, 7, rue Marconi, 57070 METZ

Technopôle, tel: + 33 387 315 281, fax: + 33 387 547 301.

E-mail: {abdelhak.moussaoui, alain.pruski}@univ-

metz.fr

-

* Corresponding author

The Automatic Control Laboratory of

Tlemcen -University of Tlemcen, FSI, BP 230, Chetouane

13000, tel: +213 43 28 56 89, fax: +213 43 28 56 85.

E-mail: [email protected]

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VOLUME 5, N° 1 2011

Articles 15

Abstract:

1. Introduction

In this paper, the Bayesian model for bimodal sensory

information fusion is presented. It is a simple and biolo-

gical plausible model used to model the sensory fusion in

human’s brain. It is adopted into humanoid robot to fuse

the spatial information gained from analyzing auditory

and visual input, aiming to increase the accuracy of object

localization. Bayesian fusion model requires prior know-

ledge on weights for sensory systems. These weights can

be determined based on standard deviation (SD) of uni-

modal localization error obtained in experiments. The

performance of auditory and visual localization was tes-

ted under two conditions: fixation and saccade. The ex-

periment result shows that Bayesian model did improve

the accuracy of object localization. However, the fused

position of the object is not accurate when both of the

sensory systems were bias towards the same direction.

Keywords: multimodal, Bayesian fusion, fixation, sac-

cade, humanoid robot.

et al.

et al.

et al.

Realizing audiovisual object localization in humanoid

robot is a challenging work. As the possible locations of

the object are computed based on image and sound inde-

pendently, the results need to be fused to form the ultimate

perceptions. This Sensory information fusion has the ad-

vantages of reducing uncertainties of the information,

providing information that is unavailable from single type

of sensor and causing the system to be fault tolerant [8]. In

years, researchers have developed sophisticated methods

for multisensory information fusion for robot. For exam-

ple, Yong-Ge Wu [10] proposed an information fu-

sion algorithm based on generalized Dempster-Shafer's

theory of evidence (DSTE). Their result shows that higher

accuracy is achieved when the vision evidence is depen-

dent which violate the basic assumption of DSTE. J.A.

Lopez-Orozco's [11] proposed an enhanced Kalman

filter multi-sensor fusion system that is used to calculate

the position and orientation of an autonomous mobile

robot. They focused on simplifying the Kalman filter

multi-sensor fusion to lower its computational cost and

solving the dependency on the selection of different sam-

pling period and assimilation waiting interval. Kentaro

Toyama's [12] developed a Markov dynamic net-

work model that integrates the analyses of multiple visual

tracking algorithm to enable better head tracking. Their

analysis shows that Bayesian system fusion usually out-

performs any of its constituent systems, often making

estimates close to the system estimate with the least error.

Futoshi Kobayashi's [13] proposed a Recurrent

Fuzzy Inference (RFI) with recurrent inputs and applied it

to a multi-sensor fusion system in order to estimate the

state of systems. The membership functions of RFI are

expressed by Radial Basis Function (RBF) with insen-

sitive ranges and the shape of the membership functions

can be adjusted by a learning algorithm. Lucy Y. Pao

[14] developed a multi-target tracking algorithm based on

Joint Probabilistic Data Association (JPDA) for use in

multi-sensor tracking situation. They found that the multi-

sensory data association probability is actually the pro-

duct of the single-sensor data association probability.

Instead of fusing spatial information of the object gai-

ned, several developed humanoid robot uses auditory in-

formation only as a guide for visual system. For example,

Carlos Beltrán-González [15] proposed a cross-

modal perceptual architecture that can segment object ob-

jects based on visual-auditory sensorial cues, by construc-

ting an associative sound-object memory and create visual

expectation using a sound recognition algorithm. In Hans-

H. Bothe’s paper [16], they described a hierarchi-

cally organized technical system performing auditory-

visual sound source localization and camera control. In

their fusion system, auditory maps that belongs to one

video interval is fused. Then, the resultant map is filtered

by three types of filters, and the results are fused again.

Finally, the fovea position of the object is calculated using

quadratic prediction. Hiromichi Nakashima [17]

proposed a learning model for sound source localization

through interactions between motion and audio-visual

sensing. The model consists of two modules, which are

a visual estimation module consisting of a three-layer per-

ceptron and an auditory estimation module consisting of

a neural network with a Look-Up-Table algorithm.

Different from fusion models discussed above, in the

field of neuroscience, recent researches [1-5] strongly

suggest that multisensory cues, especially spatial cues

may be combined in a much simpler statistically optimal

manner in the brain. For example, Paola Binda [2]

conducted test on human’s ability of localization of visual,

auditory and audiovisual stimulus during the time of fixa-

tion and saccade. They assumed that saccade has little

effect on auditory space perception and both auditory and

visual spatial cues are conditionally independent. They

showed that the result of fusion of auditory and visual spa-

tial cue can be well modeled by Bayes's theorem and the

precision of localization is better in multimodal presen-

tation compare to unimodal presentation. At the same

time, their result also showed that auditory signal becomes

more important for localization at the time of saccades,

suggesting that the visual signal has become transiently

et al.

et al.

et al.

et al.

et al.

et al.

BAYESIAN MODEL FOR MULTIMODAL SENSORY INFORMATION FUSION

IN HUMANOID ROBOT

Wei Kin Wong, Chu Kiong Loo, Tze Ming Neoh, Ying Wei Liew, Eng Kean Lee

Received 23 ; accepted 15 .rd January 2010 September 2010th


Articles16

noisy, and therefore receives less weight. In order to adapt

this Bayesian model of multisensory data fusion into ro-

bot, the weights of all sensory systems under different

conditions such as fixation or saccade must be determi-

ned. This can be done by conducting experiment to mea-

sure the level of erroneous of them.

In this paper, we focus on adopting the Bayesian fu-

sion model into humanoid robot to fused spatial properties

of an object detected by visual and auditory sensors. We

also proposed a way to calculate weights of visual and au-

ditory system based on the error of localization obtained

through experiment, which is the main contribution of this

paper. In order to reduce complexity of calculation, only

azimuth position of the target object is considered.

The visual system of the robot locate the target object

based on its dominant color, determined using color seg-

mentation process that involve log-polar transformation,

temporal difference and hue-saturation (H-S) histogram.

The auditory system locate the target object using inter-

aural time difference (ITD), which is the difference in time

for the sound to reach the left and right microphone. ITD is

determined using generalized cross-correlation (GCC)

method performed in frequency domain. Note that unimo-

dal object localization refers to object localization using

only aidutory or visual system and bimodal object locali-

zation utilize information from both sensory system.

In section 2 of this paper, the Bayesian fusion model is

described. The description of humanoid robot, the experi-

ment and conclusion are available in section 3, 4 and 5,

respectively.

= ( , , ... , ) ( )

( )

( )

( )

( )

( )

= ( , , ... , )

2. Bayesian Fusion ModelIn short, Bayes’ theorem can be described as a way of

converting one condition probability to other, by rewei-

ghting it with the relative probability of the two variables

[1]. In this paper, the weights of sensory system during

fixation and saccade were computed base on SD of locali-

zation error.

In order to derive the relationship between signal in-

puts, weights and the resultant output, let’s assume that the

independent sensor output is denoted by vector = ( , ,

... , ) and the object property (e.g. three-dimensional

position) is denoted by , so that is

the probability of sensor output being given that the ob-

ject property is , and is called the posterior pro-

bability of object property being given that the sensor

output is . In this particular application, can be

computed from data collected during tests, while

is the desired outcome. These two probabilities are related

by Bayes’theorem as follow [8]:

(1)

where the marginal probability and the prior proba-

bility are the unconditional probabilities of the sensor

output and object property being and respectively.

Then, assume that in the system there are sensors, which

give the following readings: and the

best estimate of the object property can be developed

using these sensors reading. This can be achieved by

using the likelihood estimate. In the likelihood estimate

is computed such that the following is maximized:

X x

p X Y

X

Y

k

k

1 x

x

Y y y y

p Y X

Y

X p X Y

p Y X

p X

p Y

X Y

X X X

Y

Y

2

1 2

1 2

m

m

k

�

�

�

�

�

(2)

It is usually easier to deal with the logarithm of the

likelihood than the likelihood itself, because the product

can be changed to the sum, and the term involving

exponents can be simplified. Let be the log-likeli-

hood function:

(3)

Assume that the reading from the sensors follow Gaus-

sian density function, so that is given by:

where is the variance-covariance matrix, denotes

the transpose, and denote the determinant. Now, the

expression for likelihood (Eq. 3) becomes:

The best estimate of can be found by differentia-

ting with respect to , equating the result to zero, and

computing the value of , as follows:

Consider a system that contains only two sensors and

let both and in Eq. 6 be scalar measurements and let

simply be the SD of localization error of the sensor, Eq. 6

is then becomes:

In our case, symbol is used to denote the azimuth

position, so that = be the best estimation of object azi-

muth position, = and be the visual and auditory

spatial information in azimuth plane computed by visual

and auditory system. Then, the Eq. 7 is rewritten as:

Clearly, Eq. 8 shows that result of bimodal localization

is actually the weighted sum of the results of two unimodal

localization, where the weight of visual and auditory

system ( and ) [2] are defined as:

and

Thus, Eq. 8 can be further simplified into

L Y

p X Y

C

Y Y

L Y

Y

X C

S

S

x S x S

w w

( )

( )

(4)

(5)

(6)

(7)

=

(8)

(9)

(10)

i

i

i

v,a

v a

v a

�

t

Y

y

��

1 2


p Y X( ) =�p Y X p Y( ) ( )�

p X( )

p Y p X Y( ) = ( )�� i

L Y p Y p X Y( ) = log ( ) = log ( )�� i

L Y C X Y C X Y( ) = log[(2 ) ( ) ( )�� n ti i i i

�1

Y =

y =

p X Y e( ) =i�(� � �½( ) ( ))x Y C x Yi i i

t �11

� C Xi i�1

x +1 x2

� Ci�1

i = 1

i = 1

i = 1

i = 1

� 12 �2

2

i = 1

2 2

k

k

k

k

1 1

+� 1

2 �22

1 1

k

1 1

�

�

�

(2 )� n/ /2 1 2Ci

Sv,a =

wv =

wa =

S +v Sa� v2 �a

21

� v2

1

� a2

1

1

+� v

2 �a2

1 1

+� v

2 �a2

1 1

+� v

2 �a2

1 1

Articles 17

relative to the head. The range of head’s motion was limi-

ted to 30 to the left and right. The sampling rate of the

program was 6 Hz and the eyes and ears of the robot share

the common center in horizontal plane.

(a)

(b)

Total of five tests were conducted in this experiment.

They are fixation test, 20 deg/sec saccade test, 30 deg/sec

saccade test, 60 deg/sec saccade test and condition specific

calibration saccade test. During fixation test, the robot’s

head and eyes were initially fixated towards the back-

ground at position (0 , -20 ) (elevation angle of 0o and

azimuth angle of -20 ; positive value indicates right and up

of the humanoid robot while negative value indicates left

and down). The head was then turned with 1o stepping and

fixate for 1 sec after each step, until it reach (0 , +20 ) and

back to (0 , -20 ). The test was repeated three times. Per-

ceived positions of auditory and visual stimulus were re-

corded each time the robot head fixated. During saccade

test, the neck was turned by three different angular speeds

(20 deg/sec, 30 deg/sec and 60 deg/sec). The robot head

was firstly fixate at (0 , -30 ). Once the tests start, the head

was turned between position (0 , +30 ) and (0 , -30 ) and

only the data that fell within the range (20 to the left and

right) were recorded. Each test was repeated 50 times in

order to minimize the influence of random hardware error

and the motion range was extended to 30 at both sides du-

ring sac-cade test to avoid obtaining data during saccade

onset and changes of direction of motion because images

was noisy during these conditions. After the results of the-

se saccade test were obtained, the robot system was reca-

librated based on the amount of error obtained during

60 deg/sec saccade test. Then, the condition specific cali-

°

° °

°

° °

° °

° °

° ° ° °

°

°

Fig. 2. (a) The robot facing forward which is the direction

of the red square and white noise and (b) top view of the

experiment setup, indicates the azimuth angle with res-

pect to head center (modified from [9]).

�

(11)

(12)

The humanoid robot used in the experiment is called E-

Bee, shown in Fig. 1. Its head consists of 5 degree of free-

dom (D.O.F) and is driven by servomotors. These servo-

motors are powered by a unit of D.C. power supply and are

control by a SSC-32 servomotor controller connected to

the computer. The computer contains an Intel Xeon E5335

Quad-core processor, 2 GB RAM, 100 Mbps network con-

troller, a standard 44.1kHz soundcard and a 128 MB gra-

phic card. A pair of Logitech Quickcam Express and a pair

of Sony ECM PC50 omni-directional microphones were

used in the robot to grab image and sound. The Linux base

asymmetric multiprocessing (AMP) system is equipped

with Intel open source computer vision library (OpenCV)

and Intel Integrated Performance Primitives (Intel IPP)

which contains C/C++ Compiler, Math Kernel Library and

Signal Processing Library.

In order to study the Bayesian model for sensory infor-

mation fusion and to compute the weight of visual and au-

ditory system during the time of fixation and saccade, tests

were conducted to obtain the SD of these two sensory sys-

tems. In these tests, only azimuth position of the stimulus

was considered. We assumed that error and sampling pe-

riod of auditory and visual system were independent and

time lag between visual and auditory signal can be ignored.

Background of image was a static color background in

green color, located in front of the humanoid robot, with

a distance of 60 cm from the eye-center, as shown in Fig. 2.

The visual stimulus was presented as a 2 cm x 2 cm red

square on the green background and the auditory signal

was presented as continuous white noise, played through

a normal speaker. Fluorescent light was used with no addi-

tional light source such as sun light. Due to hardware limi-

tation, the neck was turned relatively to the stimulus to

make the audiovisual stimulus move horizontally in the

image captured, instead of driving the stimulus to move

The predicted bimodal threshold is then given by

Clearly, according to Eq. 11, SD denotes the reliability

of the sensory system. A reliable sensory system with

lower SD will be assigned a larger weight and will become

more important in estimating the target’s properties under

certain condition (e.g. fixation and saccade).

�v,a

3. E-Bee the humanoid robot

4. Experiment

Fig . 1. E-Bee’s head.


S w S + w Sv,a v v a a=

�v,a =�v

2 �a2

�v2 + �a

2

Articles18

bration test was car-ried out by using similar setting with

60 deg/sec saccade test.

The robot head was assumed to turn with a constant

speed until the end of motion after a short time from sac-

cade onset and changes of direction of motion. The actual

azimuth position of robot head was estimated using a sim-

ple mathematic equation shown below:

(13)

where denote the azimuth position of the stimulus,

denote the angular speed and denote the time relative to

saccade onset of each turn.

Fig. 3 demonstrates the result of all five tests. The

result of fixation test indicates that during fixation, audi-

tory and visual localization was accurate with little error of

object localization. According to the results of 20 deg/sec,

30 deg/sec and 60 deg/sec saccade test, the error of unimo-

dal object localization increased as the angular speed of

robot head was increased. This is because the images cap-

tured were blur and not reliable during the time of saccade.

This phenomenon affected the result more and more signi-

�

t

ficantly as the angular speed of the robot head increased,

especially during the time saccadic motion starts and du-

ring changes of direction of motion.

Table 1 and Fig. 4 summarize the calculated mean and

SD of the error of unimodal and bimodal object localiza-

tion. Table 1 also summarize the calculated weights of uni-

modal object localization under different conditions. The

bimodal threshold, was calculated by comparing the

result of bimodal localization with the ideal position, rather

than using Eq. 12. According to the results in Table 1, it is

clear that Bayesian model of sensory information fusion

did improve the accuracy of object localization as the mean

error of bimodal object localization were smaller then the

mean error of object localization by visual system at all

time. Also, during all three saccade tests, auditory system

has become more reliable as it carried lower error than

visual system. However, the results of object localiza-tion

by auditory system during all saccade tests were still less

reliable since they deviated greatly (2 to 7 ) from ideal

position throughout the tests. As a result, the result of

bimodal object localization was distorted greatly when

both sensory systems were not accurate. This has clearly

demonstrates the disadvantage of Bayesian model that,

during the absence of reliable sensory system such as

auditory system in human [2], the result of Bayesian model

could become inaccurate as well.

�

� �

v,a


� �� = (30 )n t

n =�1 for motion from left to right

1 for motion from right to left

(a) (b)

(c) (d)

Fig. 3. (a) Demonstrations of a portion of the results of

unimodal and bimodal localization during fixation test.

(b) (c) (d) Demonstrations of the result of 20 deg/sec,

30 deg/sec and 60 deg/sec saccade test, respectively. (e)

The result of condition specific calibration saccade test.

(adopted from [9]).

(e)

Fixation Test for Unimodal and Bimodal Localization 20deg/sec Saccade Test

Condition Specific Calibration Saccade Test

60deg/sec Saccade Test30deg/sec Saccade Test

Articles 19


Table 1. Comparison of mean and SD of error of localization of auditory, visual and Bayesian audiovisual localization, as well

as the weight of unimodal localization [9].

Test

Fixation

20deg/sec saccade

30deg/sec saccade

60deg/sec saccade

Condition specific

calibration saccade

Visual localizationAuditory localization

SD

( )

0.8732

2.5389

3.3786

6.9261

2.3324

�a

Weight

( )

0.6897

0.4636

0.6584

0.7239

0.7132

wa

Mean

( )

0.7159

1.9910

2.7428

6.6249

1.9197

xa

SD

( )

1.3020

2.3605

4.6905

11.2159

3.6779

�v

Weight

( )

0.3103

0.5364

0.3416

0.2761

0.2868

wv

Mean

( )

0.7683

2.0713

4.2705

10.7557

2.9261

xv

SD

( )

0.7905

2.1015

3.6338

7.9769

2.3861

�v,a

Mean

( )

0.6341

1.7941

3.1689

7.7643

1.9492

xv,a

Bayesian audiovisual

localization

� � �

(a) (b)

(c) (d)

Articles20


Hence, in order to overcome this weakness of Bayesian

model, condition specific calibration was proposed. After

the condition specific calibration, the mean and SD of error

of unimodal object localization became much smaller (see

Fig. 4) compare to the 60 deg/sec saccade test. However,

the disadvantage of this solution was that, the humanoid

robot must always be aware of the angular speed of its head

and condition specific calibration value must be known.

Similar to biological system, altering robot design will

eventually alter the weight of all sensory system.

In this paper, the Bayesian model for multimodal

sensory information fusion is described. We show that

Bayesian fusion of multisensory information is simple

and can be applied in humanoid robot to increase the

accuracy of object localization. However, current model

requires prior knowledge on the reliability of sensory

system obtained through a series of experiments. Besides,

the Bayesian model failed to generate accurate spatial

position of audiovisual stimulus when both of the sensory

systems were not reliable, especially during saccade.

Future work should focus on using neural network and

reinforcement learning to provide the Bayesian fusion

model the ability to learn the weights online.

-

-

5. Conclusion

ACKNOWLEDGMENTS

Wei Kin Wong, Tze Ming Neoh, Chu Kiong Loo*

Ying Wei Liew, Eng Kean Lee

This research was funded by Intel Research Grant from year

2007 to 2009.

AUTHORS

Centre for Robotics and Electrical Systems, Multimedia

University, Jalan Ayer Keroh Lama, 75450 Melaka, Mal-

aysia. E-mails: [email protected],

[email protected], [email protected].

Intel Malaysia Sdn.

Bhd., Bayan Lepas Free Industrial Zone, 11900 Penang,

Malaysia. E-mails: [email protected],

[email protected].


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(e) (f)

Fig. 4. (a)(b)(c)(d)(e) Demonstrations of mean and SD of auditory, visual and audiovisual localization under different

conditions. (f) Comparison of mean error of localization. The mean error increased when saccade speed increased in all tests,

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Articles 21


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VOLUME 5, N° 1 2011

Articles22

Abstract:

1. Introduction

A novel method for real-time coordinated trajectory

planning and obstacle avoidance of autonomous mobile

robot systems is presented. The desired autonomous

system trajectories are generated from a set of first order

ODEs. The solution to this system of ODEs converges to

either a desired target position or a closed orbit de.ned by

a limit cycle. Coordinated control is achieved by utilizing

the nature of limit cycles where independent, non-crossing

paths are automatically generated from different initial

positions that smoothly converge to the desired closed

orbits. Real-time obstacle avoidance is achieved by spe-

cifying a transitional elliptically shaped closed orbit

around the nearest obstacle blocking the path. This orbit

determines an alternate trajectory that avoids the obsta-

cle. When the obstacle no longer blocks a direct path to the

original target trajectory, a transitional trajectory that

returns to the original path is defined. The coordination

and obstacle avoidance methods are demonstrated expe-

rimentally using differential-drive wheeled mobile robots.

Keywords: path planning, obstacle avoidance, ODE,

mobile robots.

A typical method for trajectory planning is to describe

the trajectory by a time-varying transitional state variable

that converges to the desired target from an arbitrary

initial position. A well-defined system trajectory deter-

mined in this manner, however, should not have any

discontinuities and should admit tracking controller

designs that are able to feasibly follow the resulting

trajectory. The coordination of a group of autonomous

systems is an even more challenging problem because of

the potential for dynamic interaction and collision

between members of the group and/or obstacles. A num-

ber of different approaches for the coordination of a group

of autonomous systems into user-defined formations have

been proposed. These approaches can be categorized into

virtual structure, behavior-based, and leader-follower

techniques. A survey of recent research in cooperative

control of multi-vehicle systems is presented in [1]. Much

of the work in obstacle avoidance has focused on

trajectory optimization, decentralized control approaches,

behavior-based approaches, and potential .eld methods.

A review of path planning and obstacle avoidance tech-

niques is presented in [2] In this work, a novel trajectory

planning and obstacle avoidance approach that provides

coordinated control of multiple autonomous agents is

presented. The proposed strategy is composed of two parts

consisting of a coordinated trajec-tory planning algorithm

[3] and a real-time obstacle avoid-ance algorithm [4]. The

main advantage of this approach is a practical, compu-

tationally efficient trajectory planning algorithm with the

capability for coordination of multiple autonomous agents

and real-time collision avoidance for moving obstacles.

To accomplish these tasks, transitional trajectories are

defined using sets of ordinary differential equations

(ODEs) of two general forms depending on the situation.

If the objective is to catch and follow a desired target

trajectory, the transitional trajectory is defined by a set of

exponentially stable equations whose solution converges

to the target trajectory. In the cases of coordinated motion

and obstacle avoidance, the trajectory is defined by a set of

equations whose solution is a stable limit cycle. A related

approach to trajectory planning that is applicable to

industrial robot manipulators is presented in [5]. Limit

cycles of finite size and shape are used as a way of de.ning

complex obstacles to be avoided. Applications of real-

time navigation methods for mobile robots using limit

cycles have also been addressed in [6], [7]. The limit

cycles are defined as circles with constant coef.cients

which may result in impractical control demand depen-

ding on the distance of the vehicle to the limit cycle. The

use of constant coefficients in the limit cycle equations

also applies only to stationary limit cycles. In this work,

these approaches are expanded to dynamic limit cycles

with more general elliptical geometries.

2. Mobile robot platform

Fig. 1. Experimental mobile robot from each motor to

determine the wheel velocities.

REAL-TIME COORDINATED TRAJECTORY PLANNING

AND OBSTACLE AVOIDANCE FOR MOBILE ROBOTS

Lucas C. McNinch, Reza A. Soltan, Kenneth R. Muske, Hashem Ashrafiuon, James C. Peyton

Received 23 ; accepted 25 .rd July 2010 November 2010th


Articles 23

The experimental mobile robot system used in this

work is the differential-drive wheeled robot shown in

Figure 1 based on the LEGO Mindstorms NXT controller.

It has the ability for wireless communication between

individual robots and a host computer. The coordinated

trajectory planning and real-time obstacle avoidance

application is developed for this platform in the Matlab/

Simulink programming environment using the ECRobot

rapid prototyping development tools [8]. The relatively

low cost and high performance of this device results in

a very powerful, flexible, and cost-effective mobile com-

puting platform that can be used to implement advanced

autonomous system applications directly on the target

hardware. Further detail on this mobile robot platform can

be found in [9].

represents the length of the segment

connecting the wheel

arising from the two-dimensional planar kinematics for

a differential drive mobile robot. The planar motion is

described by the velocity vector = [ ] where is the

forward velocity of the robot in the direction orthogonal to

the drive-wheel axis and is the angular velocity of the

robot about the center of the drive-wheel axis. The

kinematic equations relating the body fixed velocities to

the inertial reference frame are written as

(2)

A. Kinematic Model

The autonomous mobile robot consists of two inde-

pen-computed using equation (3) and the current position

in the dently actuated front drive wheels of radius

mounted on inertial reference frame is determined using

the kinematic a common axis as shown in Figure 2. The

track width

centers. A passive caster supports

the rear of the mobile robot. The three degree of freedom

planar posture of the robot is described in the inertial

reference frame by the vector = [ ] and the motion

of the mobile robot is subject to the nonholonomic

constraint

ÿcos sin = 0 (1)

r

d

x y

y xÿ

p �

� � �

T

q

q

v v

Fig. 2. Differential drive mobile robot schematic.

�

�

T

Assuming there is no wheel slip, the body fixed velo-

cities are related to the angular velocities of the drive

wheels by

Two methods of position feedback are available for the

experimental mobile robots. The first uses encoder feed-

back from each motor to determine the wheel velocities.

The current forward velocity and angular velocity are

then computed using equation (3) and the current position

in the inertial reference frame is determined using the

kinematic

where and is the sample period.

The reference posture and velocity vectors describing

the desired trajectory are defined at each sample period

where the reference posture and velocity

vectors must adhere to the nonholonomic

constraint equation (1). The position and orientation

(3)

where and are the angular velocities of the left and

right wheels respectively [10].

model presented in equation (2). A discrete-

time approximation of the current pose of the robot at

sample is

(4)

= [ ]

This method assumes that there is no wheel slip and

that the mobile robot travels on a perfectly flat planar

surface.

Because there are typically wheel slip and other un

measured disturbances present, a more accurate method to

determine the robot position based on real-time proces-

sing of a camera image is also available. A digital black

and white camera mounted a fixed position above the mo-

bile robots is used to view the two infrared LEDs installed

at the front and rear of the centerline of the robot as shown

in Figure 1. Each camera frame provides a dead reckoning

measurement of position and orientation of each robot that

is determined from the pixel location of the LEDs [9].

Position measurements are available at approximately

are related to the angular velocities of the drive wheels by

20Hz using the camera and at least 100 Hz using the

encoders. The advantage of using encoder feedback is that

the position can be determined locally without the need to

transmit a camera-based position to each robot from the

host computer at the 20 Hz sample rate of the camera. The

advantage of using camera feedback is that it is a dead

reckoning measurement that is not subject to error from

wheel slip, uneven surfaces, collisions, and other distur-

bances. A combination of encoder and camera feedback

using multi-rate estimation schemes can be employed to

obtain the benefits of each measurement while minimi-

zing the communication requirements, however, this tech-

nique is not considered in this work.

= [ ]

= [ ]

� �

�

�

�

L R

k k k k

k k k k

k k k

B. Position Feedback

C. Local Tracking Control

v

T

�

k

x y

x y

v

p

p

q

T

T

T


Articles24

VOLUME 5, N° 1 2011

r

r r r

r rr

Fig. 4. Open-loop and closed-loop paths.

Fig. 5. Trajectory tracking errors.

A. Trajectory Tracking Performance

B. Encoder and Camera Image Position Feedback

A demonstration of the mobile robot tracking control

performance is provided by defining a reference trajectory

that approaches and then follows a circular orbit with

a diameter of 1 meter represented by the solid line in Figure

3. The mobile robot must avoid two obstacles in this path

that are contained within the circles indicated by the

dashed lines in Figure 3. These two circles represent the

obstacle boundaries that should not be crossed. Figure 4

presents the desired reference and experimental mobile

robot paths with and without tracking control.As shown in

this figure, the tracking error increases as the robot turns

without encoder feedback tracking control. The maximum

tracking error is reduced over five times using the closed-

loop tracking control law presented in equation 6. The

corresponding tracking errors are presented in Figure 5.

Figure 6 compares the desired reference, encoder, and

camera image paths for the mobile robot under tracking

control using encoder feedback on a flat surface with

minimal wheel slip. As shown in this figure, there is very

little difference between the position determined from the

en coders and that determined from the camera image.

Figure 7 presents a similar scenario except there is signi-

ficant wheel slip obtained by using plastic wheels, as

opposed to rubber, on a slick surface and specifying

tracking

where the current pose of the robot is . Various control

laws for tracking and position control of mobile robots

have been developed. In this work, the following kine-

matic tracking control law proposed in [11] is used

(6)

where the tracking errors are defined in equation (5), is

the corrected forward velocity setpoint, is the corrected

angular velocity setpoint and the controller gains , ,

and are strictly positive constants. The corresponding

wheel velocity setpoints which compensate for any

tracking error in the reference trajectory are calculated by

inverting equation (3)

(7)

where the rotational speed setpoints are sent to

individual wheel speed controllers. An extension of this

controller to motor torque and position set point control is

proposed in [12].

error vector = [ ] is expressed as

(5)

and

An experimental veri.cation of a novel method of com-

bining trajectory planning and coordination or formation

control of robotic and autonomous systems presented in

[3] for autonomous surface vessels is demonstrated in this

section. The method generates target trajectories that are

either asymptotically stable or result in a stable limit cycle.

The former case is used to implement formation control.

Coordination is guaranteed in the latter case due to the

nature of limit cycles where non-crossing independent

paths are automatically generated from different starting

positions that smoothly converge to closed orbits.Arelated

approach for trajectory planning that is applicable to

industrial robot manipulators is presented in [5]. A survey

of recent research in cooperative control of multi-vehicle

systems is presented in [1].

ek k k k

k

k

k

x y

L R

e e e

Fig. 3. Trajectory tracking example.

T

p

v

k k

k

�

� �

�

3. Experimental application


Articles 25

VOLUME 5, N° 1 2011

p

s

s

s s

x y �

a trajectory with a smaller turning radius. A large error in

the encoder position is now present under these conditions.

In the sequel, the conditions in Figure 6 are used such that

encoder feedback will provide an acceptable position mea-

surement. The advantage of en coder feedback is that the

position can be determined locally by each robot without

the need to transmit a camera-based position to each robot

at the 20 Hz sample rate of the wheel speed controllers.

The reference trajectory for each mobile robot is defi-

ned using a set of two ordinary differential equations

(ODEs) in terms of the two planar global position variables

and

(8)

where and are the state variables, is an open and

connected subset of , and is a locally Lipschitz map

from into . The state variables and are defined as

(9)

Fig. 6. Closed-loop trajectory with camera position.

Fig. 7. Closed-loop tracking error with slip.

x y

x x D

f

D x x

C. Reference Trajectory Determination

1 2

1 2

i

i

i

�

�

2

2

where the definition of and and the form of the

functions and are determined based on the type of

reference trajectory. The reference posture and velocity

are obtained from the state variables by

(10)

(11)

where the expressions for and arise from the nonholo-

nomic constraint in equation (1)

Two general types of reference trajectory are presented

in this work. The first is implemented when the mobile

robot is required to reach a target position which may be

either moving or stationary. This type of reference trajec-

tory is referred to as a .

The second is implemented when the mobile robot is

required to smoothly converge to and then follow a closed

orbit. This type is referred to as a

.

The transitional target trajectory defines a reference

trajectory for which the mobile robot path converges expo-

nentially to a stationary or moving target position. The

desired reference position is denoted by ( ) and ( ) and

the target position is denoted by ( ) and ( ) in the inertial

reference frame. The state variables introduced in equation

(9) are defined as

(12)

such that when the ODEs in equation (8) are asympto-

tically or finite time stable, ( ), ( ) 0, which implies

that ( ) and ( ).

The exponentially stable transitional target trajectory

used in this work is defined by the following linear ODE

system

(13)

where is the initial time. The strictly positive parameters

( ) and ( ) are assumed to be functions of time in order

to generate desired reference trajectories that conform to

the physical constraints of the mobile robot system. In ge-

neral, they are selected as monotonically increasing posi-

tive time functions that start from a small value and are de-

fined based on actuator limitations and the distance to the

target. Fifth order polynomials allow for smooth and mo-

notonic transitions of these parameters to their final values.

The following polynomial may be used to compute each

parameter , =1,2 during the transition period

(14)

where = . The following boundary conditions are

selected to smoothly increase the value of from 1% to

100% of its final value

x* y*

f f

Transitional Target Trajectory

Transitional Limit Cycle

Trajectory

x t y t

x t y t

x t x t

x x t y y t

t

k t k t

k i t t t

t t t

k

k

1 2

1 2

0

1 2

0 1

0

p

q

r

r

r r

r r

t t

r t r t

� �

�

� �

� �

� �

D. Transitional Target Trajectory

i

i

i


Articles26

VOLUME 5, N° 1 2011

(15)

where and are the 1st and 2nd time derivatives of .

The six polynomial coefficients , = 0,...,5 are derived

using the six boundary conditions specified in equation

(15). Note that = ifor > and is selected based on the

initial distance of the target from the vessel.

The transitional limit cycle trajectory defines a global-

ly exponentially stable limit cycle to which the desired

reference trajectory will converge. The state variables in-

troduced in equation (9) for the transitional limit cycle

trajectory are defined as

(16)

where ( )and ( )denote the position of the origin of the

limit cycle in the inertial reference frame. The origin of the

limit cycle is assumed to be a function of time to allow for

dynamic obstacles. The transitional limit cycle trajectory

has the following form

(17)

where ( , , ) defines the limit cycle geometry which

may be an explicit function of time to account for obstacle

planar rotation. The functions ( , , ) and ( , , ) re-

present the planar particle motion kinematics on the limit

cycle; i.e. when ( , , ) = 0. The solution of equation (17)

guarantees that any trajectory with an initial position out-

side of the limit cycle will converge to the limit cycle with-

out crossing it. The positive parameters ( ) and ( ) are

again assumed to be functions of time in order to generate

realizable trajectories and are derived using equation (14).

In this work an elliptical limit cycle geometry is used.

This is a very general shape which can be used for obstacle

avoidance, coordinated control, aerial coverage or any

number of other trajectory planning applications. The limit

cycle ( , , ) is defined by the general equation of an

ellipse with semi major and semi minor axes, and ,

respectively, and origin at ( ( ), ( ))

(18)

where ( ) and ( ) are defined in equation (16) and ( ) is

the angle representing the orientation of the ellipse semi

major axis relative to the global horizontal axis. This angle

can be time dependent if the desired limit cycle is rotating.

The functions ( , , ) and ( , , ) are defined based on

the motion of a particle around an ellipse

(19)

where ( ) is the average angular velocity of the particle

on the limit cycle. Note that ( ) > 0 represents counter-

k k k

k j

k k t t t

x t y t

l x x t

h x x t h x x t

l x x t

k t k t

l x x t

a b

x t y t

x t x t t

h x x t h x x t

t

t

i i i

ij

i i 1 1

1 2

1 1 2 2 1 2

1 2

1 2

1 2

1 2

1 1 2 2 1 2

E. Transitional Limit Cycle Trajectory

F. Elliptical Limit Cycles

O O

O O

�

clockwise (CCW) rotation and ( ) < 0 represents clock-

wise (CW) rotation. The time derivative of equation (19) is

(20)

where is the time derivative of . The functions

( , , ) and ( , , ) are derived by eliminating cos

and sin from equations (19) and (20) and are given by

(21)

where

(22)

The average angular velocity ( ) is assumed to be

monotonically increasing in magnitude such that + 0.

Again, a fifth order polynomial is used to transition ( )

from 1% to 100% of its final constant value in the selec-

ted transition period of

(23)

where = . The following boundary conditions

(24)

are used to derive the six polynomial coefficients ,

=0,...,5.

An application of the transitional limit cycle trajectory

is coordinated control. The trajectories defined in equa-

tion (17), which converge to stable limit cycles, are ideal

for co-ordinated control because all paths to the limit cycle

are independent and non-crossing due to the uniqueness of

the solution to the ODEs. The following experimental ex-

ample considers the coordinated motion of three identical

mobile robots that join an elliptical reference trajectory at

spaced intervals and maintain the elliptical motion. The

mobile robots are initially located at the ( ) positions

( 1,0), (0.5,0.5), and (0.5, 0.5) expressed in meters. Each

robot joins the elliptical reference trajectory with semi-

major and semi-minor axes of =0.5m and =0.3m

respectively oriented with = 30degrees, its center at

(0,0) and a desired angular velocity of =0.45rad/sec. The

final values of the trajectory parameters are selected as

= =0.8. Figure 8 presents the actual trajectories of the

three mobile robots under tracking control using encoder

feedback. The trajectories shown remain within 2 cm of

the desired reference trajectory for the duration of the

experiment. The markers on each trajectory represent the

mobile robot positions at 2.5 second intervals for the first

ten seconds of the trajectory. The velocities during the

initial portion of the trajectory as the mobile robot joins

the limit cycle depend on the initial distance from the

closed orbit. Robots that start closer to the closed orbit

proceed at a slower velocity while those that start further

� �

� �

� �

� �

t

h x x t h x x t t

t

t

t

t t t

t t t

i

x,y

a b

k k

1 1 2 2 1 2

0 1

0

1 2

i

4. Coordinated control


Articles 27

VOLUME 5, N° 1 2011

�

�

�

�

�

� �

��


Articles28

away must approach at a higher velocity such that all three

robots converge to the limit cycle in the proper orienta-

tion. Note that these velocities are automatically genera-

ted by the ODEs in equation (17). The trajectories pre-

sented in figure 8 are calculated based on the encoder

feedback used by the control law. However, because of

wheel slip, changes in elevation of the ground surface

and/or other external disturbances, the trajectory that the

robot actually follows may be different from that calcu-

lated based on encoder feedback. Figure 9 presents the

reference and actual trajectories of the robot which started

at (1.5,0.5), including the camera image trajectory. This

.gure shows that, in contrast with the trajectory presented

in Figure 6, under some circumstances, the actual trajec-

tory of the mobile robot using encoder-based feedback

may deviate from the desired reference trajectory. It may

become necessary to include the camera image feedback

in the tracking control law when this deviation becomes

significant.

An application of both the transitional limit cycle and

transitional target trajectories is the problem of obstacle

avoidance. In this application, a mobile robot is comman-

ded to converge on a target position in the presence of

several stationary or moving obstacles. The obstacles can

be approximated by elliptical limit cycles as developed in

Fig. 8. Experimental coordinated robot trajectories.

Fig. 9. Elliptical trajectory with camera position data.

5. Obstacle avoidance

section III-F.At each sample time, the nearest obstacles on

a straight line path from the mobile robot to its target is

detected. Real-time obstacle avoidance is carried out by

transitioning from the target tracking trajectory to a tra-

jectory that approaches the nearest obstacle in the original

path. As soon as the mobile robot is around the obstacle,

the trajectory is switched either to another limit cycle

approximating and enclosing the next obstacle or to the

original target-tracking trajectory. In the following experi-

mental example the mobile robot starts at the origin and its

target position is (1.5,0). Obstacles are located at (0.5,0)

and (1,-0.1)and are approximated by circular limit cycles

with radius =0.15. Figure 10 shows the desired reference,

encoder, and camera image paths for the robot under

tracking control with encoder feedback. The numbered

markers along the desired reference trajectory represent

the desired positions at four different times. Marker 1

represents the initial position of the mobile robot and

marker 4 represents the target position. At the first instant

of the experiment, the obstacle avoidance algorithm

recognizes that obstacle 1 is the closest obstacle lying in

the straight line path to the target and a transitional limit

cycle trajectory surrounding the obstacle is generated. The

mobile robot approaches obstacle 1 following the transi-

tional limit cycle trajectory until the next obstacle is detec-

ted in its path. A new transitional limit cycle trajectory is

generated beginning at at marker 2. This trajectory is

followed until marker 3 at which point the obstacle no

longer lies in the direct path to the target. Finally, the

transitional target trajectory de.ned in Section III-D is

used to converge on the .nal target position.

A novel method combining coordinated control and

real-time obstacle avoidance for autonomous systems is

presented and experimentally veri.ed using mobile robots.

The method generates transitional reference trajectories

as a function of the current system trajectory and the

desired target trajectory. The method can be used for

coordinated control of several robots guaranteeing unique

collision free paths and coordi-nated operation. It can also

be used for real-time obstacle avoidance and target

tracking applications.

r

Fig. 10. Experimental obstacle avoidance trajectories.

6. Conclusions

VOLUME 5, N° 1 2011


Articles 29

ACKNOWLEDGMENTS

Lucas C. McNinch*, Reza A. Soltan, Kenneth R.

Muske, Hashem Ashrafiuon, James C. Peyton Jones

Support for this work from the Of.ce of Naval Research under

ONR Grant N00014-04-1-0642, the National Science

Foundation under NSF Grant DUE-0837637, and the Center for

Nonlinear Dynamics and Control (CENDAC) at Villanova

University is gratefully acknowledged.

[1] R. Murray. Recent research in cooperative control of

multivehicle systems.

, 2007, 129(5), pp. 571-583.

[2] V. Kunchev, L. Jain, V. Ivancevic, A Finn. Path plan-

ning and obstacle avoidance for autonomous mobile ro-

bots: A review. In:

, Springer-Verlag, 2006, pp.

537-544.

[3] R. Soltan, H. Ashra.uon, K. Muske. Trajectory planning

and coordinated control of robotic systems. In:

, 2009.

[4] R. Soltan, H. Ashrafiuon, K. Muske. Trajectory real-

time obstacle avoidance for underactuated unmanned

surface vessels. In:

, 2009.

[5] L.P. Ellekilde, J. Perram. Tool center trajectory planning

for industrial robot manipulators using dyna-mical

systems.

, 2005, 24(5), pp. 385-396.

[6] D.H. Kim, J.H. Kim. A real-time limit-cycle navigation

method for fast mobile robots and its application to robot

soccer. , 2003, 42(1),

pp. 17-30.

[7] R. Grech, S.G. Fabri. Trajectory tracking in the presence

of obstacles using the limit cycle navigation method. In:

, 2005, pp. 101-106.

[8] T. Chikamasa. Embedded coder robot NXT instruction

manual, 2009. www.mathworks.com/matlabcentral/

fileexchange/ 13399/.

[9] L. McNinch, R. Soltan, K. Muske, H. Ashrafiuon,

J. Peyton Jones. An experimental mobile robot platform

for autonomous systems research and education. In:

, in press, 2009.

[10] G. Antonelli, S. Chiaverini, G. Fusco. A calibration me-

thod for odometry of mobile robots based on the least-

AUTHORS

References

-


Center for Nonlinear Dynamics and Control Villanova

University, Villanova, PA 19085 USA. E-mails:

[email protected],

[email protected],

[email protected],

[email protected],

[email protected].

Journal of Dynamic Systems,

Measurement, and Control

Proc. of the 10 International Confe-

rence on Knowledge-Based Intelligent Information and

Engineering Systems, volume 4252 of Lecture Notes

in Artificial Intelligence

Proc. of

the 2009 ASME IDETC/CIE Conference

Proc. of the 2009 ASME IDETC/CIE

Conference

The International Journal of Robotics

Research

Robotics and Autonomous Systems

Proc. of the 20 IEEE International Symposium on

Intelligent Control and the 13 Mediterranean Confe-

rence on Control and Automation

Proc. of the 14 IASTED International Conference on

Robotics and Applications

th

th

th

th

squares technique: Theory and experimental validation.

, 2005, 21(5), pp. 994-

1004.

[11] Y. Kanayama, Y. Kimura, F. Miyazaki, T. Noguchi.

Astable tracking control method for an autonomous mo-

bile robot. In:

, 1990, pp. 384-389.

[12] R. Fierro, F. L. Lewis. Control of a nonholonomic mobi-

le robot: Backstepping kinematics into dynamics.

, 1997, 14(3), pp. 149-163.

IEEE Transactions on Robotics

Proc. of the 1990 IEEE International Con-

ference on Robotics and Automation

Jour-

nal of Robotic Systems

VOLUME 5, N° 1 2011

Abstract:

1. Introduction

In this paper, we propose a novel framework for un-

known environment planning of manipulator type robots.

Unknown environment motion planning, by its nature,

requires a sensor based approach. The problem domain of

unknown environment planning, when compared to model

based approaches, is notoriously harder (NPHARD) in

terms of demanding technical depth especially for difficult

cases. The framework we propose herein is a sensor based

planner composed of a sequence of multiple MBPs (Model

Based Planners) in the notion of cognitive planning using

realtime rehearsal. That is, one can use a certain model

based planner as a tactical tool to attack location specific

problems in overall planning endeavor for an unknown

environment. The enabling technology for the realtime

rehearsal is a sensitive skin type sensor introduced in the

paper. We demonstrate the feasibility of solving a difficult

unknown environment problem using the introduced

sensor based planning framework.

Keywords: sensor based planning, randomized sampling,

unknown environment motion planning, collision avoi-

dance, cognitive planning.

Unknown environment motion planning is one of the

most daunting tasks in path planning study. Sensor based

approaches have been the dominant trends in the study of

unknown environment planning for decades. When it

comes to unknown environment planning, a planner calls

for continuous perception and planning, thereby closing

the loop between sensation and actuation. For instance,

SLAM (Simultaneous localization and Motion planning)

is one of the trends to solve mobile robot navigation

problems in unknown environments.

In manipulator planning especially for unknown envi-

ronments, similar notion is applicable to solve difficult

cases. For instance, in [1], Lee and Choset promoted the

GVG concept to HGVG to solve higher order unknown

environment planning problems. The main point of study

is to introduce a new roadmap (HGVG) construction me-

thod by which a systematic roadmap of free configuration

space can be incrementally constructed using line-of-sight

sensor data.

Another example is introduced in [2], where simul-

taneous path planning with free space exploration is pro-

posed using a skin type sensor. In their approach, a robot is

assumed to be equipped with a sensitive skin.

The roadmap approach proposed by Yu and Gupta [3]

solves sensor-based planning problems for articulated ro-

bots in unknown environments. They incrementally build

a roadmap that represents the connectivity of free c-spa-

ces. But the usefulness of collision sensor attached on

the end-effector is uncertain to detect all the possible

collisions.

Sensor based planners have been advanced by numero-

us technological breakthroughs in sensor technology inclu-

ding laser scanner, vision sensor, proximity sensor, etc. De-

spite of advanced today’s sensor technologies, unknown

environment planning still falls short of a fullfledged solu-

tion especially to tackle difficult cases. On the other hand,

model based planners are also limited in their capability to

deal with certain cases of planning problems due to the

absence of realtime environment mapping capability.

Sensitive skin [3] proposed by Lumelsky and inno-

vated thereafter by Chung and Um [5] poses itself as a tool

to bridge the gap between the model based planning and

unknown environment planning domains. It is unique in

that it can either report impending collision with any part

of the body at any moment or impart a realtime environ-

ment mapping capability during the course of planning

operation. Endowed with such uniqueness, sensitive skin

will result in a SLAM capability for many degrees of

freedom robotic manipulators.

In addition,

We also believe that if a sensor

based planner utilizes model based planning strategy in

case sensitive manner, majority of the planning problems

can be resolved (See Figure 1).

To that end, we propose a novel framework of sensor

based planning constituting the totality as a sequence of

model based planners using a sensitive skin and realtime

rehearsal in cognitive sense.

The notion of

cognitive sense of planner imparts the decision capability

so that the planner can select the best strategy case by case

in each local area to increase the probability of overall

convergence (See Figure 2).

For instance, if the perceived local area at a step is with

a reasonably open space, one can choose PRM [4] or RRT

[7] for fast sampling with evenly distributed sample

points. But importance sampling or adaptive sampling [8]

can be selected for path findings in areas with difficulties.

For better results in narrower passages with many degrees

of freedom, bridge test algorithm [9] can be selected for

more effective path search.

we believe that the sensitive skin is unique

in that many advanced strategies studied in the model

based planning problem domain can be utilized to solve

unknown environment planning problems, including pro-

blems with difficult cases.

In each step of sequence, one

can use a particular model based planer tailored to attack

location specific problems perceived by the realtime

sensing capability of a skin type sensor.

A FRAMEWORK FOR UNKNOWN ENVIRONMENT MANIPULATOR

MOTION PLANNING VIA MODEL BASED REALTIME REHEARSAL

Dugan Um, Dongseok Ryu

Received 18 ; accepted 29 .th June 2010 September 2010th


Articles30

Fig. 1. Sensitive skin as a tool to bridge the gap of two

planning domains.

Fig. 2. Concept of sensor based planning framework.

The new framework proposed in this paper is to host

multiple MBPs as tactical tools within which the best local

algorithm will be eclectically selected based on spatial

examination. Therefore, of importance in the proposed

planner is the cognitive sense of spatial perception for the

optimality in the MBPselection process.

When the planning expands its scope to perception and

action, sampling and path finding problems will fall into

a cognitive problem domain. In recent literatures, eviden-

ced often is a trend of the probabilistic motion planning

expanding its scope from planning to perception and

action in c-space for better manipulability. As detailed in

[10], for example, the planner moves the mobile base to

a less clustered area to obtain better manipulability to

achieve higher success ratio in grasping path planning. In

[11], next best view search is discussed to increase the suc-

cess ratio in which the constructed 3D spatial information

of a work space is utilized.

In the framework we propose, for demonstration pur-

pose, global planning objective is rather simple and simi-

lar to that of the potential field planner, thus the robot is

simply biased toward a goal seeking the shortest path,

while the local planning objective is divided into percep-

tion and action objectives as below.

Perception objective:

Action objective:

Obtain environmental affordance.

In crowded area, increase traversability.

In less crowded area, increase manipulability and obser-

vability.

The environmental affordance we measure is the

spatial distribution of sampled spaces. To that end, the

realtime rehearsal via a sensitive skin type sensor takes

place to generate a local c-space map and to examine the

spatial distribution affordance. Increased manipulability

and observability as well as traversability result in proba-

bilistically higher reachability to the goal position. In

order to meet the action objective, we propose an action

schema of multiple MBPs, among which the best suitable

one will be exercised.

In summary, the robotic action selection will occur as

the action schema in local area, which is cognitively con-

nected to the perception stage by the affordance relations.

Before we discuss the proposed framework, the details of

the IPAskin sensor are discussed in section II, followed by

the algorithm with simulation results in III.

2. IPASkin sensorThe enabling technology for the realtime rehearsal

is a sensitive skin type perception sensor, namely IPA

(Infrared Proximity Array) skin sensor from DINAST

(Figure 3). Included in Figure 4 are some demonstration

pictures of the IPA skin sensor capability. The 3D depth

measurement capability of the sensor is shown in pictures

at upper left and right corners. With this capability, the

proposed framework enables instantaneous 3D virtual

map generation for locally sensed areas. Lower left side

picture depicts a test for finger tracking function of the IPA

sensor to demonstrate a realtime operation capability.

Finally the one on the lower right corner is the recons-

tructed 3D surface map of a human face to show precision

surface modeling capability of the IPAsensor.

From the unknown environment planning standpoint,

the IPA skin sensor is unique in that it can impart a col-

lision shield (See Figure 5) to an entire manipulator so that

the IPA equipped robot can sample an arbitrary c-space in

realtime, thereby perform realtime collision check in

work space. One example of an IPA sensitive skin equip-

ped robot is shown in Figure 6.

In summary, IPA sensor endows the reatlime rehearsal

capability to a multi-DOF robotic manipulator with

following features.

1. Collision shield

2. Realtime workspace mapping

Fig. 3. IPA skin sensor, sensing range.


Articles 31

depending on applications at the cost of resolution loss.

Active retina principle can also be adopted for regional

magnification for high variant areas. In addition, IPA

sensitive skin is equipped with a fish eye lens with image

dewarping capability, thus imparting complete hemi-

sphere coverage for maximum sensibility (Figure 3).

Prevention of crosstalk between sensor modules is one

concern when multiple IPA sensitive skin sensors are put

together for a collision shield. For instance, IR light emit-

ted from one sensor can be seen by another IPA module as

an object, which results in a false alarm. In order to

prevent crosstalk, a higher level control logic is in need for

robust sensing and 3D mapping. Figure 7 shows the firing

diagram based on groups of sensors that has little or no

correlation in sensitivity due to geometric configuration.

The higher level controller will drive each group of sen-

sors in sequence to prevent crosstalk based on the firing

diagram in. The firing diagram will result in 50% increase

in sampling time due to the sequential IR light emission.

Frequency modulation can be used for minimum loss in

sampling time if more groups of sensor are needed.

Table 1. IPA sensitive skin sensor specification.

Fig. 7. Multiple Sensor Firing Diagram for crosstalk

prevention.

2.2. Cross talk prevention between modules

3. RRT-Cabomba plannerThe planner we propose to implement the concept in

Figure 2 and to maximize the usefulness of the IPA sen-

sitive skin is a RRT variant, namely RRT-Cabomba. The

planner resembles how a natural aquatic cabomba stret-

ches in water (Figure 9). RRT-Cabomba searches an un-

known space based on local probing via the IPA skin and

develops a local map in a virtual workspace perceived in

sensing range. In each local virtual workspace, RRT-

Cabomba grows RRT to examine spatial distribution.

Depending on the result of the spatial distribution,

RRT-Cabomba will determine which MBPis the most suit-

able for the sensed local area. Search completeness in local

area will be granted as long as each MBP used is a com-

plete planner, but the global search completeness has yet to

be proved as a whole. The sensor model (Figure 8) used for

Fig. 4. IPA skin sensor demonstration.

Fig. 5. The concept of collision shield.

Fig. 6. IPA sensitive skin installation on Adept robot.

2.1. IPAsensitive skin sensor specification.

IPA sensitive skin is a DSP technology based smart

sensor with sensor array of about 0.3 million sensors in

synch with each other. At the moment, the refresh rate of

the sensor is 20 ms for realtime operation. The sensor itself

is compact in size, thus can comply with any surface

configuration with a 3D realtime surface modeling power

to support advanced motion planning capability. Detailed

sensor spec is in Table 1.

The frame rate of the IPA sensitive skin is adjustable


Description

Size

Sensing range

Sensing rate

Measurement error

Viewing angle

S

±

pecification

6.5cm x 4.5cm x 4cm

350mm

20Hz at 640*480 resolution

8mm at 350mm

hemi-sphere complete coverage

Articles32

simulation is similar to the IPA sensitive skin depicted in

Figure 3, but in 2D plane.

RRT-Cabomba Natural cabomba

In summary, the RRT-Cabomba is a planner with the

following logic.

1.

Sense a local workspace.

2.

Generate a local virtual environment with measured

3D depth information.

3.

Grow an RRT in local virtual environment established

in step 2.

4.

Measure the spatial distribution of the grown local tree.

Determine which MBP is the most suitable for the

sensed area.

5.

Move the robot to the next position depending on the

MBPstrategy chosen at step 4.

6.

Check if the goal position is reached. If not, loop back

to step 1.

In step 1, we apply a global algorithm such that the

cabomba tree is steered toward the goal position by

controlling the sensing direction biased toward the goal

position. Steering toward a goal generally, but not always,

yields faster convergence to the goal at the cost of being

entrapped at local minima. Sensor data collection is neces-

sary to build a complete local environment model to fully

grow a c-space RRT in each movement.

Fig. 8. Sensor model.

Fig. 9. RRT-Cabomba.

Sensor data collection

Virtual workspace generation

Realtime rehearsal

Cognitive decision

Advance the robot

Convergence check

RRT-cabomba

In step 2, piecewise information from each sensor is

put together to form a complete virtual environment.

Cartesian coordinate with orientation of each sensor on the

manipulator body is obtained forward kinematics. Ob-

tained data of each sensor are then mapped and projected

in a virtual environment for workspace object construction

followed by a realtime rehearsal. Growing a c-space RRT

with respect to a constructed local workspace provides the

result of realtime rehearsal for collision in workspace, and

at the same time, generates sampled nodes in c-space. We

utilize order standard distance distribution as a mea-

sure of spatial distribution in Step 4 as detailed below.

where is the sampled nodes of joint and is the

mean value of . , if larger than , signals an open

c-space, or crowded space otherwise. The value of

is determined empirically. In our case, 0.3 yields reason-

able performance (maximum value is 0.5). For step 5, in

our simulation, we only use following two MBPs for open

and crowded cases.

(Open c-space): Move to a node closest to the

mean values of sampled nodes.

(Crowded c-space): Move along the axis with

largest standard deviation in c-space.

For , as mentioned earlier, moving to a mean

value of offers probabilistically best location for next

via

nth

MBP #1

MBP #2

Fig. 10. RRT-Cabomba for difficult area path search test.

Fig. 11. Comparison between original and one with bug

algorithm.

MBP #1

i th mean

i N threshold

threshold

i

� �

�

�

� �

�

�

i


( )��

��N

i

meaniNN 1

21��

RRT-cabomba switching

to bug algorithm

in crowded area

Original RRT-cabomba

Articles 33

move in terms of maneuverability for each joint, thus may

increase reachability as well. The is chosen for

reasonable traversability, but can be replaced with variety

of other MBPalgorithms feasible for specific needs in each

local area.

One execution example of RRT-cabomba is shown in

Figure 10.

Here we use the identical sensor model as the physical

IPA skin sensor, hemisphere frontal sensing range with 3

DOF (x, y, ) mobility.

One big drawback of RRT-Cabomba is not being able

to handle local minima problems effectively due to the

global steering algorithm. Global steering mechanism in a

completely unknown environment is still a daunting and

not a fully addressed issue. Therefore, in this study, we

focus more on reasonable search results for a completely

unknown environment with RRT-Cabomba planner in

conjunction with the IPA skin sensor, leaving the global

completeness of the algorithm a future work. As for the

algorithm complexity, since RRT-Cabomba uses mixture

of local algorithms depending on the situation, the com-

plexity varies as the planner changes. For instance, if the

local planner selected is the RRT planner, than it has the

same complexity of RRT in the local area. However,

although c-space construction is known to be polynomial

in the geometric complexity for unknown environments,

the proposed algorithm effectively generates c-space map

as the robot explores the environment. Therefore the c-

space construction is not the bottleneck of the algorithm.

In order to demonstrate the versatility of the RRT-

Cabomba, in this case to tackle the local minima problem,

we change the to the bug algorithm. The bug

algorithm proposed by Dr. Lumelsky [12] and improved

thereafter has a unique property of global convergence in

2D and 3D unknown environments. For higher order

manipulator path problems, one can utilize algorithms

introduced in [8], [9], [13]. These algorithms deal with

difficult cases in higher order path problems.

In step 4, if is smaller than , we switch the

local strategy to the bug algorithm. Shown in Figure 11 and

Table 1 is the results of the previous RRT-Cabomba and the

one with the bug algorithm. No significant statistical diffe-

rence is evidenced in running performance.

Notice though that the second planner follows the wall

of the first obstacle it encountered because of the bug algo-

rithm engaged in crowded areas. In order to demonstrate

the local minima avoidance capability of the RRT-Cabom-

MBP #2

Table 1. Results of two algorithms

Local minima avoidance with bug algorithm

MPB #2

�

� �N threshold

ba, another simulation set is prepared. Shown in Figure 12

is an execution example with the bug algorithm as the

second MBP. As demonstrated in the figure, a group of

MBPs in conjunction with realtime rehearsal produces

reasonable results in a difficult unknown environment

case.

One limiting factor of the RRT-Cabomba from the

implementation perspective in real world is the realtime

rehearsal for advancing a manipulator without interruption

when continuous motion is needed. In order to perform the

realtime rehearsal, however, virtual world generation with

3D-depth measure of a local area followed by a realtime

rehearsal with RRT in local area has to take place with zero

time lag. In the previous simulation with a computer

equipped with an Intel Core 2 CPU at 2.13 GHz and 2Gb

RAM, each foot step takes 3.7 second in average, meaning

the robot in the real world has to wait for 3.7 second in each

step motion for path search. This bottleneck can be tackled

with several improvements in implementation such as

faster computer, smaller step size, distributed simulation

and so forth. However, the algorithm needs to be signifi-

cantly improved for a full scale, 6 DOF robotic manipu-

lator operating in a completely unknown environment.

A novel manipulator path search framework with

a sensitive skin type sensor for a completely unknown

environment planning, especially for difficult cases with

local minima, has been investigated. To that end, a novel

IPA sensitive skin has been developed and demonstrated

its capabilities in the paper.

The proposed algorithm, RRT-Cabomba, provides

a unique solution for difficult unknown environment plan-

Fig. 12. Local minima avoidance with bug algorithm as

a crowded area search strategy.

Table 2. Local minima avoidance simulation.

4. Conclusion


Algorithm

Search time

Total collision

nodes

Total collision

free nodes

Total No.

of nodes

Original RRT

- Cabomba

125 s

6838

21218

28056

RRT-Cabomba

with bug algorithm

155 s

5892

21162

27054

Algorithm

Search time

otal collision nodes

otal collision free nodes

Total No. of nodes

T

T

Total foot steps

RRT-Cabomba

with bug algorithm

187 s

11512

25562

37074

50

Articles34


ning cases by merging sensor based planning and model

based planning ideas for maximum synergy. In RRT-

Cabomba, multiple MBPs can be employed to tackle local

area specific planning problems. Cognitive decision

making has been studied with realtime rehearsal for each

step motion to eclectically select the best possible local

strategy in each step. For the feasibility test of the propo-

sed algorithm, a series of simulations has been performed

and the results are shared in the paper.

Time-lag in simulation due to expensive calculations

would be a bottleneck for a real world implementation, but

for the proof of the usefulness, the concept of realtime

distributed simulation of the proposed algorithm is under

investigation at the moment. In addition, real world imple-

mentation with IPA sensitive skin is under consideration

for future work.

- Texas A&M University –

Corpus Christi, Science and Technology Suite 222, 6300

Ocean Drive, Unit 5797, Corpus Christi, Texas 78412.

Tel. 361-825-5849, Fax 361-825-3056.

E-mail: [email protected].


AUTHORS

*,

References

Dugan Um Dongseok Ryu

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th rd

th th

VOLUME 5, N° 1 2011

Articles 35

Abstract:

1. Introduction

In this paper an autonomous leader-follower is pre-

sented and tested in an unknown and unpredictable envi-

ronment. Three different types of controller named as

First principles-based proportional (P) controller, Fuzzy

Logic Controller, and Model-based Predictive Controller

are developed and tested in real-time to provide a smooth

following behaviour. The follower used the leader's status

sent by a smart phone to differentiate between obstacles

and the leader and then using two types of sensor, laser

and sonar, during the obstacle avoidance procedure. In

order to identify the leader again out of many obstacles

around, two alternative techniques are proposed using

superposition of the scans collected by the laser and pre-

dicting the leader's trajectory using evolving Takagi-

Sugeno (eTS). At the end, experiments are presented with

a real-time mobile robot at Lancaster University.

Keywords: leader-following robot, human-robot interac-

tion, evolving Takagi-Sugeno.

The role of robotics has grown significantly in wide

variety of applications such as defence, security, industry,

etc.

Many autonomous robots are developed to operate

with humans in work environments like nursing homes

[1], hospitals [2] and office buildings. In order such robots

to be socially acceptable to people, they need to interact

with humans and navigate in such a way that people ex-

pect them to do without using specific technical exper-

tise.Also, in some applications robot is required to operate

in an unknown environment and needs to posses the capa-

bility of performing multitude of tasks autonomously

without complete a priori information while adapting to

continuous changes in the working environments [3]. This

paper focuses on the ability of mobile robots to follow a

moving object/leader. Following the leader can present

significant challenges. In order to follow moving objects

in an unknown environment, the robot should be aware of

the surrounding obstacles and also be capable of distingu-

ishing obstacles from the leader. Furthermore, the follo-

wer should be aware of any, possibly unpredictable, beha-

viour of the leader beforehand and respond to that.

This paper describes an intelligent person-follower

behaviour for Unmanned Ground-based Vehicles (UGV)

aiming to follow the movements of the leader with un-

known trajectory or kinematic model of motion. The pro-

posed behaviour requires minimal or no intervention from

the leader while following him/her in an unknown and

unpredictable environment. It should be highlighted that

it operates autonomously with high-level of intelligence

and situation awareness in contrast to currently available

UGVs which are operated remotely, but manually [4], [5]

and often rely on GPS or pre-loaded maps.

The remainder of the paper is organized as follows.

First, in Section 2 related works on person-following un-

der investigation is summarized and some challenges in

leader-follower tasks are pointed out. The proposed ap-

proach is briefly outlined in Section 3. Section 4 describes

the basic follower by introducing three different control-

lers, namely, a first principle controller, a fuzzy logic con-

troller, and a predictive controller. Section 5 introduces

detecting obstacles and the method for differentiating

them from the leader. Sections 6 and 7 describe status of

the leader's motion and obstacle avoidance behaviours.

Section 8 describes how the follower rejoins the leader

after avoiding the obstacle. Section 9 displays the experi-

mental results. At the end, Section 10 provides conclusion

and future works.

2. Related WorkOne of the most common methods in person following

is to use an external device to attach to a person/leader or

wearing an emitting device located in the range of sight of

the mobile robot [6], [7]. However, existing systems that

provide positioning information are not a practical solu-

tion since they mostly rely on pre-installed and calibrated

environment infrastructures [7]. Several researchers have

investigated using a camera for tracking moving objects.

In [8] a camera-based method is used for face detection of

the leader; nevertheless, this method is not robust due to

varying background colour and illumination conditions as

the robot moves through various environments. Tarokh

and Ferrari [9] implemented a fuzzy controller with came-

ra-based target-position estimation to produce steering

speed commands to keep the moving target centred in the

camera view. In order to improve the target recognition

algorithm, they allowed a few objects besides the target to

enter the camera's field of view [9]. To address the diffi-

culties of the camera-based method for person-following,

the method developed in [10], [12] integrated laser-based

and camera-based techniques together. Laser is used to

find the leader's legs and a camera to detect the leader's

face. Using face detection, however, requires the leader

always to face the robot, and is, thus, practically impossi-

ble and inconvenient for the leader when the robot follows

the person behind. On the other hand, Cielniak and his

colleagues used an omni-directional camera [11], which

requires a new training of an artificial neural network for

each person that needs to be followed. Such a requirement

INTELLIGENT LEADER-FOLLOWER BEHAVIOUR

FOR UNMANNED GROUND-BASED VEHICLES

Pouria Sadeghi-Tehran, Javier Andreu, Plamen Angelov, Xiaowei Zhou

Received 6 ; accepted 20 .th May 2010 December 2010th


Articles36

restricts the generality of the method. Other methods for

person-following require a previously acquired map of the

environment. Montemerlo and colleagues developed a la-

ser-based leader-follower using mobile robots [13]. How-

ever, in order to follow a person by a robot a prior map of

the environment is required which makes the method un-

suitable for cases where a map of the environment is un-

available or the robot is required to frequently change the

place during its operation. Our proposed approach is simi-

lar to Shaker and Saade [14], who use a laser range finder

to detect a person's position providing distance informa-

tion to the system for the control process. However, they

used the relative velocity of the robot to the person's leg

and the difference between the relative distance and the

safe distance in [14] instead of using the angle/bearing of

the robot towards the moving object (leader) as the inputs

of the fuzzy inference system as we do. Moreover, the au-

thors of [14] did not address the challenge when the robot

meets an obstacle and the algorithm is only tested in in-

door environment when no other obstacles are around.

3. The proposed approachThe approach introduced in this paper is innovative

and has been implemented on a laboratory demonstrator

for UK Ministry of Deference (MoD) funded project [31].

It has a hierarchical architecture and three main layers

(Fig. 1). The Basic Follower procedure is operating in the

first layer which will be explained in more details in the

following section. The second layer is activated only when

the follower meets an obstacle and avoids it by manoeuv-

ring around. At the third layer, the follower determines the

current position of a person and rejoins the leader.

The key feature of the person-following behaviour is

maintaining a distance and heading of the robot directed

towards the leader [15]. The robot has to maintain a certain

safe distance from the leader and, at the same time, follow

the leader in a smooth motion. To achieve a robust trac-

king, a first principle controller based on the linearization

Fig. 1. Schematic representation of the proposed approach

with its three layers: 1) Basic Follower; 2) Obstacle avoi-

dance; 3) Rejoining.

error [16] is used.Alternatively, in order to obtain more ac-

curate tracking, a fuzzy controller can be applied. Note,

that a well-tuned fuzzy controller [17], [23] can also achie-

ve a higher accuracy comparing to the simple linear con-

troller. However, the challenging issue to the conventional

controllers is that they generate the manipulated value

(control command) according to the observation of system

status at the current and the past time instants while the

purpose of the control is to minimise the observed error in

the forthcoming (future) time instant. Taking into account

the dynamic nature of the target system, this delay, in turn,

may lead to larger errors. In order to minimise the response

delay and improve the control quality, we propose a pre-

dictive controller using evolving Takagi-Sugeno (eTS)

fuzzy rules [19], [20] as a model predictor. This novel me-

thod [18], [19] allows the TS fuzzy model to be designed

on-line during the process of the control and operation.

This is especially suitable for applications such as UGV

autonomous navigation where the mobile robots operate in

an unknown environment [21].

The person-follower algorithm has the role of provi-

ding the information needed by first principle/fuzzy/ pre-

dictive controller to control the motion of the robot. The

algorithm analyses the information provided by the laser

sensor to detect the position of the leader. Laser provides

the distance, relative to the robot and angle/bearing to-

wards the moving target, measured in real-time (Fig. 2).

The obtained relative distance and relative bearing are then

made available as input information to the controller. The

outputs (control action) are the velocities of the robot's

wheels which will determine how much speed change is

needed by the robot in order to follow the leader.

The aim is maintaining a pre-defined distance, and

a bearing angle of 0 so that the target is closely followed

without a collision to any obstacle. Note, that due to unpre-

dictable movement of the target/follower, there will be

some misalignment between the heading of the leader and

the robot (bearing in Fig. 2).

In an unknown environment, the follower should be

aware of all surrounding obstacles and have enough intel-

ligence to detect and avoid them. To make this happen, we

propose two conditions to be checked by the follower. The

first condition is a sudden and large drop in the distance

d

d

Fig. 2. The leader (target) and the follower attempting to

keep a constant distance, dref and to nullify the misalign-

ment, .

�

�

�

�

ref


Basic FollowerProcedure

if there isan obstacle

Obstacleavoidancebehaviour

Determine wherethe leader is

Rejoiningthe leader

abso

lute

y-d

irec

tion

absolute x-direction

robot x-direction

�x

�y

d

d ref

Headin

g

roboty-

direct

ion

robotfo

llow

er �

( , )x yo o

( , )x yn n

Articles 37

d

d

V

V

Fig. 3a. Distance component.

Fig. 3b. Angular component.

ref

ref

f

max

which leads to a large acceleration of the robot. How-

ever, if the distance is smaller than (the required refe-

rence distance we want the UGV to follow the leader at),

the velocity component, is set to Negative. The tech-

nical limit of the Pioneer 3-DX robot is = 1400 mm/s.

(4.1)

(4.2)

Fuzzy controllers have recently been used in a variety

of applications such as underground trains, robotics, etc.

owing mainly to their flexibility and higher accuracy

which is achieved due to their non-linearity. The data from

the detection of moving objects provided by the laser is,

sometimes, noisy and could be, occasionally, incorrect.

Therefore, in order to provide a more flexible (non-linear)

relation between the inputs (distance and bearing to the

target/leader) and outputs (the velocities of both wheels),

a Zadeh-Mamdani type FLC [23] has been implemented

which consists of twenty five fuzzy rules (Table 1) for

each left/right robot wheels.

4.2. Fuzzy Logic Controller (FLC)

which causes the negative velocity of the wheels; the se-

cond alternative condition is the signal from the leader

which indicates its motion status.

After collision detection and avoidance procedure, the

follower should be able to determine the new position of

the leader out of, possibly, many objects in front of it, and

rejoin the leader again. Two alternative techniques aiming

to identify the current position of the leader are introduced

which rely on the prediction of the trajectory of the leader

and on super-positioning of the scans taken by the laser.

The proposed approach for person-following will be

explained in more details in the following sections.

In order to track the leader, the heading of the mobile

robot is controlled indirectly through the difference of the

velocities of both wheels. Three alternative controllers are

used to control the robot motion while following a person.

1) First principles-based proportional (P) controller;

2) Fuzzy Logic Controller (FLC);

3) Model-based Predictive Controller (MBPC).

The controller is based on the explicit linear des-

cription of the problem. It keeps acceleration of the robot

proportional to the distance to the target/leader, [22].

Due to the inertia of the real systems it takes a short period

of time after a velocity command is received by the motor

for the desired velocity to be reached. The velocities of

both wheels (left and right) are selected as control values

and the turning of the robot is achieved by control of the

velocity difference between the left and right wheels.

When the velocity of the left wheel is higher than the

velocity of right wheel, the robot makes a right turn and

vice versa. Based on these principles, the wheel velocity

control model is described by the following equations:

It consists of two components; the component for

maintaining , and the pair of velocities and to de-

termine the heading of the mobile robot. The two com-

ponents are defined by the following equations:

(3)

where is threshold of insensitivity which filters the

sensors; and are proportionality coefficients. = 2

and = 3, are chosen based on preliminary tests)

Fig. 3 depicts the linear proportionality between; a) the

velocity of the robot and distance to the target measured

from the robot (Fig. 3a); b) the heading angle (determined

by the left and right velocities) and the angle/bearing to the

target/leader respectively (Fig. 3b). When the distance

between the robot and the target is too large, the velocity

component gets a higher value in order to maintain the

4. Basic Follower

(1)

(2)

4.1. First Principles-based Controller

P

d

V

d V V

k k k

k

V

f

ref i r

f

�

1 2 1

2


0 0��

k2� � �k2��

V k d df ref= ( )1 �

V V + V

V V + Vleft f l

right f r

=

=

�

�

�

� �Vl = ; Vr =� �

a = k1 2� ��

�� a = a =1 2 0

��a = k2 2 ��

�

�whenr =

whenr =

�

��

��

Articles38

Table 1.a. Rule table of "Left Velocity".

(N=Negative, SN=Small Negative, S=Small, SP=Small

Positive, P=Positive, QB=Quick Backward, SB=Slow

Backward, H=Hold, SF=Slow Forward, QF=Quick Forward)

Table 1.b. Rule table of "Right Velocity".

d

Fig. 4. FLC schematic diagram.

d

R R

Each rule represents a typical situation during the

"Leader following" task. The input vector is formed based

on real-time measurements by the sensors (laser or sonar)

providing the distance, and angle/bearing (see Fig. 4).

The closeness between the measured input vector and

the focal points of each fuzzy set is calculated based on

triangular/trapezoidal membership functions illustrated in

Fig. 5. The result is aggregated to form the degree of firing

for each rule and normalized and aggregated further to

form the overall output of the FLC [23].

(5)

where denotes the i membership function; and are

the inputs; - number of the fuzzy rules, =25.

The commonly used aggregation method called "cen-

tre of gravity" is applied to determine the firing strength of

each fuzzy rule as follows:

(6)

�

��ith

(7)

where =[ ,..., ; ,..., ] are the parameters of the

consequent part of the FLC.

The antecedent part of the fuzzy rules is defined by

linguistically interpretable terms that describe the distan-

ce (Fig. 5a) and angle/bearing (Fig. 5b); the consequent

fuzzy sets are defined in respect to the left velocity and

right velocity (Fig. 6).

As mentioned earlier, the key aim in using a controller

is to minimise the observed error provided by the observa-

tion of the systems status at the current and the past time

instants. However, delay in response caused by dynamic

nature of the target may lead to large errors. In order to

minimise the error caused by the delay, a more advanced

class of controllers is used, namely model-based predicti-

ve controllers (MBPC) [24], [27]. MBPC is an optimal

a a a a a

Fig. 6 Fuzzy sets for left/right velocity (mm/s).

11 1 21 2R R

Fig. 5a. Fuzzy sets for distance (mm).

Fig. 5b. Fuzzy sets for angle/bearing (deg).

4.3. Model-based Predictive Controller


N

SN

S

SP

P

N

SN

S

SP

P

�

�

d

d

Crash

QB

QB

QB

QB

QB

Close

QB

SB

SB

SB

SB

Proper dist.

H

H

H

H

H

Not Far

QF

SF

SF

H

H

Far

QF

QF

QF

SF

SF

Crash

QB

QB

QB

QB

QB

Close

SB

SB

SB

SB

QB

Proper dist.

H

H

H

H

H

Not Far

H

H

SF

SF

QF

Far

SF

SF

QF

QF

SF

�i = i

R

i = 1�� i

i i i= ( ) ( ) = [1,..., ]� �1 2d i R� �

� � �1 2 .... R

a a11 21

a a12 22

a a1 2R R

� � �1 2 .... R

V

Vl

r

d

�=

....

Articles 39

control strategy that uses the dynamic model of the system

to obtain an optimal control sequence by minimising an

objective function (8).

The MBPC used employs a discrete-time prediction

model and control law. The discrete-time model of the ro-

bot motion used can be given by [35]:

(8)

where and are the linear and angular velocities; and

is a sampling period.

In a general, state space form it can be given as:

(9)

where = [ ] is the control input; and = [ ] des-

cribes the configuration

The leader's trajectory and the robot motion control

(velocities) vector, u are related by:

(10)

The problem of trajectory leader tracking can be stated

as "to find a control law in such a way that at any time in-

stant", satisfies:

(11)

This tracking problem is solved by employing MBPC.

At each sampling instant, the model is used to predict the

behaviour of the system over a prediction horizon, . The

predicted output values, denoted ( ) = 1,...,

depend on the state of the process at the current time in-

stant, and on the future , ( ) =

0,..., 1, where is the . The sequence

of future control signal ( ) = 0,..., 1, is com-

puted by optimising a given objective function, in order to

track the reference trajectory, as close as possible [27],

[37].

(12)

where 0, 0 is the output error and control increment

weighting

The first term accounts for minimising the variance of

the process output from the reference, while the second

term represents a penalty on the control effort.

Ohya and Monekata [24] proposed an algorithm to

predict the next position and speed of the leader based on

the history of the leader's position with time instance re-

corded. However, they assume that the leader will move

with the constant acceleration and same angular velocity,

which is unrealistic. On the other hand, some other appro-

aches [25], [26] propose using the predictive target trac-

king algorithm based on the well established Kalman filter

v T

u v w X x y

x

k

H

X k + j for j H

k control signals u k + j for j

H H control horizon

u k + j for j H

x

�

�

�

�

� � � �

T T

n

n

p

p

c c

c

r

and report achieving high reliability. Babuska [27] used

a Takagi-Sugeno (TS) fuzzy model as a model predictor;

however, the model was predefined off-line with a fixed

structure which assumes knowing the order and nature of

the non-linearity associated with the leader's motion. In

this paper, we propose to go further and use evolving Ta-

kagi-Sugeno (eTS) fuzzy ruled-based systems [18-20],

[36] which requires no assumptions on the order and com-

plexity of the non-linearity and the structure of the con-

troller. We propose a MBPC that is using eTS (eMBPC) to

predict the next position of the leader online for a horizon

of several steps ahead based on the historical observations

(time-series of realtime readings).

(13a)

(13b)

where (.) is a short-hand notation for the eTS fuzzy

rule-based model and -represent the memory used.

In this technique, the controller continuously estima-

tes the future predicted position of the leader as he/she

moves. Once the leader's future position is estimated, the

motion controller implements the required control signals

to the robot wheels having in mind the position of

the leader. This leads to minimising the tracking error cau-

sed by the delay in response of the controller and increa-

sing the performance and reliability of the Leader follo-

wing behaviour.

MBPC operates in real-time, on-line and is self-lear-

ning (both in terms of its parameters and in terms of its

structure). Moreover, it does not need to be fed by an ini-

tial structure or tuned which makes it generic. The data are

processed on-line (in one-pass) and, therefore, it requires

very limited computational resource and is suitable for on-

board implementation on the mobile robots (UGV). The

improved performance of the prediction is discussed in

Section 8

Differentiating between an obstacle and the is

one of the challenging aspects of this task. As mentioned

earlier, in Section 3, distance and angle/bearing to the

nearest object are used as inputs of the controller. If the

leader disappears from the view of the robot after meeting

and obstacle (for example quickly avoiding it), the latter

may be misled. The robot may find it difficult to distin-

guish between cases when the leader stopped and cases

when the leader quickly disappeared and the nearest ob-

ject is, in fact, an obstacle that needs to be avoided. The

UGV should have enough intelligence to differentiate bet-

ween these two cases which may look similar if only dis-

tance and bearing are used as inputs. To address this prob-

lem, we proposed two conditions to be checked;

i. Sudden large drop of the distance

ii. Signal related to the leader's motion status

If the distance to the leader suddenly drops signifi-

cantly (more than ), the robot moves backwards to

maintain the reference distance which leads to a negative

velocity of the robot wheels (Zone B, Fig. 7). Such moves

(that lead to a distance drop) often take place during the

eTS

leader

d

�

future

.

5. Detecting Obstacles

ref


x k x k v k k T( +1) = ( ) + ( ) cos ( )�

� � �( +1) = ( ) + ( )k k k T

X k f X k u k( +1) = ( ( ), ( ))d

X k f X k u kn n n n( +1) = ( ( ), ( ))

xk H k k� ��p= ( , ..., )eTS x x

y y yk H k k� ��p= ( , ..., )eTS

X k X k( ) ( ) =� �n

J X k + j X k + j u k + j= [ ( ) ( )] + [ ( 1)]�� r2 2

y k y k v k k T( +1) = ( ) + ( ) sin ( )�

Hp Hc

j = 1 j = 1

Articles40


process of leader following (Zone A, Fig. 7) but the am-

ount of the distance drop is usually not large since the con-

troller aims to minimise the deviations from . However,

when an obstacle is detected or the leader stops the dis-

tance drop is large and sudden (contrast zone B to zone A,

Fig. 7).

The second condition assumes a short informative sig-

nal from the leader to be sent wirelessly which indicates its

motion status (to be described in more detail in Section 6).

Summarising, the two conditions used to distinguish

the leader (when (s)he stops) from an obstacle are formu-

lated as; i) the mean velocity of both wheels to be negative,

and; ii) the leader to have motion status ' '. If both

conditions are satisfied at the same time, the robot starts

executing an obstacle avoidance manoeuvre (to be descri-

bed in Section 7). This part of the algorithm can be illus-

trated by the following IF-THEN rule.

(mean velocity) < 0 && (leader's status is 'walking')

{

ObstacleAvoidance procedure

}

{

Basic Follower procedure

}

After avoiding the obstacle, the robot switches again

to the Basic Follower procedure (layer 1, Fig. 1)

Avery robust and effective way of informing the robot

about the motion status of the leader (walking or stopped)

is by using a very short signal emitted by a device carried

by him/her, e.g. The latest generation smart phone (Nokia

N97) which includes several on-board sensors and more

than enough processing capacity to handle them. Eventu-

ally, this off-the-shelf device can be substituted by a much

d

Fig. 7. Distance and velocity of the wheels measurements

(data from the real-life experiment).

walking

ref

IF

Else

.

6. Status of Leader's Motion

smaller purpose-build microprocessing system. Nokia

N97 has on board an ARM11 343 Mhz processor and

works with Symbian OS v.9.4. The selected technology to

connect the robot and the leader was Wi-Fi upon the

802.11 b/g standard [33]. We also experimented the use of

another (3G) mobile connection technology; however, it

entails the installation of third party antennas which

would make system deployment very complex. There-

fore, Nokia N97 was selected.

Note, that the message that is transmitted between the

Nokia N97 and the on-board computer of the Pioneer ro-

bot is extremely short (few bits - the status can be a single

bit 0/1 plus the address of the receiver in a datagram pac-

ket) and can be sent at an extremely low bit rate (it can be

done only by request from the robot when in Zone B, Fig.

6) - this situations practically occurs quite rarely, e.g. with

a frequency in mHz range).

The smart phone Nokia N97 offers some useful featu-

res such as; the accelerometer, orientation, azimuth, GPS

data, and distance to the nearby objects by a proximity

sensor. We discarded GPS data because it implies using

satellite connections and also proximity sensor as lacking

enough range and precision.

From a software point of view, the smart phone has

a Java Midlet (J2ME). At the receiver side (the robot/

UGV) a Java Application (J2SE) is deployed on its on-

board computer [28]. This application is in charge of gat-

hering all datagram packets sent by the smart phone

UDP/IP protocol. TCP/IP connections were initially dis-

carded because no order of the data is required, hence gi-

ving priority to the speed of the reply. Using the UDPdata-

gram protocol allows establishing a connectionless net-

work (the address is a part of the packet) which is a con-

venient solution for the particular problem. The sampling

rate (with which the raw data of the acceleration of the

leader are being collected) is being set by default to 40 Hz.

The data from the internal (to the smart phone) sensor

have been filtered and processed before sending over the

channel to avoid peaks and outliers in the signal and to mi-

nimise the communication (sending the status data instead

of the raw acceleration data reduces the bitrate and the

possibility of an interception dramatically). The robot re-

quests a signal from the leader's device only when Zone B,

Fig. 6 occurs (either when the leader stops or an obstacle

obstructs suddenly his/her way). It is important to stress

again that the latter situation happens very rarely with

a frequency in mHz range

The ability to avoid obstacles is one of the most funda-

mental competences in mobile robotics and autonomous

systems. In order to prevent collision with obstacles, the

follower (UGV) should have Obstacle Avoidance beha-

viour. In our case, after the follower successfully disting-

uishes an obstacle from the leader, the ObstacleAvoidance

procedure is being activated. In order to increase its reli-

ability, we used two types of sensors, sonar and laser. So-

nar sensors are used to check if the UGV fully passed the

obstacle, and laser, alternatively, is used to monitor if there

are no obstacles between the robot and the leader. As a re-

sult, the UGV successfully avoided all obstacles that were

identified (see Section 9 for the experimental result)

via

.

.

7. Obstacle avoidance behaviour

VOLUME 5, N° 1 2011

Articles 41


8. Rejoining the leaderAfter an obstacle is being avoided, the UGV have to

identify the leader/target out of the, possibly, many objects

that are visible. Two alternative techniques are proposed

for the re-joining the Leader behaviour;

i. Predicting the leader's trajectory

ii. Superposition of the scans taken by the laser

The module for predicting the movements of the lea-

der contributes to the increase of the situation awareness

and enables the UGV to rejoin him/her once the obstacle is

being avoided. As described in Section 4.3, we use evol-

ving Takagi-Sugeno (eTS) fuzzy rule-based systems [18-

20] for predicting in real-time the position of the leader/

target. This is illustrated in Fig. 8 for one step ahead pre-

diction. However, to predict the position of the leader after

avoiding the obstacle is very difficult (leader may go to

a completely opposite direction). In order to minimise the

error and achieve a more precise re-joining behaviour an-

other alternative technique is proposed using superposi-

tion of the laser's scan which is explained in more details

in the next section.

An alternative technique to implement Rejoin the lea-

der behaviour is proposed which is based on super-impo-

sing the scans taken by the laser in real-time. The idea is to

subtract the motion of the robot and, thus, to leave the ob-

jects that are static clearly separated and visible from the

moving objects for which the cloud (cluster) of points will

be moving. This is illustrated in Fig. 9.

We propose using online clustering to identify the

moving object(s)/target(s) and to distinguish them from

the static object(s). The online clustering is based on

recursive calculation of the Euclidian distance between

the new points in respect to the previously gathered

centres [32]. From Fig. 8 it is clearly seen that points

which belong to static objects remain in the same place of

the space, overlapping the previously gathered points

corresponding to the same objects. (The reason that

overlap in Fig. 9 is not absolutely perfect is the noise

associated with the real measurements and data streams

induced from both laser sensor and movement of the

robot). However, it is easy to distinguish points which

belong to an object from points that belong to moving

8.1. Predicting the Leader's Trajectory

Fig. 8. Leader's Trajectory prediction using eTS (black

dotted line - line trajectory of the leader, red solid line - the

prediction using eTS).

8.1.1. Superposition of the Laser's Scans

objects (e.g. the leader). This approach also provides

information about the distance in terms of direction of the

moving object(s) (e.g. the leader). It is sparse in terms of

space with their total mean deviation bigger than the

others.

The experiment was carried out outdoors (Fig. 10a)

and indoors (Fig. 10b) at Lancaster University in October

2009 with a Pioneer 3-DX mobile robot. The Pioneer 3-

DX [29] is equipped with a digital compass, a laser, sonar

sensors and onboard computer. It is very suitable for va-

rious research tasks including mapping, vision, monito-

Fig. 9. Scans taken by the laser mounted on the mobile ro-

bot. The static obstacles (walls) are clearly seen as clus-

ters of circle dots centers of these clusters were identified

on-line as the square dots; one of the cluster centers was

moving which indicates the leader - it is shown as a string

of cross points (the position of the cluster centers in diffe-

rent time instants are linked with a solid line indicating of

the leader's motion direction).

Fig. 10a. Outdoor experiment.

9. Experimental Result

Fig. 10b. Indoor experiment.

VOLUME 5, N° 1 2011

Articles42


ring, localisation, etc [21], [22]. The leader (the role was

played by the first author of the paper) carried a late gene-

ration smart phone in his pocket used only in

mode to provide leader's status (' ' or '

') as described in Section 6.

The experiment was performed in 3 main phases and

was fully unsupervised (without using GPS or any exter-

nal links; the wireless connection only was used to down-

load data; the only manual intervention was to start and

stop the robot). In phase 1, the robot started with the Basic

Follower behaviour (layer 1, Fig. 1). In phase 2, in order to

test the ability of the robot/UGV to autonomously disting-

uish obstacles from the cases when the leader stops, he

quickly disappeared behind an obstacle (Fig. 11a). The

robot detected the obstacle after the two conditions were

checked and satisfied (Section 5) and avoided the obstacle

successfully (Fig. 11b). In phase 3, both Rejoining the

leader behaviours (Section 8) were tested successfully

(Fig. 11c).

Nokia N97

wi-fi moving statio-

nary

Fig. 11b. Robot avoided the obstacle.

Fig. 11c. Robot searched for the leader and rejoins him.

Fig. 11a. Leader quickly disappears behind the obstacle.

The video clips from the experiments are available at:

http://www.lancs.ac.uk/staff/angelov/Researc h.htm

When the Basic Follower behaviour is realised a 90°

fan area is being scanned using the laser with 1° resolu-

tions [30]. It returns the distance, and the bearing/angle,

to the nearest object (Table 2).

The aim was to control the distance, as close as pos-

sible to (in this experiment = 500mm). The samp-

ling frequency was 10Hz (100ms per sample). The con-

trol values were generated at each sampling interval. Al-

though, during the experiment the target/leader performed

a series of behaviours including acceleration, decelera-

9.1. Basic Follower

d

Fig. 12.b. Angle measured in real-time (the interval when

the robot stopped is clearly marked; the high variance of

the readings when the robot is moving is due to the noise

and imperfection of the laser scanner and the controller).

d

d d

�

Table 2. Example of the data collected in realtime with the

control inputs and outputs.

Fig. 12.a. Distance measured in real-time (the interval

when the robot stopped is clearly marked; the high

variance when the robot is moving is due to the noise and

imperfection of the laser scanner and the controller).

ref ref

VOLUME 5, N° 1 2011

Time

(ms)

0

200

400

600

800

1000

d

(mm)

588.07

563.28

570.84

583.25

566.37

575.64

�

�

�

�

( )

9.13

8.30

0.467

8.17

6.27

18.81

Velocity

Left (mm/s)

203.54

134.05

143.09

191.01

131.90

207.7

Velocity

Right (mm/s)

148.75

183.87

140.29

141.99

151.57

94.86

Articles 43

tion, turning, reversing, the UGV, following the leader

smoothly and even moving back when the leader stopped

(the distance, d became smaller than the distance, ).

The velocity was measured by the tachometer (odo-

meter) of the robot [28]. An example of the measured dis-

tance and angle to the target is illustrated in Fig. 12.

Traditional (P) controllers are known to be heavily re-

liant on tuning. Therefore, when a P controller was used it

required some tests to establish suitable parameters (

and ). FLC was also not free form this disadvantage

MBPC is self-learning and, therefore, it did not require

extensive prior tuning. Table 2 illustrates the comparison

between the three controllers. The discrepancy between

the real observation and the target values of distance and

angle has been used to calculate the errors. The mean

absolute error (MAE) and the root mean square error

(RMSE) are used as the criteria for the comparison of the

three controllers. As it is clear from the table, predictive

controller has assisted the fuzzy controller to achieve

a better control performance in terms of distance and to

some extent in terms of tracking angle to minimise the

delay in control response. FLC also has a better perfor-

mance compare to Pcontroller in term of distance. In order

to improve the angle tracking in FLC which is worse than

P controller, more rules describing the response to diffe-

rent observation in angles can be added to the fuzzy con-

troller to achieve higher accuracy. Also, the off-line tech-

niques such as ANFIS [34] can be used in order to get the

optimal parameters of fuzzy controller.

When the leader meets an obstacle and disappears

quickly from the view of the robot, this is detected by acti-

vating the procedure described in Section 5 using a signal

sent through connection from the smart phone that

the leader carries. Combining the two conditions the ro-

bot/follower initiates an obstacle avoidance manoeuvre.

This behaviour is activated immediately after the ob-

stacle is being avoided. The challenging part is the need

the robot to identify the leader out of many objects. One al-

ternative was to use the prediction (as described in Section

8). Another approach is to super-impose the laser's scans.

The following sub-sections describe the results using each

of the two techniques.

The precision of the prediction as described in Section

8.1 is in order of 100mm for a prediction horizon of 3-5 s

(the prediction horizon is determined by the obstacle avoi-

dance module) [31]. Note, however, that this is an useful

d

k

k

Table 3. Result comparison.

wi-fi

ref

9.1.1. Controllers Comparison

9.3.1. Predicting the Leader's Trajectory

1

2

9.2. ObstacleAvoidance

9.3. Rejoining the Leader

range of errors, because the aim is to rejoin the leader after

an obstacle avoidance and to approximately identify the

location of the leader in such a way that there is no other

obstacle in the vicinity of dozen cm which is reasonable,

although not perfect. A higher precision may be prefer-

able, but the task is very complex since the motion of the

leader, especially, when avoiding an obstacle himself is

very difficult to predict for a long horizon. Note, also, that

the leader is not a dot in the space, but a physical object

with width of about 500 mm.

After avoiding the obstacle, the scans are taken by the

laser every 200 ms with 180° fan view. The detectable ran-

ge of the laser is [150; 10,000] mm. However, while the

robot is moving, these scans had to be adjusted in respect

to an absolute coordinate system (e.g. linked to the initial

robot's position), see Fig. 2. This is done by super-positio-

ning scans stored in a buffer and ordered by tuples regar-

ding the time of collection taken at consecutive time ins-

tants (Fig. 9). Once several (10-15 in this experiment)

scans were collected, the robot could detect the position of

the leader (red point) out of several static objects (green

point) and rejoin the leader and to continue with the Basic

Follower procedure, Fig. 1.

In this paper we demonstrated a novel approach for

leader follower behaviour of UGV in uncertain environ-

ment. The hardware specification of the provided plat-

form was a selected Pioneer 3-DX robot with laser, sonar

sensors and a compass on board. As an external sensor,

a smart phone with accelerometer and a compass on board

was also provided.

The software components of the proposed approach

were divided in three main layers: following, detecting

and avoiding obstacles, and rejoining the leader. Three

controllers (the P-controller, FLC, and eMBPC) were ap-

plied separately in the Basic Follower layer. In order to de-

tect obstacles and differentiate those from the leader, two

conditions were checked; i) sudden large drop in the dis-

tance and ii) the current status of the leader provided by

the smart phone Nokia N97. When the obstacle is detec-

ted, two sensors (laser and sonar) were used for the obsta-

cle avoidance procedure. Finally, in order to rejoin the lea-

der after the robot successfully avoided the obstacle, two

alternative techniques were proposed; i) predicting the

leader's trajectory, and ii) super-position of the scans taken

by the laser. The overall behaviour was tested successfully

indoors and outdoors at Lancaster University.

The leader follower behaviour presented in this paper

assumed that the robot follows the leader behind. How-

ever, in order to develop a robot with social skills and

intelligence, it needs to engage the leader and accompany

him/her as a human would. We intend to address the issue

in our future work by developing behaviours which allow

the robot to travel side-by-side with the leader in a socially

acceptable manner.Another task is to improve the control-

9.3.2. Superposition of the Laser's Scan

10. Conclusion

10.1. Summary

10.2. Future Work


P Controller

FLC

MBPC

8.5

4.93

4.45

RMSE

0.53

0.60

0.54

RMSE

d (mm) � �( )

150.6

112.3

100.6

MAE

10.00

11.21

8.58

MAE

Articles44


ler by developing a self-adaptive fuzzy controller which

works online without the compulsory need of an offline

pre-training and does not need a prior knowledge about

the differential equations governing the system. In addi-

tion, to improve the robot's performance in identifying the

leader out of many moving objects (after avoiding the ob-

stacle), super-position of the scans collected by the laser

can be combined with the direction of the movement of the

leader measured by the magnetometer of the smart phone

carried by him/her. This issue should be addressed in an-

other publication

Intelligent Systems Laboratory, School of

Computing and Communications, Infolab 21 Lancaster

University, LA1 4WA, United Kingdom. E-mails:

{p.sadeghi-tehran; j.andreu; p.angelov}@lancaster.ac.uk.

EntelSenSys Ltd., Lancaster, UK.

E-mail: [email protected]

.

-

-

.


ACKNOWLEDGMENTS

Pouria Sadeghi-Tehran*, Javier Andreu, Plamen

Angelov

Xiaowei Zhou

The authors would like to acknowledge the funding support by

the UK Ministry of Defence (MoD), Centre for Defence Enter-

prise - Call 'Reducing the Burden °n the Dismounted Soldier

Capability Vision, Task 4, stage 1 - Assisted Carriage', project

RT/COM/7/047.

[1]

AUTHORS

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Articles46

Abstract:

1. Introduction

Approximately 50 to 60 percent of the more than five

million stroke survivors are moderately or minimally

impaired, and may greatly benefit from rehabilitation.

There is a strong need for cost-effective, long-term

rehabilitation solutions, which require the therapists to

provide repetitive movements to the affected limb. This is a

suitable task for specialized robotic devices; however,

with the few commercially available robots, the therapists

are required to spend a considerable amount of time

programming the robot, monitoring the patients,

analyzing the data from the robot, and assessing the

progress of the patients. This paper focuses on the design,

development, and clinically testing an expert system-

based post-stroke robotic rehabilitation system for

hemiparetic arm. The results suggest that it is not neces-

sary for a therapist to continuously monitor a stroke pa-

tient during robotic training. Given the proper intelligent

tools for a rehabilitation robot, cost-effective long-term

therapy can be delivered with minimal supervision.

Keywords: rehabilitation robots, intelligent systems,

human-robot interaction, expert systems, stroke therapy.

The goal of this work is to investigate a novel metho-

dology that would enable physical and occupational thera-

py clinicians to provide long-term robotic rehabilitation of

the hemiparetic arm for stroke survivors with minimal

supervision. Neither the use of robotics, nor the use of

artificial intelligence (AI) techniques is new to the field of

medicine. However, the idea of applying robotics to the

field of rehabilitation medicine is relatively new. Robotic

rehabilitation has yet to attain popularity among the main-

stream clinicians. With the increased use of robots in reha-

bilitation medicine, there are numerous opportunities

where AI assisted technologies could play a vital role in

assisting the clinicians.

This paper focuses on designing, developing, and cli-

nically evaluating an expert system-based post-stroke

robotic rehabilitation system for hemiparetic arm [31].

The system serves as a valuable tool for therapists in

analyzing the data collected from the robot when it is used

by the patient, helping the therapists to make the right

decisions regarding the progress of the stroke patient, and

suggesting a future training plan. The system is designed,

developed, and evaluated by conducting a clinical study.

The effectiveness and the usefulness of such a rehabili-

tation system are analyzed in this paper. If it can be shown

that the proposed expert system-based robotic rehabilita-

tion is effective and useful for the therapists and the

patients, this work could pave the way for an affordable,

easy to use long-term robotic rehabilitation solution for

stroke survivors. Moreover, such a system that requires

minimal intervention from the therapist will play a role in

making remote stroke therapy a reality.

Stroke is extremely prevalent and its effect is long-

lasting; yet the availability of long-term rehabilitation is

limited. Every 45 seconds, someone in the United States

has a stroke. Stroke is a leading cause of serious, long-

term disability in the United States. From the early 1970s

to early 1990s, the estimated number of non-institutio-

nalized stroke survivors increased from 1.5 to 2.4 million,

and an estimated 5.6 million stroke survivors were alive in

2004 [2]. Approximately 50 to 60 percent of stroke survi-

vors are moderately or minimally impaired, and may

greatly benefit from rehabilitation [11], [23].

Loss of voluntary arm function is common after

a stroke, and it is perceived as a major problem by the

majority of chronic stroke patients, as it greatly affects

their independence [4]. In recent years, clinical studies

have provided evidence that chronic stroke patients have

motor recovery even after 4 to 10 years from the onset of

stroke [39], [29]. Given this fact, there has been a strong

demand from patients and caregivers to develop effective,

long-term treatment methods to improve sensorimotor

function of hemiparetic arm and hand for stroke survivors.

Even partial recovery of arm and hand sensorimotor

function could improve the patients' quality of life, and

reduce the socioeconomic impact of this disease-induced

disability.

The major challenges involved in post-stroke rehabi-

litation are the repetitiveness of the therapy, and the avail-

ability of therapists for long-term treatment. Many rehabi-

litation techniques involve repetitive mechanical move-

ment of the affected arm by a therapist. The utilization of

robots for rehabilitation has assisted the therapists with

the repetitive tasks of the therapy. However, the therapists

are still required to spend a considerable amount of time in

programming the robot, monitoring the patients, analy-

zing the data from the robot and assessing the progress of

the patients. Even the few commercially available robots

neither include any tools for analyzing the data, nor do

they have any decision making capabilities. The commer-

cial rehabilitation robots do not take full advantage of the

available computing power. Hence, this paper focuses on

designing an expert system-based robot that contains tools

1.1. Motivation

1.2. Problem Statement

ROBOTIC REHABILITATION OF STROKE PATIENTS

USING AN EXPERT SYSTEM

Pradeep Natarajan, Arvin Agah, Wen Liu

Received 2 ; accepted 7 .nd June 2010 th September 2010


Articles 47

for post-stroke rehabilitation of the upper limb that are

easy to use by the therapists.

In order to overcome the repetitiveness of the rehabi-

litation therapy, the interactive robotic therapist was deve-

loped [16], [17]. Since then, even though a number of re-

habilitation robotic systems have been developed, they all

have one thing in common they lack intelligence, and

hence are not easy to use by the therapists. Although most

of the systems have the capability to collect different kinds

of data during patient training, they still require a therapist

(or someone assisting the therapist) to analyze the collec-

ted data in order to make the decision regarding the chan-

ges in the training program. This makes the system diffi-

cult to use and it takes the therapist's time away from the

patient.

The hypothesis of this work: Stroke patients rehabi-

litated using an expert system-based robotic system will

experience the same improvement as the stroke patients

using the same robot without the expert system. The

neuro-motor function of the hemiparetic upper limb will

be assessed primarily using Fugl-Meyer and Motor Status

scores [1] , [40].

Integrating an expert system with the rehabilitation

robot allows a new training program to be selected by the

robot, based on the data collected during the patient's

training. This essentially serves as a feedback loop. The

primary aim of this research work is to design and imple-

ment an expert system-based robotic rehabilitation sys-

tem, and evaluate it in a clinical setting. The expert sys-

tem-based rehabilitation robot will be easy to use by the

medical personnel (not requiring any type of program-

ming expertise) and therefore will reduce the therapist's

time. Since the expert system is developed using the

knowledge acquired from multiple therapists, the deci-

sions made by the expert system are no longer the opinion

of one therapist which is the case in conventional therapy.

The primary research question addressed by this work is

whether or not an expert system-based robotic rehabili-

tation system, in which the robot will be able to autono-

mously analyze data, and make suggestions for the future

training exercises depending on the progress of the pa-

tient, can provide the same or better result than the robotic

rehabilitation system without an expert system while

making the system easier to use for the therapists.

This paper is organized into eight sections. The next

section provides an introduction to stroke and rehabi-

litation of the upper limb. Section 3 presents an overview

of the current state of robotics in upper limb rehabilitation

for stroke. Section 4 gives an introduction to expert sys-

tems and the steps followed to develop a successful expert

system. Section 5 describes how the entire system was

developed and the rationale behind the design of various

system components. Sections 6, 7 and 8 detail the clinical

study, the results and the conclusions.

1.3. Approach

2. Stroke rehabilitationA stroke (also known as Cerebral Vascular Accident),

occurs when blood flow to any part of the brain stops.

When blood supply to a part of the brain is interrupted, it

results in depletion of the necessary oxygen and glucose to

that area. The functioning of the brain cells (or neurons)

that no longer receive oxygen will be immediately stop-

ped or reduced and the oxygen starved neurons will start to

die [34], [8]. Brain cells that have died cannot be revived,

and the body parts that were receiving signals from those

cells for various functions like walking, talking, and thin-

king may no longer do so. Stroke can cause paralysis,

hemiparesis (paralysis of one side of the body), affect

speech and vision, and cause other problems [2].

Stroke rehabilitation is the process by which the survi-

vors undergo treatment in order to return to a normal,

independent life as much as possible. It is known that most

of the motor recovery takes place in the first three to six

months after stroke. However, depending on the therapy,

minor but measurable improvement in voluntary hand/

arm movement occurs even long after the onset of stroke

[5]. Some clinical studies have shown that the brain

retains the capacity to recover and relearn the motor con-

trol even after four years from the stroke onset [39], [29].

Therapy to reestablish the stroke patients' functional

movement is a learning process based on the normal adap-

tive motor programming [3]. The motor relearning of the

stroke patients is based on the brain's capacity to reorga-

nize and adapt with the remaining neurons. It has been

reported that rehabilitation and intensive repetitive train-

ing can influence the pattern of reorganization [20], [35],

[28]. Even though many different treatment approaches

have been proposed, e.g., [37], physical therapy practice

heavily relies on each therapist's training and clinical

experience [33].

Studies of robot-aided motor training for stroke pa-

tients have demonstrated that it is not only more produc-

tive for patient treatment, but it is also more effective in

terms of functional improvement of the hemiparetic upper

limb compared to conventional physical therapy [6], [24].

The robot-aided motor training could have great potential

to evolve into a very effective and efficient clinical

treatment.

3. Robotics in Upper Limb RehabilitationOne of the earliest robots developed for manipulation

of the human arm was the interactive robotic therapist [16],

[17]. The interactive robotic therapist allows for simulta-

neous diagnosis and training by therapists through inter-

actions with patients. This system is also used for the quan-

tification of the patients' recovery and progress. Following

the successful results of this robotic therapist, several reha-

bilitation robots were designed, including the Mirror-

Image Motion Enabler (MIME) [7], Assisted Rehabilita-

tion and Measurement Guide (ARM Guide) [36], Moto-

rized Upper-Limb Orthotic System (MULOS) [21], and

GENTLE/s haptic system [28]. Researchers agree that in

general, compared with conventional treatment, robot-

assisted treatment definitely has therapeutic benefits [7] ,

[30]. Robot-assisted treatment has been demonstrated to

improve strength and motor function in stroke patients. In

one clinical trial even follow up evaluations for up to three

years revealed sustained improvements in elbow and

shoulder movements for those who were administered

robotic therapy [1], [40].

The InMotion2 robot is a commercially available reha-

bilitation robot [19]. The InMotion2 robot can be program-


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VOLUME 5, N° 1 2011

system is a system based on rules.Arule is a unit of know-

ledge represented in the following form [15], [14]: IF

<conditions> THEN <actions>. The expert knowledge

represented as a production system is used to make the

decisions regarding the selection and/or modification of

any training exercise. The expert system is used to moni-

tor the progress of patient's motor learning and modify the

training exercises. From the accumulated records of the

patient's arm movement data, the progress of the patient

can be determined.

The expert system

was developed as high-level intelligence for the InMo-

tion2 robot controller programs. This high-level intelli-

gence monitors the progress of the patient and issues

appropriate guidance to the robot for its low-level motion

control. One of the most commonly used open-source

tools for developing expert systems is C Language Inte-

grated Production System (CLIPS) [14]. Programs for

analyzing the raw data collected during the patient train-

ing are developed in C. Tcl/Tk is used for the user interface

of the robot.

A training exercise is defined as a sequence of tasks or

training goals. The rule-based expert system analyzes the

symbolic information provided by the system, such as the

initial subject conditions, the various movement parame-

ters, the current movement data, and evaluates the sub-

ject's progress. As a result of the assessment, the expert

system can modify the training exercise. The modification

could include selecting a new exercise from the given set

of exercises, and/or determining how the determinants of

the exercise should be adjusted. In many cases the same

exercise could be selected with different determinants

(such as the range of motion, velocity of motion, and assis-

tive or resistive forces). Figure 1 shows the architecture of

the expert system while it is in use.

The expert system is

tested at the Neuromuscular Research Laboratory of the

Department of Physical Therapy and Rehabilitation

Sciences at the University of Kansas Medical Center

(KUMC). Next, the expert system was demonstrated to

some therapists at KUMC in order to be validated. The

experts presented different cases and made sure the expert

system satisfies the requirements

This section addresses the design, development, and

clinical evaluation of an expert system-based robotic re-

habilitation system using the InMotion2 robot. The first

step in developing this system is to understand the current

stroke rehabilitation practices. Based on interviews with

therapists, literature review, and a survey among clini-

cians in Kansas and Missouri, a list of patient conditions

and corresponding treatment protocols were formulated.

These protocols were fine-tuned to suit robotic therapy

with the help of therapists. This became the knowledge

base (also known as rule base) for the expert system.After

the knowledge base was finalized, the protocol for the

clinical study was developed. Based on the requirements

of the clinical study, the software components of the

system were developed and implemented. The system was

tested and the clinical study was conducted upon approval

from the Institutional Review Board at KUMC.

Tool Selection and Development:

Verification and Validation:

.

5. Research methodology

med to interact with a patient to shape his/her motor skills

by guiding the patient's limb through a series of desired

exercises with a robotic arm. The patient's limb is brought

through a full range of motion along a single horizontal

plane to rehabilitate multiple muscle groups [16], [17],

[24]. The InMotion2 robot is available in the Neuromus-

cular Research Laboratory (NRL) at the University of

Kansas Medical Center (KUMC) and was used

An expert system is an interactive computer-based

decision tool that uses both facts and heuristics to solve

difficult decision problems based on the knowledge

acquired from human experts. Any application that needs

heuristic reasoning based on facts is a good candidate for

expert systems. Some of the earliest expert systems were

developed to assist in areas such as chemical identifi-

cation (DENDRAL), speech recognition (HEARSAY I

and II), and diagnosis and treatment of blood infections

(MYCIN) [18]. The expert system development process

consists of four phases - knowledge acquisition, know-

ledge representation, tool selection and development, and

verification and validation [18], [26]. Each phase of the

expert system development process is discussed in this

section.

Knowledge acquisition

refers to any technique by which computer systems can

gain the knowledge they need to perform some tasks [38].

Knowledge often goes through stages of refinement in

which it becomes increasingly formal and precise. Part of

this process involves identifying the conditions under

which the knowledge is applicable, and any exceptional

conditions. In order to design the knowledge base, several

discussions with physical and occupational therapists

were conducted. In these meetings, the capabilities of the

rehabilitation robot were demonstrated to the therapists.

Based on the discussions, it was clearly understood that

the expert knowledge in the field of physical therapy was

very complex, based on practical experience, very subjec-

tive to the patient and the type of motor impairment.

A pilot survey to better understand the current clinical

practices in stroke rehabilitation was conducted among

physical and occupational therapists in Kansas and

Missouri [33].

The knowledge-based

expert system should encapsulate the expertise of the

therapists, in order to be an effective tool during rehabi-

litation therapy. The captured information takes into

account factors that are relevant to stroke rehabilitation

such as:

The general principles of therapy.

The initial conditions of the stroke patient.

The most effective training exercises, along with the

determinants for each exercise.

The methodology by which therapists assess the

patient's progress.

The knowledge gathered from the experts is first refi-

ned in a manner such that it is applicable to the InMotion2

robot which is used for stroke rehabilitation of the arm.

Next, the refined knowledge is represented using a stan-

dard format such as a production system. A production

.

4. Expert systems

Knowledge Acquisition:

Knowledge Representation:

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VOLUME 5, N° 1 2011

Fig. 1. Expert System while in use.

5.1. Design Requirements

In the conventional rehabilitation, the therapist per-

forms an initial assessment of the stroke patient's sensori-

motor skills. Many standard tests such as Fugl-Meyer, and

Motor Status Assessment are widely accepted as quanti-

tative tests. Based on the initial assessment, the therapist

chooses one or more exercises for the patient and starts the

rehabilitation process. This cycle of assessing the patient

and administering therapeutic exercises is repeated as

long as it is feasible. Several studies have shown that

robotic post-stroke therapy can be effective but there is no

published literature that outlines a comprehensive and

generic treatment procedure. In most of the reported

studies, a therapist makes an initial assessment of the

patient, then chooses one or more exercises with suitable

determinants (the variable parameters for each exercise),

and then begins the robotic rehabilitation. When the

patient undergoes therapy with the robot, the therapist

visually observes the various motor skills of the patient

and assesses the progress of the patient. In some cases the

therapist manually analyzes the data collected from the

training programs and makes a decision regarding the

patient's progress. Depending on the therapist's asses-

sment, once again one or more training exercises with

suitable parameters are chosen for the patient and this

process is repeated.

In the expert system-based rehabilitation system, ins-

tead of the therapist continuously monitoring the patient

and providing the robot with the training exercise and the

parameters, the expert system makes the necessary deci-

sions. The system undertakes the usually time-consuming

task of analyzing voluminous training data in order to

evaluate the patient's progress without the intervention of

the therapist. The expert system then presents the future

training exercise and the parameters along with the expla-

nation for the decisions. The therapist reviews the deci-

sions and the explanation. Once the therapist approves,

the robotic training is repeated. This allows the therapist to

supervise the entire process for multiple patients within

a short amount of time. For the therapist, it is not necessary

to monitor each patient continuously.

The determinants or the variable parameters of each

robotic training exercise include the single plane move-

ment patterns, the number of repetitions or desired time

duration, velocity of the training motion, assistive forces,

resistive forces, and range of motion. These parameters

need to be selected and modified by the expert system

after taking into consideration the various patient condi-

tions. The entire decision tree for the expert system is pre-

sented as the treatment protocols.

Understanding the current practices is imperative for

the development of an expert system-based robotic stroke

rehabilitation system. Given the broad range of therapy

approaches, it is important to obtain data on what stroke

rehabilitation methods are actually being used by clini-

cians. The pilot survey was aimed at understanding the

current stroke rehabilitation practices of physical and

occupational therapists who were providing care in two

Midwest states: Kansas and Missouri. More than 100 cli-

nicians participated in the survey. The knowledge collec-

ted from clinical experts enabled the development of the

treatment protocols which serve as the rehabilitation

system's knowledge base.

The stroke rehabilitation methods adopted by thera-

pists vary widely and they seem to combine principles

from different approaches in their current practice. This

may be an indication of a need for an optimal approach to

be developed through more research. The majority res-

ponses from the clinicians were used to construct the

knowledge base of the expert system for robotic reha-

bilitation. The self-reported background information of

the clinicians correlates with the dated treatment choices

reported in sections of the questionnaire. The uncertainty

among clinicians revealed in some sections of the survey

shows that more evidence of clinical approaches is needed

to ensure efficacious treatments [33].

The knowledge gathered is implemented as rules for

the expert system. These rules are used by the expert sys-

tem to determine the appropriate robotic training for the

patients during the clinical study.Astep-by-step treatment

protocol has been developed in conjunction with the

knowledge gathered from the experts and the current

literature. This protocol is given in a diagrammatic format

in this section.

When a stroke patient begins the robotic therapy, the

therapist makes an initial assessment which includes all

the base-line evaluations. During the initial assessment

three main conditions of the patient are determined - tone,

strength, and Passive Range of Motion (PROM). For each

patient, tone can be normal or high, strength can be dimi-

nished, and PROM can be limited or normal. Figure 2

shows the treatment plan that has to be followed if the

patient's tone is normal and the PROM is limited.

Similar to treatment plan 1, two more treatment proto-

cols were developed one for treating diminished strength,

and the other for high tone and limited PROM. Definitions

of acronyms used in the treatment plan are:

ROM - Range of Motion of the patient

PROM - Passive ROM, the range in which the patient

is unable to actively contract the muscles to perform

the movement on his/her own.

AROM -Active ROM, the range in which the patient is

5.2. Knowledge Base Development

5.3. Knowledge Representation

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VOLUME 5, N° 1 2011

able to actively contract the muscles to perform the

movement on his/her own.

Patient's progress during the stretching treatment is

monitored primarily using the range of motion. Subse-

quent training exercise parameters are modified as follows:

Increase amplitude as tolerated to increase ROM.

Patient's progress during the strength treatment is

monitored primarily using the accuracy. Subsequent

training exercise parameters are modified according to the

following:

Accuracy of 90% or better over a given number of

repetitions, number of trials, or time.

Progress resistance for patients functioning with

AROM.

If applicable, wean patients off assistance as tolerated.

Adjust time demand.

Modify target constraints to make the task more

difficult.

The software components in this research were

developed according to the needs of the clinical study. The

design provides the human user full control and maximum

flexibility for manual override at any point during the

clinical study. Figure 3 gives an overview of the software

architecture as well as the flow of data through the system.

The components can be grouped into three categories:

(1) The expert system developed using CLIPS.

(2) The robot testing and training programs in Tcl/TK

(3) The analysis program developed using C.

The functioning of the overall system can be explained

in a step by step manner as follows:

In the patient's first visit, initial testing with the robot is

done. During this test various arm movement para-

meters and the patient's conditions regarding tone,

strength, and PROM are recorded and saved in the

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�

5.4. Software Implementation

parameters and conditions data file, respectively. The

list of parameters is given in Table 1.

The expert system takes the data files as input and se-

lects an appropriate treatment regimen for the patient.

In addition to selecting the training exercises, the ex-

pert system also makes the necessary modifications to

the parameters data file.

The robot training program takes the parameters data

file as input and provides the appropriate exercise to

the patient.

During the training, the program records the data re-

levant to the patient's movement at 30 milliseconds

interval. The data includes the x and y position, the x

and y forces, the x and y velocities, and the time at

every data point.

The data from the training sessions is analyzed by the

analysis program. This program calculates the average

deviation, the percentage accuracy, the average velo-

city, and the average of the peak resultant velocity. The

analysis program stores the calculated values back in

the para-meters data file.

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Fig. 3. Overview of the software components.


Articles 51

VOLUME 5, N° 1 2011

Fig. 2. Treatment Plan 1 - normal tone and limited PROM.


Articles52

�

�

The parameters data file is the input to the expert sys-

tem. The expert system checks if the new parameters

are different from the old parameters in terms of

accuracy, range or motion, velocity, etc., as shown in

the treatment plan. If they are different, the conditions

for progress are checked and subsequently any applic-

able changes are made to the parameters data file.

This new parameters data file is used as input by the

training program and the training cycle repeats.

The list of parameters in file is given in Table 1, and

how the parameters are represented in the InMotion2

robotic training is explained.

Center of y-axis - The center position (origin) of y-axis

can vary from patient to patient due to reasons such as the

position of the chair in front of the robot, the length of the

patient's arm, etc.

ROM - The range of motion is represented as the

radius of a circle. This implies that the range is not

direction specific. For example, if a patient has AROM of

0.14m then it can be understood that the patient can

actively move the arm under his/her own power to any

point within the circle of radius 0.14m from the center (the

origin) position.

Resistance - In the InMotion2 robot, any force is

a function of a parameter called stiffness. This is similar to

that of the stiffness of a spring called the spring constant.

The robot uses back-drivable electric servo motors to

implement a spring-like feel. The stiffness is measured in

Newtons per meter. For a spring, it is the amount of force

required to stretch the spring by one meter, and it can be

represented as:

where is the spring constant, is the displacement of the

spring. When the robot arm is set to be stationary at a point

and if one tries to move the arm, one will be moving

against the resistance of the arm. This resistance will be

felt like the stiffness of a spring and the force experienced

will increase as the arm is moved farther away from the set

position. This method is used in the strength training

exercises.

Assistance - The assistive forces applied to a patient's

arm by the robot arm is also manipulated as a function of

the stiffness. When the patient does not need any assis-

tance from the robot, the stiffness can be set to 0, i.e., no

Table 1. Patient Parameters in Data File.

k x

F = k x�

force from the robot. As the stiffness is increased and the

robot arm is programmed to move along a specified path,

then it will exert assistive force on the patient's arm.

Higher stiffness means that the robot arm follows the

programmed path more closely and provides increased

assistance to the patient's arm.

Deviation - During training, the robot is programmed

to record the position data about every 30 milliseconds.

The data file also stores the information about the desired

straight line path in the form of starting point and ending

point. If the starting point is given as ( , ) and the ending

point is ( , ) then the equation of the straight line can be

given as:

Using this equation of the line, the perpendicular dis-

tance to the line from any given point, ( , ), can be

calculated as follows:

The calculated distance is given as the deviation from

the desired straight line path at any given instant.

Accuracy - The calculated accuracy is an extension of

the deviation. The average deviation is represented as

a fraction of the length of the motion segment. For exam-

ple, more than 96% accuracy means that the average de-

viation is less than 4% of the length of the motion segment.

Velocity - The velocity is calculated from the time ta-

ken to complete a motion segment. Although the instan-

taneous velocity is recorded every 30 ms, this velocity is

not constant. Therefore, in order to calculate the average

velocity of the patient's arm, the time taken to complete

each motion segment is noted. Based on the time and the

distance of the motion segment, the average velocity is

determined.

Resultant Velocity - The velocity recorded in the data

file from the robot controller is the instantaneous and

velocity vectors. The magnitude of the resultant is calcula-

ted using the formula,

The aim of this clinical study is to test various aspects

of the newly developed expert system-based stroke reha-

bilitation system in a clinical setting. The results of the

clinical study will serve as “proof of concept” for a possi-

ble full-scale study.

Two chronic stroke patients were recruited for this stu-

dy with the help of the Kansas Stroke Registry, established

at the University of Kansas Medical Center. Subjects in

this study were adults, greater than 21 years of age, who

are diagnosed patients with symptoms of mild to moderate

stroke. One subject assigned to the experimental group

underwent robotic training with the expert system the

other subject assigned to the control group underwent

x y

x y

Ax + By + C =

A = y y , B = x x C x y x y

x y

1 1

2 2

2 1 1 2 2 1 1 2

0 where

, and = ( . ) ( . )

p p

x y

6. Clinical study

VOLUME 5, N° 1 2011

PARAMETER

AROM

PROM

resist_force

assist_force

center_y

deviation

accuracy

velocity

max_res_vel

SHORT DESCRIPTION

Active Range of Motion (m)

Passive Range of Motion (m)

Maximum tolerable resistance (N/m)

Minimum required assistance (N/m)

Center position, origin of y-axis (± m)

Average deviation from straight line path (m)

Average % accuracy with respect to length

of motion segment

Average velocity calculated from time taken (m/s)

Average of the peak resultant velocity (m/s)

22 )()( velvelvel yxR ��


Articles 53

robotic training without the expert system. A step-by-step

overview:

Base-line (initial) testing should be done for all

subjects by a therapist. This will be used for post-training

comparisons. The therapist should also find out the patient

conditions regarding Passive Range of Motion (PROM -

limited/normal), and tone in the passive range (normal/

MAS1/ MAS1+/MAS2).

Initial testing using the robot - should be done for

all subjects. The various parameters of arm movement for

the subject are measured using the appropriate robotic

testing programs. The parameters measured are listed in

Table 1.

(Steps 3, 4, 6 and 7 are for the experimental group. For

the control group only step 5 is given; however the para-

meters for step 5 are manually calculated and verified by

the therapist.)

Use the recorded data file from the testing pro-

gram and run the analysis program on it. This program

calculates the average deviation, accuracy, average velo-

city and peak resultant velocity, and stores the results.

Run the expert system to determine the steps in

training.

Subject training - run the training programs as

suggested by the expert system (or by the therapist for

control group) after the parameters are verified by the

therapist.

The warm-up programs stretch the ROM and slowly

increase the velocity. There are five trials each with two

repetitions on the diagonal pattern. Resistance and assis-

tance is minimal.

The train_ROM and train_strength programs are used

for stretching the range and for strengthening the affected

arm respectively. There will be 10 trials each with two

repetitions.

After every two sessions (i.e., one week of

training with the same parameters) use the recorded data

file from the training sessions and run the analysis pro-

gram on it. This program calculates the average deviation,

accuracy, average velocity, and peak resultant velocity,

and stores the results.

Run the expert system to determine the progress

and the future training steps.

Repeat steps 5, 6 and 7 for four weeks (two sessions/

week).

Repeat step 1 to collect all the end-treatment data.

As explained in the study protocol two human subjects

were recruited for this study. One of the subjects was as-

signed to the experimental group and one to the control

group.

STEP 1.

STEP 2.

STEP 3.

STEP 4.

STEP 5.

STEP 6.

STEP 7.

STEP 8.

7. Experimental results

A baseline evaluation was conducted for each subject

to assess the sensorimotor function. Primary measure for

the motor function are the Motor Status Score for shoulder

and elbow (MS1) and Fugl-Meyer Assessment (FMA)

score for upper extremity. A quantitative assessment of

motor function of hemiparetic upper limb was also

conducted using the robot. Other neuromotor functional

assessment techniques which were used include: Motor

Status Score for wrist and fingers (MS2), and Modified

Ashworth Scale (MAS).

Motor Status Score [12] - MS1 consists of a sum of

scores given to 12 shoulder movements and five

elbow/forearm movements (maximum score = 40). MS1

uses a six-point ordinal (unequal intervals) grading scale

(0, 1-, 1, 1+, 2-, and 2), ranging from no volitional

movement to faultless movement. The MS1 is capable of

detecting a significant advantage of robot therapy for

shoulder and elbow [1], [40].

Fugl-MeyerAssessment [13] - FMAscale is applied to

measure the ability to move the hemiparetic arm outside

the synergistic pattern on a three-point scale (maximum

score of 66 points). The FMA scale is also widely used in

evaluating the effectiveness of robot-aided therapy [7],

[30].

Modified Ashworth Scale - MAS is a six-point rating

scale that is used to measure muscle tone.

Quantitative assessment of motor function - During

initial testing, the program displays a circular pattern with

eight different targets along the circumference and the

subjects are asked to move the robot arm from the center

of the circle to each of the targets sequentially. During this

movement the velocity of the motion, the active and

passive range of motion, the accuracy of movement, and

the required assistive/resistive forces are measured and

recorded. These values provide a quantitative assessment

of the subject's upper limb motor function.

The therapist and the subjects were unaware of the

subjects' group assignment. The therapist's opinion was

sought regarding the best robot-aided treatment option for

both subjects. The therapist opined that since the subjects

do not have PROM limitation (i.e., within the applicable

range of robot therapy), they should be trained for impro-

ving strength and accuracy.

Experimental Subject Training: For the experimental

subject, the expert system is used to determine the treat-

ment plan. Since the subject does not have PROM limi-

tation, the expert system chose strength training treat-

ment. For the robot training, the parameter values are cho-

sen from the initial testing data. However for the AROM,

if the subject's AROM is greater than 14cm, then it is

automatically capped at 14cm [10].

In the strength training exercise, the robot arm is posi-

tioned at the center of a square and the targets are placed at

the four corners. A screenshot of this program is shown in

Figure 4, and a subject using the program is shown in

Figure 5. The subject is asked to reach the targets moving

along the diagonal of the square. The robot arm resists any

movement away from the center position. The maximum

tolerable resistance measured during the testing session is

7.1. Baseline Evaluations

7.2. Subject Training

VOLUME 5, N° 1 2011


Articles54

used by the training program. The training session con-

sists of 10 trials in which the subject is required to reach

the targets twice, i.e., two repetitions in each trial. The

training program also keeps track of the number of targets

missed by the subject. Each target has a maximum time-

out period of about two minutes after which the target

expires and moves to the next position.

The analysis program is used on the experimental sub-

ject's training data to measure the average deviation, per-

centage accuracy, and velocity of movement. To allow for

day to day variations in the motor functions, analysis is

done after two training sessions instead of every session.

Following the analysis, the expert system is used to deter-

mine the progress made by the subject. According to the

expert system, perceivable progress is made only if these

conditions are all satisfied:

At least 95% of the targets are reached,

Measured accuracy is better than 90%, and

Velocity has improved compared to the previous

result.

For the experimental subject, only once during the

four week training did the expert system detect progress

and subsequently increased the resistance value for future

training. This change in resistance was approved by the

therapist.

As mentioned earlier, the

therapist determined that strength training would be ap-

propriate for the control subject as well. The same strength

training program is used under the supervision of the the-

rapist. The main difference in the treatment for the control

subject is that the performance of the subject during train-

ing was visually monitored and manually noted if any tar-

gets were missed by the subject. Based on this observation

therapist determined whether the subject has made

enough progress to warrant any increase in resistance.

The end-treatment evaluation was conducted within

five days after the completion of the training. The same

tests that were used during the baseline evaluation were

used again to assess the neuromotor functions of the sub-

jects. The results of the end-treatment evaluations of the

two subjects are given in Table 2. In addition, the table also

shows the change in scores compared to the baseline

values.

�

�

�

Fig. 4. Screenshot of the strength training program.

Control Subject Training:

7.3. End-Treatment Evaluations

Fig. 5. A subject using the strength training program.

Table 2. Comparison of Subjects.

After the initial testing, the expert system was used to

determine a treatment plan for the experimental subject.

The expert system arrived at the conclusion that strength

training should be carried out because the subject had no

PROM limitation within the range of the robot. The thera-

pist agreed with this decision of the expert system and the

subject went through strength training for four weeks. Du-

ring the four weeks, the expert system was used to monitor

the progress. Only once during this period did the expert

system detect progress and increased the resistance. After

reviewing the data, the therapist agreed with that decision

as well.

For the control subject, after the initial assessment, the

therapist determined that strength training would be most

suitable.After the the decision, the control subject's initial

conditions were input to the expert system and it arrived at

the same conclusion as the therapist. After the first two

sessions of strength training, according to the therapist's

observation the subject had made progress and so decided

to increase the resistance. The therapist's decision was

checked against the expert system but it did not detect

enough progress with the subject because the velocity had

not improved. The therapist was very pleased with the

analysis program. The data collected during a training ses-

sion typically contains close to 115,000 data points (one

data entry for every 30ms). The analysis program makes it

possible to quickly analyze and summarize the data from

an entire training session.

7.4. Effectiveness of the Rehabilitation System

VOLUME 5, N° 1 2011


Articles 55

8. ConclusionsThe objective of this work is to design, develop, and

evaluate an expert system based post-stroke robotic

rehabilitation system [31]. The new rehabilitation system

can be a valuable tool for therapists in analyzing the data

from the robot, helping them make the right decisions

regarding the progress of the stroke patient, suggesting

future training exercises, and delivering robotic therapy.

To understand the current stroke rehabilitation practices,

a survey was conducted among clinicians in Kansas and

Missouri. The majority responses from the clinicians were

used to construct a treatment plan for robotic rehabili-

tation. The treatment plan was implemented as the rule

base of the expert system. The delivery of robotic rehabili-

tation required the development of certain testing pro-

grams and training programs, and a data analysis program

that can analyze the voluminous training data and sum-

marize it to the expert system. These associated compo-

nents were developed as part of a new robotic rehabilita-

tion system. The rehabilitation system was evaluated in

a clinical setting with two human subjects. The clinical

study is intended to verify the effectiveness of expert sys-

tem-based robotic rehabilitation. The effectiveness of the

expert system, the testing and training programs, and the

analysis program was evident from the fact that the thera-

pist agreed with the analysis and the decisions made by the

system.

The expert system-based rehabilitation was studied

both for its correctness and usefulness. The correctness of

the expert system was evaluated based on how close its de-

cisions are to that of the therapist. Twice the expert system

made decisions regarding the treatment plan and regar-

ding the progress of the subject in the experimental group.

Both times the therapist agreed with the decisions and was

satisfied by the explanation provided by the expert sys-

tem. For the control subject the therapist made the deci-

sions about the treatment plan and the progress. When the

expert system was used to test the therapist's decision, it

produced the same treatment plan but not the same deci-

sion about the subject's progress. The one time in which

the expert system produced a different result can be attri-

buted to the fact that the therapist made the decision about

the subject's progress based mainly on visual observation.

The therapist did not use any tools to analyze the

quantitative data. The therapist followed the procedure

that clinicians follow in everyday practice.

The training programs record data at an interval of

about 30 milliseconds. The data file produced by the train-

ing programs on average consists of about 115,000 data

points. Manual analysis of one of the data files using

a spreadsheet program could take an average computer

user anywhere from one to two hours.Atherapist using the

robot does not have the time to quantitatively analyze all

of the patient's data. The data analysis program developed

as part of this rehabilitation system can analyze the data

file and produce summaries within a few seconds. It pro-

duces information such as the average deviation of the

subject's arm from the targeted straight line path, calcula-

tes the percentage accuracy as a fraction of the length of

the path, calculates the average time taken to reach the

targets and thereby the velocity, the average x and y direc-

tional forces, and the mean peak resultant velocity.

Having this information immediately after a training

session would enable the therapist to make sound deci-

sions based on quantitative data. The ability to summarize

a training session also means that the therapist does not

have to observe the patient continuously. The therapist can

simply look at the summarized results at the end of

a training session and make decisions.

The results also show that a subject trained with the

robot tends to show improvement in his/her motor func-

tions. This result is consistent with many other studies that

have shown that robot therapy improves motor function in

hemiparetic arm of stroke patients [1], [30]. Figure 6

shows the move-ment of the affected arm of the experi-

mental subject before and after robot-aided strength

training. It can also be seen that the accuracy has improved

marginally. The mean deviation before the therapy was

VOLUME 5, N° 1 2011

Fig. 6. Movement of the experimental subject's arm along the x-y plane, before and after treatment. The graph on the left

shows the data from the initial testing and the graph on the right shows the data from the end-treatment testing.


Articles56

0.0139m and after the therapy it was 0.0134m.

The results of this clinical study presented in Table 2

also show that the experimental subject's FMA for both

sensory and motor scores improved by two. Similarly for

the control subject, the FMAmotor score has improved by

three. The end-treatment testing with the robot also re-

vealed that the experimental subject had a marked im-

provement (32%) in the velocity of the affected arm.

There is also a slight increase in arm strength of both

subjects as measured using the robot. At the end of the

clinical study, both subjects were asked to fill out an exit

survey. Their feedback showed that the subjects were

comfortable and enjoyed using the robot. This illustrates

that robotic therapy in addition to being effective, can be

entertaining and enjoyable for stroke patients as well. The

results presented in Table 2 show that both the control

subject and the experimental subject benefited from

robotic therapy. The improvement in motor performance

was similar for both subjects. This proves that the quality

of care provided by the expert system-based rehabilitation

system is comparable to the care provided by a therapist

using existing robotic treatment methods.

The main limitation of this rehabilitation system is that

stroke therapy is highly subjective, varying from therapist

to therapist and also from patient to patient. Currently,

there is no consensus among the therapists regarding the

best treatment options. It is still possible that a therapist

might reach a conclusion different from that of the one

suggested by the expert system based on his/her beliefs

and clinical experience. Since the clinical study conduc-

ted to evaluate the rehabilitation system is limited to only

two stroke patients, it should be construed as a “proof of

concept” as the results are not statistically significant.

One area of immediate focus following this research

could be modifying the expert system to behave as a low-

level intelligent controller for the robot producing a real-

time adaptive system. Another area directly related to this

research is tele-rehabilitation [9], [22]. The results from

this research prove that minimally supervised therapy is

possible. A rehabilitation robot can be made available at

a com-munity clinic and a therapist from a remote location

can periodically review the suggestions made by the

expert system in order to approve or modify it. The deli-

very of robotic rehabilitation and its effectiveness could

be augmented by incorporating virtual reality and haptic

feedback devices [32], [27] or by a portable exoskeleton

arm [25]

IBM Corporation, Lenexa, KS

66219 USA. E-mail: [email protected].

Department of Electrical Engineering and

Computer Science, University of Kansas, Lawrence, KS

66045 USA. E-mail: [email protected].

Department of Physical Therapy and Rehabili-

tation Sciences, University of Kansas Medical Center,

Kansas City, KS 66160 USA. E-mail: [email protected]

.

-

-

-

.


AUTHORS

*

Pradeep Natarajan

Arvin Agah

Wen Liu

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VOLUME 5, N° 1 2011

Abstract:

1. Introduction

In wireless mobile sensor networks, periodic calcula-

tion of coverage is very important, since mobile sensors

can be moved adequately to current needs, thus increasing

the coverage. Those moves require the execution of

navigation tasks. Global network central algorithms for

those tasks are very costly regarding energy consumption

and computational resources. Considerations presented

herein pertain to the best algorithms for a network of

numerous small mobile sensor nodes used for monitoring

of large terrains. Localized algorithms suggested in this

paper help calculate the coverage on-line and locally with

the involvement of neighboring nodes only. Furthermore,

localized collaborative navigation is presented. It enables

yielding position estimation with no GPS use and ensures

improvement rather than deterioration over time. It uses

multi-sensor fusion algorithms based on optimization and

a distributed iterative extended Kalman Filter.

Keywords: wireless, mobile sensor network, coverage,

navigation.

There are two major problems regarding wireless mo-

bile sensor networks, i.e. whether a network has adequate

coverage of monitoring area and whether a network is able

to rearrange sensor-nodes to fulfill the specific require-

ments of coverage. The ability to self-deploy and self-

configure is of critical importance for mobile sensor net-

works because of the unattended nature of intended

applications. The network should be able to dynamically

adapt its topology to meet application-specific require-

ments of coverage and connectivity. In static networks,

topology control is achieved by controlling the transmis-

sion power or sleep/wake schedules of densely deployed

nodes. In contrast, mobile sensor networks can exploit

control over node positions to affect network topology

thus eliminating the need for over-deployment and

increasing the net area sensed. A key challenge posed by

this objective is the typically global nature of the desired

network properties, one of which is coverage of a network.

A wireless sensor network is a collection of sensors

that offer the ability to communicate with one another and

the ability to sense the environment around them, but have

limited computational and battery capacity, e.g. solar-

powered autonomous robots. These considerations con-

cern the best algorithms for a network of numerous small

mobile sensor nodes used for monitoring of large terrains,

including those used in safety applications. This paper

attempts to select the best solution to how a wireless mo-

bile sensor network could take advantage of its mobility to

improve its coverage by self-deployment of sensors con-

suming as little power as possible. Power consumption is

of critical importance in such networks. Topology control

algorithms that reduce energy consumption have been an

area of thorough research and numerous works, many of

which are presented in [1]. Our paper describes selected

techniques for evaluation of both coverage and locali-

zation of nodes, focusing on those presenting a distributed

method and requiring as low energy consumption and as

little computation resources as possible. The remaining

sections are organized as follows: Section 2 provides the

theoretical framework of coverage, Section 3 describes

evaluation of sensor field exposure, Section 4 contains

a short review of existing works concerning algorithms

for coverage calculation, Section 5 presents the distribu-

ted algorithm for minimal exposure path evaluation,

Section 6 pertains to navigation problems in mobile sen-

sor network, Section 7 presents distribution multi-sensor

fusion algorithm for navigation and Section 8 presents

performance analysis of location estimates. The last sec-

tion provides some conclusions and gives suggestions for

future work.

-

sensing ability diminishes as distance increases;

sensing ability improves as the sensing time

(exposure) increases (due to noise effects).

2. Calculation of coverageOne of the fundamental problems regarding sensor

networks is the calculation of coverage. Exposure is direc

tly related to coverage in that it is a measure of how well an

object, moving on an arbitrary path, can be observed by

the sensor network over a period of time. The minimal

exposure path is a path between two given points such that

the total exposure acquired from the sensors by traversing

the path is minimized [2]. The path provides information

about the worst-case exposure-based coverage in the

sensor network. Exposure can be defined as an integral of

a sensing function that generally depends on the distance

between sensors on a path from a starting point and

a destination point . The specific sensing function

parameters depend on the nature of the sensor device and

usually have the form , with K typically ranging from

1 to 4.

Generally speaking, sensors have broadly diversified

theoretical and physical characteristics. Most sensor

models share two facets:

With this in mind, for a sensor , the general sensing

model at an arbitrary point can be expressed as

follows:

pS

pD

d

s

S p

�K

�

�

CONSIDERATIONS ON COVERAGE AND NAVIGATION

IN WIRELESS MOBILE SENSOR NETWORK

Tadeusz Goszczynski, Zbigniew Pilat

Received 8 ; accepted 14 .th August 2010 December 2010th


Articles58

(1)

where is the Euclidean distance between the sensor

and the point , and positive constants and are sensor

technology-dependent parameters.

d(s,p)

s p K�

3. Sensor Field Intensity and Exposure

4. Algorithms for coverage evaluation

In order to introduce the notion of exposure in sensor

fields, the Sensor Field Intensity for a given point in the

sensor field should be defined. Sensor field intensity can

be defined in several ways. Two models are presented for

the sensor field intensity: All-Sensor Field Intensity and

Closest-Sensor Field Intensity.

Exposure for an object in the sensor field during the

interval [ ] along the path is defined [3] as:

(2)

where:

All-Sensor Field Intensity for a point in the field is

defined as the effective sensing measurements at point

from all sensors in . Assuming there are n active sensors,

s , s ,…s , each contributing with the distance-dependent

sensing the function can be expressed as:

( ) = ( ) (3)

Centralized method algorithm requires sensor nodes

not only to perform the exposure calculation and shortest-

path searching in the sensor network, but also to know the

topography of the network. Both functionalities, parti-

cularly discovering the network topography, may require

extensive computation resources and energy consumption.

Communication, required to discover the network topo-

graphy, is the major energy consumer in wireless sensor

networks. Therefore, it is important that a localized mini-

mal exposure path algorithm is developed so that sensors

can estimate the network's minimal exposure path without

having to know the entire network's topography. In such

a localized algorithm, the number of messages sent across

the network and the computation performed at each node

should be kept at the minimum level.

Most research conducted so far has focused on redu-

cing the design and maintenance (including deployment)

costs or increasing the sensor network's reliability and

extending its lifetime. However, another crucial problem

is to determine how successfully the sensor network mo-

nitors the designated area. This is one of the most impor-

tant criteria for evaluating the sensor network's effective-

ness [4]. Another problem is the navigation of mobile

nodes in order to improve coverage of the network. We re-

view several techniques of coverage estimation and a few

techniques for localization and navigation of mobile sen-

sor-nodes in order to move it to improve the coverage of

the sensor network. Coverage in a mobile sensor network

can be evaluated as optimization problem for best case or

worst-case coverage. The best-case coverage involves

p

F

O

t ,t p(t)

p F

p

F

I F, p S s , p

1 2 n

A i

1 2

two approaches: maximum exposure path and maximum

support path. The worst-case coverage has another two:

maximum breach path and minimum exposure path [5].

For safety applications, the latter two approaches are

chosen. Maximum breach path is not a unique one, since it

finds a path such that the exposure at any given time does

not exceed a given value. Therefore, the minimum expo-

sure path is chosen, since it attempts to minimize the expo-

sure acquired throughout entire measured time interval.

Determining such a path enables the user to change the

current location of some nodes to increase the coverage.

The authors of [6] suggest that the problem should be

transformed to a discrete by generating x square grid

and limit the existence of the minimal exposure path only

along the edges and diagonals of each grid square. Each

edge is assigned a weight calculated by special function

using numerical integration. The solution produced by the

algorithm approaches optimum at the cost of run-time and

storage requirements. [7] discusses an algorithm for a net-

work that initially deploys a fixed number of static and

mobile nodes and then static nodes find the coverage holes

and mobile nodes are relocated to the targeted localiza-

tions to increase coverage. In the next section, we present

the distributed local algorithm, which do not present limi-

tation of fixed number of nodes. The authors of [8] deve-

loped distributed algorithms for self-organizing sensor

networks that respond to directing a target through a re-

gion and discussed self-organizing sensor networks cap-

able of reacting to their environment and adapting to

changes. It can also evaluate coverage of the network in

a different way. They described an innovative application:

using the sensor network to guide the movement of a user

equipped with a sensor that can communicate with the net-

work across the area of the network along a safest path.

Safety is measured as the distance to the sensors that de-

tect danger. The protocols for solving this problem imple-

ment a distributed repository of information that can be

stored and retrieved efficiently when needed.

In [9], the author proposes a virtual force algorithm

(VFA) as a sensor deployment strategy to enhance the

coverage after initial random placement of sensors. For

a given number of sensors, the VFA algorithm attempts to

maximize the sensor field coverage. A discreet combina-

tion of attractive and repulsive forces is used to determine

virtual motion paths and the rate of movement for the

randomly placed sensors. Once the effective sensor posi-

tions are identified, a one-time movement with energy

consideration incorporated is carried out and the sensors

are redeployed to these positions.

The best solution for the objective set forth in these

considerations is the localized algorithm for minimum

exposure calculation proposed by Veltri and others [10].

It ensures low costs of communication and computation in

the wireless sensor network without the necessity to

determine the entire network's topography and does not

require a fixed and limited number of nodes. In this

algorithm, only neighboring nodes need to be updated

with information and minimum path can be calculated on-

line in an easy and efficient manner.

n n

5. Localized algorithm for minimumexposure calculation


n

1

Articles 59

factors and used the following formula to calculate the

heuristic value for node with respect to the sender node :

(4)

where:

- distance between the sender and its neighbor ,

- maximum communication radius,

- number of hops that the message has currently

been transmitted (assume starts with 1)

- positive constant

To balance the above two unrelated values, both are

normalized, where the first term rewards nodes that are far

away from the sender and the second term penalizes

neighbor if it is further to the destination than the sender

node , (the case in which the second term is negative). The

constant reflects how rapidly the weight is shifted from

picking a neighbor remote from a sensor to picking

a neighbor close to the destination. The method tends to

pick a sensor closer to the destination as , the number of

hops, increases to prevent the message from being circu-

lated endlessly throughout the sensor network.

The algorithm for localized approximation of mini-

mum exposure path consists of 4 steps:

the sensor that is closest to the starting coordinate

sends a Search message to the node that is determined

on the basis of the above heuristic value;

when this Search message reaches its destination

sensor (the sensor closest to the ending coordinate),

the sensor calculates the minimum exposure path

using a Voronoi-based approximation algorithm and

the network's topological information it receives. (The

Voronoi-based approximation algorithm gives the

near-optimum exposure path within a Voronoi cell

without using the grid-based method requiring exten-

sive computational resources and hence reduces the

computation requirements);

next, the algorithm selects the sensor in the location

that most probably contains the minimum exposure

path and sends a Forward message to this sensor.

When the appropriate sensor receives the Forward

message, it sends a Search message back to the desti-

nation sensor to acquire more information on the sen-

sor network's topography that is needed by the Voro-

noi-based approximation algorithm;

this process is repeated until no sensor node requires

any further topological information or no locations

look promising in comparison to the current minimum

exposure path calculated.

The minimum exposure path approach is very useful

in the network's evaluation. Once the minimum exposure

path is known, the user can manipulate sensors in the net-

work or add sensors to the network to increase coverage.

The problem arising after the calculation of the net-

work's coverage is how to navigate nodes effectively in

order to move some of them and improve this coverage

j i

D(i, j) i j

R

h

h

k

j

i

k

h

�

�

�

�

6. Algorithms for navigation in mobilesensor network

The following assumptions were made:

sensor nodes do not possess the necessary knowledge

to compute the shortest path locally and hence rely on

forwarding messages to their neighbors using a shor-

test-path heuristics;

a sensor node stores topological information it re-

ceives and forwards the topological information it

knows;

the Voronoi diagram-based minimum exposure path

approximation algorithm is used to further reduce the

computation (of exposure) at each sensor node.

To use the Voronoi diagram to estimate the minimum

exposure path, grid points are placed along Voronoi edges,

and grid points on the same Voronoi cell are fully

connected (see Fig.1).

The weight of an edge between two Voronoi grid

points is the single-sensor optimal solution weight for the

sensor corresponding to the Voronoi cell. However, this

weight only applies if the shortest path lies entirely within

the Voronoi cell. If the path strays beyond the Voronoi cell,

a straight line is used to weight the edges. Furthermore, the

single-sensor optimum solution is used to bound areas to

search; if the single-sensor optimum solution between two

points is larger than an already found estimated solution,

those two points are not investigated during subsequent

iterations of the localized algorithm.

Two types of messages, Forward messages and Search

messages, are passed among sensors in the sensor net-

work. Search message - the receiving node will search

locally. Forward message - the receiving node will for-

ward it to a neighboring sensor. To reduce the costs of

communication and computation on the wireless sensor

network, a heuristic method was applied to rapidly come

to a solution that is hoped to be close to the best possible

answer. A sensor node selects its neighbor node as the

recipient of the message. It would pick the node that has

potentially large number of distinct neighbors so that it

can quickly learn the network topography or it is close to

the destination. In [10], the authors combined these two

�

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Fig. 1. Voronoi diagram for sensor network. Circles repre-

sent sensors, ellipses a starting point and a destination

point.


Articles60

, ,

,

[11]. Czapski [12] suggested a fully adaptive routing

protocol for the wireless sensor network taking advantage

of the nature of sensed events to localization of individual

nodes. In [13], the authors described techniques enabling

the incorporation of GPS measurements with an IMU

sensor. A complementary filter known as the Kalman

Filter (KF) provides the possibility to integrate values

from the two sources whilst minimizing errors to provide

an accurate trajectory of the vehicle. The following GPS

and INU data is post-processed by an Extended KF (EKF).

In [14], the authors described a multiple sensor fusion-

based navigation approach using an interval analysis (IA)-

based adaptive mechanism for an Unscented Kalman

filter (UKF). The robot is equipped with inertial sensors

(INS), encoders and ultrasonic sensors.An UKF is used to

estimate the robot's position using the inertial sensors and

encoders. Since the UKF estimates may be affected by

bias, drift etc., an adaptive mechanism using IA to correct

these defects in estimates is suggested. In the presence of

landmarks, the complementary robot position information

from the IA algorithm using ultrasonic sensors is used to

estimate and bound the errors in the UKF robot position

estimate.

Using GPS system is costly and power consuming and

known collaborative methods of localization based on

positions of neighboring nodes also consume plenty of

energy and are not precise. In our paper, we consider

a network with a dynamically changing number of nodes.

Each node can move according to its own kinematics and

there is no correlation between them, but their initial

positions are known. The task of a navigation algorithm is

to estimate the position of each node at all instances in

a globally fixed coordinate system. There should be two

kinds of measurements: the displacement of each node

between two time instances (from inertial unit) and the

distance between any two nodes within a certain range at

each time instance (from RF measurements). The multi-

modal fusion problem can be presented as a graphical

model, where nodes represent variables and links repre-

sent constraints. The problem is to estimate the values of

the variables - representing nodes, given the constraints -

representing the links. For estimation of a single value for

each variable as the best solution, this can be formed as

a maximum a posteriori (MAP) estimation problem.

The standard Kalman Filter approach with a distribu-

ted iterative EKF formulation provides excellent accuracy

and convergence. In the distributed approach, the location

of each node estimates an independent computational unit

requiring very limited communications overhead to ex-

change intermediate location estimates with neighbors.

This formulation enables practical implementations with

minimum location accuracy reduction. Using a standard

EKF algorithm, the state vector of each node can be

estimated in parallel. However, since the estimation re-

sults of one node are strongly dependent on the estimated

locations of the other nodes, one pass through the algo-

rithm is not sufficient to accurately estimate the locations

of entire network. To solve this problem, Wu and others

[15] proposed an iterative distribution algorithm, in

which:

7. Iterative distribution algorithm

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�

�

�

each node estimates its state by using its inertial

navigation unit (INU) observation;

each node obtains RF distance measurements from all

the remaining nodes;

each node exchanges its state estimate with all the

remaining nodes;

the RF distance measurements are used to re-estimate

each node's state by EKF.

After multiple iterations of the last two steps, the state

of each node will converge to its optimization point. In this

manner, the locations of the entire sensor network can be

accurately estimated in parallel with virtually no

redundant computation and with minimal inter-node

information exchange.

The algorithm estimates limit errors in position esti-

mations by continuous fusion of new INU measurements

and previously fused location estimations. Position dis-

placements can be determined with MEMS-based INUs

and the distance between two nodes can be measured

by time-of-arrival TOA-based radio frequency distance

measuring sensors. The main underlying assumption of

the EKF approach is that the node state can be modeled as

a multidimensional Gaussian random variable. This as-

sumption is justified by the fact that an INU measurement

gives highly peaked unimodal distribution. The final

distribution of the state, after considering the ranging con-

straints, exhibits a dominant mode (the correct solution)

close to the INU peak. Since the distance measurement is

not a linear function of the locations, the standard Kalman

Filter approach with a distributed iterative EKF formu-

lation that provides excellent accuracy and convergence

was used. Since the INU measures the location offset,

which is directly proportional to velocity, the filter will

have nearly no lag. Moreover, the authors made a few

assumptions regarding the node motion and use a simple

constant velocity model for prediction, enabling flexible

implementation that accepts asynchronous sensor inputs.

They postulated the integration of two sensors: INU and

distance sensor to estimate absolute and relative positions

of sensor nodes. INU sensors prevent geometric ambi-

guities and distance sensors reduce the drift rate of the

individual INUs by a factor of by providing mutual

constraints on possible position estimates of collabo-

rating nodes. Collaborative navigation gives improve-

ments of performance in drift-rate reduction and resetting

errors of location estimates even after a dynamic change

of system conditions.

Considerations of error reduction effects expected

when multiple nodes collaborate to determine their

locations.

If and are vectors representing the INU-derived

estimates of the locations of nodes and respectively

and is the estimate of the distance from to (TOA

measurement), then the location of node can be estima-

ted by the average of INU in node or by: .

If and have independent errors of size and the

error of is negligible as compared to it, then this average

is statistically optimum and has the reduced error of

2. The error can be further reduced when additional

�

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�

� �

n

n

r r

A B

r B A

A

A r r r

r r

r

/

8. Calculating errors

A B

AB

A B AB

B A INU

AB

INU


Articles 61

nodes collaborate in RF distance measurements. The error

of an average of independent and identically distributed

measurements with common error ! is: ;

If = and 1 (5)

are identically distributed measurements with common

standard deviation then:

= (6)

For a -sized network with direct communication,

a given node has independent INU location estimates.

One estimate is from its own INU; the remaining 1 are

based on the INU location estimates of the remaining 1

nodes, the inter-node distance measurements and the

previously fused location estimates. A combination of

independent INU estimates provides a 1!/ error im-

provement. This improvement is independent of the spe-

cific multi-sensor data fusion algorithm employed, pro-

vided the fusion algorithm is formulated in a way that

leverages the principles of averaging independent sensor

estimates. It is also independent of specific noise model of

the underlying sensor (INU or otherwise) that is used to

obtain the base position estimate. In practice, instead of

requiring the relative location of B from A, only the dis-

tance between them is needed for the scaling result to hold.

The use of continuous INU measurements and previously

fused location estimates virtually eliminates geometric

ambiguities like flips, translations, and rotations.

The derivation of the error-scaling law assumes that

the ranging measurement error is negligible compared to

that of the inertial measurement to achieve the 1!/

improvement.

The analysis presented in [16] shows that the scaling

law improvement is quite insensitive to distance measu-

rement errors and that its effect should hold quite well

even as the distance measurement error approaches that of

the inertial sensors. Reasonable values of INU and ran-

ging error result in a deviation from the ideal scaling

behavior by only a few percent, while a typical worst-case

combination yields at most an 11 percent deviation.

When nodes with independent self-location estimates

come within range of one another for distance measure-

ment, the errors of all their location estimates are imme-

diately reset to lower values. Reset error levels are inde-

pendent of the history of ranging activities among the

nodes and long-term error growth is insensitive to splitting

or joining of the group. The errors in incremental node

displacement estimates contribute to the errors in the

location estimates in an additive fashion. Let and

be real displacement and estimated by INU displacement

of node made between and respectively. The addi-

tive uncertainty associated with this estimate is denoted

with a common variance . The location estimates

of node at time are given in [17]

(1) = + (7)

n

n

y x (i= , ... ,n)

n

n

n

n

n

n

n

x dx

i t t

N

i t

x dx x N

� �

�

�

�

�

�

�

�

�

�

i

x

y

k k

k k+

k INI

k

K k k k

8.1. Sensitivity to Distance Constraint Error

8.2. Resetting errors of all location estimates

i i

i

i i i i

1

2

If at time node joins a group of 1 other nodes,

then, using the independent averaging assumption, the

location estimate of node becomes

( ) = (1) + (8)

where is the highly accurate location of node relative

to node enabled by distance measurement.

Although the position uncertainty of a node in a small

group may grow while the groups are separated, it is

immediately reset to the appropriate lower value when the

groups merge to form a larger group.

Analytical prediction of these multi-sensor fusion

algorithms should be checked using some simulations. In

[17], the authors gave a few formulas for the evaluation of

the algorithms' performance. Absolute and relative RMS

errors can be calculated using the results of Monte Carlo

simulations. The absolute RMS error vector of the entire

network at will be

( ) = ( ) ( )) (9)

where ( ) = ( ), ( ) , ( ) , ( ), ( ) (10)

are the estimated locations and real locations, respecti-

vely, of the node in the Monte Carlo run; and is the

total number of the runs.

The relative averaged RMS error can be defined as

( ) = ( ( ) ( )) (11)

where and are the estimated distance and real

distance, respectively, between nodes and at time

from the run.

Calculation of exposure is one of fundamental pro-

blems in wireless ad-hoc sensor networks. This paper

introduced the exposure-based coverage model, formally

defined the exposure and analyzed several of its proper-

ties. An efficient and effective algorithm for minimum

exposure paths for any given distribution and

characteristic of sensor networks was presented. The

minimum exposure path algorithm developed as a locali-

zed approximation algorithm was chosen for planned

network of numerous small mobile sensor nodes used for

monitoring of large terrains. The algorithm works for

arbitrary sensing and intensity models and provides an

unbounded level of accuracy as a function of run time.

It works even after a few nodes of network are damaged

and requires minimum consumption of energy. The

second problem arising after the calculation of the

network's coverage is to move some of the nodes in order

to improve this coverage and to navigate nodes effectively

(also in GPS-denied areas). Position displacement can be

determined with micro-electromechanical system-based

INUs. As the author of [18] convinces us, for many navi-

gation applications, improved accuracy/performance is

t i n

i

x n x x

x i

j

t

k x k x k

k k k k x k x k

i l m

k d k d k

d d

i j t

l

k

K K ij

ij

k

i i

x y z i i

ij ij

ij ij

�

� �

�

� �

�

i i

l l

l l

l l

l l

8.3. Evaluation of performance

k

2

2

th th

th

9. Conclusions


(x + ... + x )1 n 1

1

11

�x

n n

mn

n n( 1)�m

�n

K K K

k=1

n

m

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l=1

i=1l=1

i=1

j=i+1

k=1 k=1

Articles62


not necessarily the most important issue, but meeting

performance at reduced cost and size is definitely vital.

In particular, small navigation sensor size enables the

introduction of guidance, navigation, and control into

applications previously considered out of reach. In recent

years, three major technologies have enabled advance-

ments in military and commercial capabilities. These are

Ring Laser Gyros, Fiber Optic Gyros, and Micro-Electro-

Mechanical Systems (MEMS). The smallest INUs

presented have the size of 3.3 cc. Distributed fusion of

multiple independent sensors using the suggested navi-

gation algorithms can exploit the complementary nature

of each sensor-nodes characteristics for overall improve-

ments in system accuracy and operational performance

without sacrificing operational flexibility, estimating both

absolute and relative positions for the members of a mo-

bile sensor network by continuously fusing pair-wise

inter-node distance measurements and the position dis-

placement measurements of individual nodes. The bene-

fits of the collaborative error reduction can be realized

without use of anchor reference nodes and also with as few

as two sensor nodes.

It is likely that progress in computing and sensing

technologies will soon determine new criteria for algo-

rithms of mobile sensor-nodes and, therefore, in our future

works, we plan to make adequate simulations with both

algorithms working simultaneously on sensor nodes with

different environment parameters.

- Industrial

Research Institute for Automation and Measurements,

PIAP, Al. Jerozolimskie 202, 02-486 Warsaw, tel.: +48-

22-8740-377, fax: +48-22-8740-204.

E-mail: [email protected].


AUTHORS

Goszczynski Tadeusz*, Pilat Zbigniew

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[6] Meguerdichian S., Koushanfar F., Qu G., Potkonjak M.,

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robot localisation and navigation using multi-sensor

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Aerospace, Power and Sensors, RMCS, Cranfield

University, Shrivenham, Swindon, UK - SN6 8LA.

Available at: http://cswww.essex.ac.uk/technical-

reports/2004/cs

[15] Wu S., Kaba J., Mau S., Zhao T., ”Teamwork in GPS-

Denied Environments. Fusion of Multi-Sensor

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[16] Zhao T., Wu S., Mau S.C., Kaba J., “Collaborative

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[17] Wu S., Kaba J., Mau S., Zhao T., ”Distributed Multi-

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RTO EDUCATIONAL NOTES EN-SET-064

th th

th th

�

-

-

VOLUME 5, N° 1 2011

Articles 63


SPECIAL ISSUE

Editorial to theSpecial Issue Section on

Hybrid Intelligent Systems forOptimization and Pattern Recognition

Part II

Guest Editors:

Oscar Castillo and Patricia Melin


Editorial to the Special Issue Section on

“Hybrid Intelligent Systems for Optimization and Pattern Recognition - Part II”

The special issue on hybrid intelligent systems for optimization and pattern recognition comprises five

contributions, which are selected and extended versions of papers previously presented at the International

Seminar on Computational Intelligence held at Tijuana, Mexico on January of 2010. The papers describe

different contributions to the area of hybrid intelligent systems with application on optimization and pattern

recognition. In the papers, an optimal combination of intelligent techniques is applied to solve in an efficient

and accurate manner a problem in a particular area of application.

In the first paper, by Mario I. Chacon-Murguia , a method for Fusion of Door and Corner Features for

Scene Recognition is presented. This paper presents a scenery recognition system using a neural network

hierarchical approach. The system is based on information fusion in indoor scenarios. The system extracts

relevant information with respect to color and landmarks. Color information is related mainly to localization of

doors. Landmarks are related to corner detection. The corner detection method proposed in the paper based on

corner detection windows has 99% detection of real corners and 13.43% of false positives.

In the second paper, by Martha Cárdenas ., the Optimization of a Modular Neural Network for Pattern

Recognition using Parallel Genetic Algorithm is presented. In this paper, the implementation of a Parallel

Genetic Algorithm for the training stage and the optimization of a monolithic and modular neural network for

patter recognition is presented. The optimization consists in obtaining the best architecture in layers, and

neurons per layer achieving the less training error in a shorter time. The implementation was performed in

a multi-core architecture, using parallel programming techniques to exploit its resources. We present the results

obtained in terms of performance by comparing results of the training stage for sequential and parallel

implementations.

In the third paper, by Claudia Gómez Santillán , an Adaptive Ant-Colony Algorithm for Semantic

Query Routing is presented. In this paper, a new ant-colony algorithm, Adaptive Neighboring-Ant Search

(AdaNAS), for the semantic query routing problem (SQRP) in a P2P network is described. The proposed

algorithm incorporates an adaptive control parameter tuning technique for runtime estimation of the time-to-

live (TTL) of the ants. AdaNAS uses three strategies that take advantage of the local environment: learning,

characterization, and exploration. Two classical learning rules are used to gain experience on past performance

using three new learning functions based on the distance traveled and the resources found by the ants.

The experimental results show that the AdaNAS algorithm outperforms the NAS algorithm where the TTL

value is not tuned at runtime.

In the fourth paper, by Leslie Astudillo , a new Optimization Method Based on a Paradigm Inspired by

Nature is described. A new optimization method for soft computing problems is presented, which is inspired

on a nature paradigm: the reaction methods existing on chemistry, and the way the elements combine with

each other to form compounds, in other words, quantum chemistry. This paper is the first approach for the

proposed method, and it presents the background, main ideas, desired goals and preliminary results in

optimization.

In the fifth paper, by Marco Aurelio Sotelo-Figueroa , the Application of the Bee Swarm Optimization

BSO to the Knapsack Problem is presented. In this paper, a novel hybrid algorithm based on the Bees

Algorithm (BA) and Particle Swarm Optimization (PSO) is applied to the Knapsack Problem. The Bee

Algorithm is a new population-based search algorithm inspired by the natural foraging behavior of honey bees,

it performs a kind of exploitative neighborhood search combined with random explorative search to scan the

solution, but the results obtained with this algorithm in the Knapsack Problem are not very good. Although the

et al.

et al

et al.

et al.

et al.

Editorial

Oscar Castillo*, Patricia Melin

VOLUME 5, N° 1 2011

66 Editorial


combination of BA and PSO is given by BSO, Bee Swarm Optimization, this algorithm uses the velocity

vector and the collective memories of PSO and the search based on the BA and the results are much better.

In conclusion, this special issue represents a contribution to the state of the art in the area of hybrid

intelligent systems with application on optimization and pattern recognition.

Oscar Castillo

Tijuana Institute of Technology, Tijuana, Mexico

[email protected]

Guest Editors:

Patricia Melin and

[email protected],

VOLUME 5, N° 1 2011

67Editorial

Abstract:

1. Introduction

-

Scene recognition is a paramount task for autonomous

systems that navigate in open scenarios. In order to achie

ve high scene recognition performance it is necesary to

use correct information. Therefore, data fusion is beco

ming a paramount point in the design of scene recognition

systems. This paper presents a scenery recognition system

using a neural network hierarchical approach. The system

is based on information fusion in indoor scenarios. The

system extracts relevant information with respect to color

and landmarks. Color information is related mainly to

localization of doors. Landmarks are related to corner de

tection. The corner detection method proposed in the pa

per based on corner detection windows has 99% detection

of real corners and 13.43% of false positives. The hierar

chical neural systems consist on two levels. The first level

is built with one neural network and the second level with

two. The hierarchical neural system, based on feed for

ward architectures, presents 90% of correct recognition in

the first level in training, and 95% in validation. The first

ANN in the second level shows 90.90% of correct recogni

tion during training, and 87.5% in validation. The second

ANN has a performance of 93.75% and 91.66% during

training and validation, respectively. The total perfor

mance of the systems was 86.6% during training, and 90%

in validation.

-

-

-

-

-

-

-

-

-

-

-

-

-

-

Keywords: scene recognition, robotics, corner detection.

The advance of science and technology has motivated

new and more complex engineering applications. These

new challenges must involve not only the design of adap

tive and dynamic systems but also the use of correct infor

mation. It is everyday more evident that good multicriteria

decision making systems requires the fusion of data from

multiple sources. A research area where data fusion has

become a fundamental issue is autonomous robot naviga

tion. Making a robot to navigate and perceive its environ

ment requires similar information as the used by a human

[1], [2], [3]. This information usually comes from range

detection sensors such as ultrasonic, laser, or infrared, and

also from image acquisition sensors, such as CCD or

CMOS cameras [4]. The information of each sensor must

be processed adequately in order to extract useful infor

mation for the navigation system of the robot. One para

mount issue in autonomous navigation of robots is related

to scenery recognition. Recognition of sceneries consists

on the identification of a scenario perceived through mea

surements provided by a sensor. The sensor may be any of

the previously mentioned. However, vision sensors are the

most frequently used in this task [5], [6], [7]. The advan

tage of a vision sensor is that it provides compound infor

mation that may be separated into useful properties like

color, edges, texture, shape, spatial relation, etc. There

fore, it is possible to achieve data fusion with the infor

mation of a vision sensor.

This paper presents the design of a hierarchical neural

system for scene recognition using information fusion

from indoor scenarios provided by a camera. The problem

to solve is constrained to the recognition of 10 indoor sce

narios shown in Figure 1. The features used in the design

of the hierarchical neural networks are related to door

position, and corner detection.

The paper is organized in the next sections. Section 2

presents the corner detection method. Door detection

through color analysis and segmentation is explained in

Section 3. Section 4 describes the design of the hierar

chical neural network, and the paper concludes with the

results and conclusions in Section 5.

-

-

-

-

-

-

-

-

-

-

-

Fig. 1. Scenarios to be recognized.

2. Corner detection methodCorner detection is used in areas such as robotics and

medicine. In robotics, it is used for data fusion, navi

gation, and scene recognition. In medicine, it is applied for

image registration such as x-rays, ultrasounds, and me

dical diagnostics [8]. In other applications, corner detec

tion is used for object recognition, stereo vision, motion

detection, among many other usages [9].

Corners are the features more abundant in images of

the real world, in contrast to straight lines [10]. For this

reason, the use of corners is commonly found in tasks such

as image matching. One of the advantages that corners

offer is that, if we have images of the same scene, although

taken from different perspectives, we will find almost the

same corners, which is a good feature for image regis

tration. That in turn will provide information for naviga

FUSION OF DOOR AND CORNER FEATURES FOR SCENE RECOGNITION

Mario I. Chacon-Murguia, Rafael Sandoval-Rodriguez, Cynthia P. Guerrero-Saucedo


Articles68

tion of mobile robots. There are different definitions for

what it is considered a 'corner'. F. Mokhtarian and

R. Suomela, in [11], explain that the points of corners in an

image are defined as points where the contour of the image

has its maximum curvature. Juan Andrade-Cetto, in [12],

mentions that corners are one of the most simple features

which can be extracted from an image, and define corners

as those points in an image where there is change in the

intensity in more than one direction. Krishnan Rangarajan

[13] describe a corner as the point of union of two or

more straight lines. In this paper, a corner is considered

accordingly to the definition of Rangarajan. Based on this

consideration, the proposed method described in this

paper locates corners based on corner window analysis.

The methods for the detection of corners can be divi

ded in two groups: those which can accomplish the detec

tion from the image in gray scale, and those which first

detect edges and then detect corners. Among the methods

of the first group, the most mentioned in the literature are

the method of SUSAN [14] and the method of Harris [15].

The method of SUSAN differentiates from other methods

in that it does not compute the derivative of the image

under analysis and that it is not necessary to reduce the

noise that could be present in the image. It uses a circular

mask which scans the whole image, comparing the gray

levels of the central pixel in the mask and the rest of the

pixels inside the mask. All the pixels with a gray level

equal to the central pixel level are considered as part of the

same object. This area is called USAN (Univalue Segment

Assimilating Nucleus). The USAN area has a maximum

value when the center is in a plain region of the image,

a mean value when it is on an edge, and a minimum value

when it is on a corner.

The method of Harris is more sensitive to noise beca

use it is based on the first derivative of the image. How

ever, it is invariant to rotation, translation and illumina

tion, which give it advantages over other methods. This

method uses a window which scans the image and deter

mine sudden changes in gray levels which results from

rotating the window in several directions.

Among the second group of corner detectors, which

use any method of edge detectors, we can mention the one

of X.C. He and N.H.C. Yung [16]. They use the method of

Canny and indicate the steps to follow for the detection of

corners calculating the curvature for each edge. Other

authors use windows for corner detection from edge

images, such as K. Rangarajan [13]. In a similar way,

G. Aguilar [17], compare images of fingerprints for

the identification of persons using 3x3 windows. On tho

se, they propose different bifurcations to be found, which

we could call 'corners'. W. F. Leung [18], use 23 win

dows of different bifurcations, and 28 different windows

of other type of corners for their detection in the finger

print image using neural networks. The method described

in this work is based on the second group of corner detec

tors. Those which first apply edge detection and then de

tect corners using windows over the edge image.

The general scheme of the corner detection method is

shown in Figure 2. The original image ( ) is convolved

with a Gaussian filter to remove noise that could be pre

sent in the image, yielding the image ( ). A gradient

operator and a threshold to determine the edges are appli

et al.

et al.

et al.

et al.

I x, y

G

Is x,y

-

-

-

-

-

-

-

-

-

-

-

-

ed to the image . These two operations correspond

to the edge detection using the method of Canny. The

Canny method was used because it yielded better results

than the Sobel and other common edge operators. The re

sulting image is convolved (*) with 30 corner de

tection windows, of order 3x3, to detect the corners

present in the image. The resulting image contains

the corners found.

The corner definition adopted in this work is the one

provided by Rangarajan. It is necessary to find the main

line intersections of the scene under analysis. These lines

are detected through an edge detection procedure. Among

the edge detector operators tested in this work were Sobel,

Prewitt, Robert, Canny, and Laplacian. It was decided to

use the Canny edge detection method [18], because it ge

nerated the best edges in the experiments achieved in this

research. It is also one of the most mentioned and used

edge detector methods in the literature. The Canny me

thod was developed by John F. Canny in 1986. This me

thod detects edges searching maxima of the image gra

dient. The gradient is obtained using the derivative of

a Gaussian filter. The edges are defined by considering

two thresholds related to weak and strong edges which

makes the method more robust under noise circumstan

ces. The method uses two thresholds, to detect strong and

weak edges, and includes the weak edges in the output

only if they are connected to strong edges. This method is

therefore less likely than others to be fooled by noise, and

more likely to detect true weak edges. For a complete des

cription of the method, the reader is encouraged to read

[19]. The parameters used for the Canny method were, =

1.2, = 0.18, and = 0.18. These values were chosen

after several experimentation results.

The papers from G. Aguilar [17], and W.F. Leung

[18], coincide in that there are different types of bifur

cations or corners that we call them,Y´s, V´s, T´s, L´s, and

X´s, accordingly to the form they take, as shown in Figure

3. Based on the similitude of these corners with fingerprint

marks, it was decided to investigate the possibility of

using a unified theory between fingerprint recognition and

scene recognition. Thus, from the fingerprint recognition

works, some windows were chosen to detect corners.

Is x,y

I x,y

w

I x,y

µ

( )

( )

( )

-

-

-

-

-

-

-

-

b

c

e

Fig. 2. Corner detection process.

H µL

et al.

Fig. 3. Type of corners.

2.1. Edge Detection

2.2. Corner detection windows

-

�


Articles 69

Each corner detection window is then associated with

an index window

for (1)

obtained by

(2)

where the multiplication is element by element and not

a matrix multiplication. In this way, each window is

related to an index window . In the same way, each index

window can be associated to a total weighting factor

obtained by

(3)

where the corresponds to the weighting factor in .

Corner detection of a scene is accomplished by the

next steps. First convolve the binary Canny result image

( ) with the index matrix

(4)

This step yields the possible corners related to each

corner window . The next step is to decide which of the

possible candidate pixels in each is a corner that

corresponds to . This process is realized scanning the

and assigning a pixel value according to

(5)

to produce a new set of images , where

and . The value 1 indicates that the

pixel is a corner of the type . This process ends

up with 30 binary images that indicate the position of the

different type of corners. The final step consists on the

union of the images to produce the final corners

(6)

Experiments of corner detection were performed on

images from 16 different scenarios, see Figure 7. Scenarios

1 to 10 represent the environment of interest in this work.

They correspond to the real environment where a mobile

robot is going to navigate across. Scenarios 11 to 16 were

added only for evaluation purposes in other scenarios. Be

sides, semi-artificial scenarios to compute the quantitative

performance were obtained from some of these real scena

rios. The purpose of using semi-artificial scenes is to obtain

a correct performance measure of the proposed method,

which would be hard to compute in real (noisy) scenes.

These type of images allow to compute false positives and

false negatives detections in a simpler form, and to achieve

Fig. 6. Generic weight matrix, .

B

c,i

I x,y

w

T

T w B

B w T

w

B

B

b

b B

B

I x,y I x,y B

I x,y

w

I x,y

p x,y

I x,y p x,y

I x,y p x,y I x,y

p x,y w

I x,y

I x,y I x,y

n

i

n c i

i c n

c

i

i i

i i

i i

b i

ci b i

c

ci

c

ci

ei

ei ci

ci ei ei

ci c

ei

FC ei

: = 1,..., 30

=

= 1+

= * +1

=

�

�

�

( ) ( )

( )

( )

( )

( ) ( )

( ) ( ) ( )

( )

( )

( ) ( )=

-

-

�

� �

�

�

These selected windows in addition to other proposed

in this work make a set of 30 windows. Each corner detec

tion window, , is a 3x3 mask and their structures are

illustrated in Figures 4 and 5. The set of windows is

composed as follows. Windows , , , and , are four

windows modified from the work of Leung [18]. The

modification consists on the aggregation of one pixel,

because they try to find terminal points, and in our case we

look for crossing lines. The extra pixel is darkened in these

windows. Windows to were also taken from Leung.

The windows to appear in Aguilar [17]. The

subset to are windows proposed in this paper. The

proposed windows were defined by analysis of the corners

usually found in the set of images considered in this work.

Corner detection is achieved through a windows ma

tching process. The image is scanned with the different

corner detection windows wc, trying to match the window

corner shape with the edge pixels. Assuming a 3x3 neigh

borhood and two possible values {0,1} for each pixel, the

number of permutations is 2 = 512. The values 2 for =

0,1,…, 8 can be considered as weighting values to yield

a generic weight matrix , starting at the coordinate

(1, 1) as shown in Figure 6.

-

w

w

w w w w

w w

w w

w w

c

c

1 2 3 4

5 20

17 20

21 30

1 16

17 30

et al.

et al.

Fig. 4. Windows w to w for corner detection.

Fig. 5. Windows w to w for corner detection.

n

T

p

2.3. Corner detection

-

-

9 n

n

�


b Bi i�

i = 1

30

1 p x,yci i( ) = �

o otherwise

��

Articles70

an analysis of these cases. A semi-artificial image is a sim

plified image extracted from a real scene. Results of the

application of the proposed method to the scenarios are

shown in Figure 8. Figure 8a is the semi-artificial scenario

obtained from a real one; the corners to be detected are in

8b. The detected corners with the Harris and proposed me

thods are illustrated in 8c and 8d, respectively. A summary

showing the performances of both, the proposed and the

Harris methods are shown in Tables 1 and 2. The detection

-

-

of real corners is very alike in the two methods, 99% and

98%, respectively. Where there is a more noticeable diffe

rence is in the false positives, where the proposed method

has 13.43%, while the Harris has a 25.86%. Comparison

with the SUSAN algorithm is not possible because it re

quires multi gray level information. In the case of the Harris

method, it was assumed two-gray-level images. A qualita

tive comparison will be given over the original images later

on. Our explanation for the false negatives and false posi

-

-

-

-


Fig. 7. Scenarios considered to test the corned detection method.

Fig. 8. a) Semi-artificial scenarios, 3, 6, 10, 15,16. b) corners to detect, c) Harris detection, c) SUSAN detection,

d) detection with the proposed method.

a) b) c) d)

Articles 71

a) b) c)

Fig. 12. A block diagram of the process for door detection.

Fig. 11. Corner detection by a) Harris b) SUSAN and c)

Proposed method.

3. Scene Segmentation

-

-

-

-

-

-

-

-

-

-

Interior environments are highly structured scenarios,

because they are designed under specific conditions rela

ted to size, shape, position and orientation of their compo

nents like doors, walls, aisles. Therefore, finding land

marks of these scenarios are important hints for autono

mous robot navigation systems. Z. Chen and S.T. Birch

field [19], state that doors are particular landmarks for na

vigation since they indicate input and output points. Besi

des, doors provide stable structures and they are semanti

cally significant.

This section describes the segmentation process to ob

tain features related to the doors found in the scenarios un

der analysis. The process is shown in Figure 12. The RGB

image is transformed to the HSV color space to be more

tolerant to illumination changes [19]. Then detection of the

doors is achieved by color analysis.

tives is as follows. False negatives are mainly due to cor

ners that do not match exactly to any . For example, two

false negatives of scene 2 are indicated with circles in Figu

re 9a.Aclose view of these cases is illustrated in Figure 9b;

it can be observed that the missed left corner does not

match exactly with the . On the other hand, false positi

ves tend to appear as a consequence of multiple corner de

tections. This is because more than one make a match

with the image edge structure. Figure 10 shows multiple

detections in scene 1. It also shows the corner structures ,

, , that make a match with the , and windows.

Figure 11 shows the corners detected by Harris (a),

SUSAN (b), and the proposed method (c). It can be

observed that, in general, Harris and SUSAN tend to

detect more corners than the proposed method. However,

the false positive rate is assumed to be very high, as proved

with the semi-artificial images using the Harris method.

Considering that corner information is used for robot na

vigation, high rate on false positives may lead to compli

cate more the scene recognition than the lack of some

corners.

-

-

-

-

-

-

w

w

w

w

w w w w w

w

c

c

27

5

8 9 28 5 8

27

Table 1. Performance of the proposed method.

Table 2. Performance of the Harris method.

Fig. 9. a) False negatives, b) One false negative zoomed

out, c) Pixel view, d) Window .

Fig. 10. False positives in scene 1.


Scenario

3

6

10

15

16

Total

Scenario

3

6

10

15

16

Total

Real

corners

42

55

32

34

38

201

Real

corners

42

55

32

34

38

201

Corner

detected

40

61

36

46

43

226

Corner

detected

55

70

43

42

40

250

Hits

40/95.23%

55/100%

32/100%

34/100%

38/100%

199/99%

Hits

41/97.6%

52/94.5%

32/100%

34/100%

38/100%

197/98%

False

positives

0/0%

6/11%

4/12%

12/35%

5/13%

27/13.43%

False

positives

14/33%

18/33%

11/34%

8/24%

1/2.6%

52/25.86%

False

negatives

2/5%

0/0%

0/0%

0/0%

0/0%

2/1%

False

negatives

1/2.4%

3/5.5%

0/0%

0/0%

0/0%

4/2%

Articles72


3.1. RGB to HSV color space

3.2. HSV component analysis

The HSV color space is one of the spaces commonly

used in color analysis. Schwarz, Cowan and Beatty [20],

performed an experiment to compare five color-space mo-

dels. In this experiment they found significant differences.

In particular, they found that the RGB model is fast but it is

imprecise. On the other hand, the HSV is not as fast as the

RGB but it is very precise for color analysis.

The color space transformation from RGB to HSV is

obtained by the following equations

(7)

(8)

(9)

Statistics of the HSV component values where deter-

mined by a sampling process. The sampling consisted on

a set of 11samples from each door in the scenarios, see

Figure 13. Each sample corresponds to a window of 5x5

pixels. The process involved the computation of the mean

and variance of the mean distributions of the windows

samples over the HSV values. Table 3 shows the mean dis-

tributions over the scenarios, while Table 4 presents the

statistic values of the means.

V

Fig. 13. Color door sampling in two scenarios.

Table 3. Mean distributions over the scenarios.

= maxi ( )R, G, B

Table 4. Statistic values of the means.

H H T and S S T and V V T

Then p x,

H S V p

x,y T T T

T H H T S S T V V

Fig. 14. Door segmentation.

Door detection in autonomous robot navigation is an

important issue because they appear in many interior envi

ronments [21]. Thus, doors are important landmarks that

can be used for scene recognition. Door detection is achie

ved in this work by analysis of the HSV components im

plemented in the following condition

if

(10)

where , , and are the HSV components of the pixel

at coordinates ( ). The thresholds , , and are

(11)

After the classification of the color pixels, blobs of less

than 300 pixels are eliminated since they are not conside

red doors. Figure 14 illustrates some examples of doors

detected by the previous method.

The recognition of the scenarios is achieved with a hie

rarchical neural network, HNN. This type of architecture

was selected due to the similarity of some of the scenarios.

The HNN is composed of two levels, Figure 15. The first

level is composed by one neural network and the second

by two neural networks.

The idea of this HNN is to separate the scenarios into

4 classes, and then use the second level to resolve more

specific cases.

The first neural network is a feedforward backpro

pagation network with 32 inputs, 32 neurons in the hidden

layer, and 4 output neurons, sigmoid tangent activation

functions in the hidden layer, and sigmoid logarithmic

functions in the output layer. This first neural network is

trained to classify the four classes, see Figure 16, using the

next feature vector

3.3. Door segmentation

-

-

-

) is a door pixel

= = =

-

-

-

� � � � � � � � �

� � � � � � � � �

p h p s p v

p p p

h s v

h s v

( y

� � �

4. Recognition of scenarios

VOLUME 5, N° 1 2011

1max( ) =R, G, B R

and G B6 max( ) minR, G, B � ( )R, G, B

6 max( ) minR, G, B � ( )R, G, B

11 max( ) =R, G, B R

and G B�

1 1max( ) =R, G, B G

6 max( ) min( )R, G, B R, G, B� 3

3

1 2max( ) =R, G, B B

6 max( ) min( )R, G, B R, G, B�

G � Bif

B R�if

HB R�

if

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Scenario

2

3

4

5

7

9

10

H Mean

0.078

0.059

0.084

0.075

0.099

0.075

0.078

S Mean

0.361

0.471

0.605

0.393

0.367

0.576

0.308

V Mean

0.357

0.205

0.252

0.360

0.361

0.243

0.372

Component

H

S

V

Mean

H

S

V

= 0.078

= 0.440

= 0.307

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H

S

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= 0.077

Articles 73


(12)

here and are the centroids of the blobs that corres

ponds to the doors, while and are the normalized

height and width, respectively, of those blobs. Figure 17

presents examples of the door blobs with their respective

centroids.

The neural network of the second level is trained to

classify classes 2 and 4 into their corresponding scenarios,

as shown in Figure 18, using the next feature vector

(13)

where and are the centroids of the corner coordi

nates and is the number of corners found in the scenario.

Figure 19 presents examples of the corners with their

respective centroids.

C C

C C

N

Fig. 15. Hierarchical neural network scheme.

P P

E Ey

xi yi

i i

x

-

-

The neural network of the second level is a feedfor-

ward-backpropagation, with 3 inputs, 26 neurons in the

hidden layer, 2 output neurons, sigmoid tangent activation

functions in the hidden layer, and sigmoid logarithmic

functions in the output layer.

h a

Fig. 16. First level of classification.

Fig. 17. Examples of door centroids.

Fig. 18. Second level of classification.

-

-

-

-

-

-

Fig. 19. Examples of corner centroids.

Results of the scenario recognition are commented

next. The performance by levels of the HNN over the 10

scenarios considering the two levels is illustrated in

Figure 20. The first classification was achieved by door

detection using the centroids, height and area of the door

blobs. The ANN of the first level was trained to classify

the 10 scenarios into 4 classes. This ANN had 90% of

correct classification during training, and 95% in valida

tion. Class 1 that contains the scenarios 2, 5, 7, and 10 was

considered as one type of scenario because of the high

degree of similarity among the four scenarios. This simila

rity turns to be hard to resolve even for human observers.

Class 3 was not reclassified because it only contains ima

ges of scenario 9. Regarding the classification of scenarios

in the classes 2 and 4, in the second level, it was performed

by using corner detection information as well as the num

ber of corners. ANNs 2 and 3 were trained with this infor

mation. TheANN 2 separated class 2 into scenario 3 and 4

wih a performance of 90.90% in training and 87.5% du

ring validation. The neural network 3 that classifies class 4

into the scenarios 1, 6, and 8 has a performance of 93.75%

VOLUME 5, N° 1 2011

1

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X

CPxi

CPyi

hi

ai

2, 3

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Articles74


in training, and 91.66% in validation.

The total performance of the systems considering

all the scenarios was 86.66% for training, and 90% in

validation.

Two important results are derived from this work. The

first one is related to the proposed corner detector method

and the second to the recognition for scenarios. In regards

the corner detector method we can mention that the

proposed method has similar hit performance in semi-

artificial scenarios as the Harris detector, 99% and 98%,

and false negatives 1% and 2%, respectively. However the

proposed method is better with respect to false positives,

13.43% and 25.86%. On the other hand, the use of com

bined information, door and corner information, provide

important discriminative data validated by the hierar

chical ANN. The ANN findings present an adequate over

all performance of 86.66% for training, and 90% in

validation.

In conclusion, it can be said that the proposed corner

detector method shows good corner detection in semi-

artificial as well as in real indoor and outdoor scenarios as

it was validated in the corner detection experiments per

formed in this research. Besides, the corner detection me

thod provides correct information that is validated with

the performance achieved in theANNs 2 and 3.

Another important conclusion is that the proposed

solution to the scene recognition problem based on fusion

of color and corner features proved to be effective based

on the experimental results obtained in this work.

Results shown in this research confirm that complex

problems like scene recognition for robot navigation are

well faced with information fusion where different type of

information complements each other.

Fig. 20. System performance for levels.

5.

6.

Results

Conclusions

-

-

-

-

-

ACKNOWLEDGMENTS

The authors thanks to Fondo Mixto de Fomento a la Investi

gación Científica y Tecnológica CONACYT - Gobierno del

Estado de Chihuahua, by the support of this research under grant

CHIH-2009-C02-125358. This work was also supported by

SEP-DGEST under Grants 2173.09-Pand 2172.09-P

-

.

AUTHORS

-

References

Mario I. Chacon-Murguia*, Rafael Sandoval-Rodri

guez, Cynthia P. Guerrero-Saucedo -

.


Visual Perception

on Robotic Applications Lab Chihuahua Institute of

Technology, Chihuahua, Mexico. E-mails:

{mchacon, rsandoval, cpguerrero}@itchihuahua.edu.mx

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20-29,.

[2] Durrant-Whyte H., Bailey T., “Simultaneous Locali-

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[3] Bailey T., Durrant-Whyte H., “Simultaneous Locali-

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, vol. 24, no. 2, February

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, September 2007, pp. 24-34.

[7] Srinivasan M.V., Thurrowgood S., Soccol D., “Compe-

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, September 2009, pp. 59-71.

[8] Antoine Maint J.B., Viergever M.A., “A Survey of

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1998, vol. 2, no. 1, pp. 137.

[9] Zitova B., Flusser J., “Image Registration Methods:

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[10] TissainayagamP.,Suter D., ”Assessing the Performance

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pp. 663-679.

[11] Mokhtarian F., Suomela R., “Curvature Scale Space for

Robust Image Corner Detection”. In: -

, Brisbane, Australia,

1998.

[12] Andrade J.,

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[21] Cariñena P., Regueiro C., OteroA., BugarínA., Barro S.,

VOLUME 5, N° 1 2011

Articles76

Abstract:

1. Introduction

-

In this paper, the implementation of a Parallel Genetic

Algorithm (PGA) for the training stage, and the optimi

zation of a monolithic and modular neural network, for

pattern recognition are presented. The optimization con

sists in obtaining the best architecture in layers, and neu

rons per layer achieving the less training error in a shor

ter time. The implementation was performed in a multi-

core architecture, using parallel programming techniques

to exploit its resources. We present the results obtained in

terms of performance by comparing results of the training

stage for sequential and parallel implementations.

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-

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Keywords: modular neural networks, parallel genetic

algorithm, multi-core.

The recognition of individuals, from their biometric

features, has been driven by the need of security applica

tions, mainly of security such as in surveillance systems

for control of employee assistance, access control security

places, etc. These systems, have been developed with dif

ferent biometrics including face recognition, fingerprints,

iris, voice, hand geometry, and more [15]. Although there

are systems based on classical methods, biometric pattern

recognition developed in the area of artificial intelligence

with techniques such as fuzzy logic, data mining, neural

networks, and genetic algorithms.

Real-world problems are complex to solve and require

intelligent systems that combine knowledge, techniques

and methodologies from various sources. In this case, we

are talking about hybrid systems, and these can be obser

ved in some applications already developed in [6], [13],

[14].

Artificial Neural Networks applied to pattern recogni

tion have proved to give good results. Therefore is com

plex to deal with monolithic neural networks. The use of

modular neural networks can divide the complex problem

into several task smaller, in order to get a efficient system

and good results.

Artificial Neural Networks and Modular Neural Net

works have high potential for parallel processing. Their

parallel nature makes them ideal for parallel implemen

tation techniques; however it's difficult to find optimal

network architecture for a given application. The architec

ture and network optimal parameter selection is the most

important part of the problem and is what takes a long time

to find. Genetic Algorithms (GAs) are search techniques

that used to solve difficult problems in a wide range of

disciplines. Parallel Genetic Algorithms (PGAs) are pa

rallel implementations of GAs, which can provide con

siderable gains in terms of performance and scalability.

PGAs can easily implemented on networks of heteroge

neous computers or on parallel machines like a multicore

architecture.

Dongarra [10] describe several interesting appli

cations of parallel computing. In the coming years, com

puters are likely to have even more processors inside, and

in [3] a description of multi-core processor architecture is

presented. An introduction to Multi-Objective Evolutio

naryAlgorithms can also be found in [8].

The primary goal of this research is to implement an

optimized modular neural network system for multimodal

biometric. In this paper first we describe the implemen

tation of a monolithic neural network optimized with

a PGA, and later the first stage of the modular system that

consist in a modular neural network for only one biome

tric measure, the system is optimized using a PGAmaster-

slave synchronous.

The paper is organized as follows: in the section 2 we

explain relevant concepts include in this research, section

3 defines the problem statement and the method proposed,

section 4 presents the results achieved and finally Section

5 show the conclusions and future work.

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et al.

2. Theoretical ConceptsSoft Computing consists of several computing para

digms, including fuzzy logic, neural networks and genetic

algorithms, which can be combined to create hybrid intel

ligent systems, these systems leverage the advantages of

each of the techniques involved [15]. In this research,

we use the paradigms of neural networks and genetic

algorithms.

A neural network is a computational structure capable

of discriminating and modeling nonlinear characteristics.

It consists of a set of units (usually large) of interconnec

ted simple processing, which operate together. Neural net

works have been widely used because of their versatility

for solving problems of prediction, recognition, approach

[6], [15], [12], [24].

These systems emulate, in a certain way, the human

brain. They need to learn how to behave (Learning) and

someone should be responsible for teaching (Training),

based on previous knowledge of the environment problem

[16], [26], 25].

The most important property of artificial neural net

works is their ability to learn from a training set of pat

terns, i.e. is able to find a model that fits the data [22].

2.1. Artificial Neural Networks

OPTIMIZATION OF A MODULAR NEURAL NETWORK

FOR PATTERN RECOGNITION USING PARALLEL GENETIC ALGORITHM

Martha Cárdenas, Patricia Melin, Laura Cruz


Articles 77

Areview of the physiological structures of the nervous

system in vertebrate animals reveals the existence of a re

presentation and hierarchical modular processing of the

information [18].

Modularity is the ability of a system being studied,

seen or understood as the union of several parts interacting

and working towards a common goal, each performing

a necessary task to achieve the objective [2].

According to the form in which the division of the

tasks takes place, the integration method allows to inte

grate or to combine the results given by each of the cons

tructed modules. Some of the commonly used methods of

integration are: Average, Gating Network, Fuzzy Infe

rence Systems, Mechanism of voting using softmax func

tion, the winner takes all, among others.

In Fig. 1 shows a general diagram of a Modular Neural

Network, in this model the modules work independently

and in the end a form commonly called integrator, per

forms the function of deciding between the different mo

dules to determine which of them has the best solution (in

cluding network of gateways, fuzzy integrator, etc.) [17].

John Holland introduced the Genetic Algorithm (GA)

in 1970 inspired by the process observed in the natural

evolution of living beings [26], [19]. Genetic Algorithms

(GAs) are search methods based on principles of natural

selection and genetics. A GA presents a group of possible

solutions called a population; the solutions in the popula

tion called individuals, each individual is encoded into

a string usually binary called chromosome, and symbols

forming the string are called genes. The Chromosomes

evolve through iterations called generations, in each gene

ration the individuals are evaluated using some measure

of fitness. The next generation with new individuals called

offspring, is formed from the previous generation using

two main operators, crossover and mutation, this repre

sentation is shown in Fig 2.

2.2. Modular Neural Networks

2.3. GeneticAlgorithms

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Fig. . Modular Neural Network.

Fig. . Structure of a Simple GA.

1

2

These optimization techniques are used in several

areas such as business, industry, engineering and com

puter science, also are used as a basis for industrial plan

ning, resource allocation, scheduling, decision-making,

etc. The GA is commonly used in the area of intelligent

systems, some examples of optimization of fuzzy logic

systems and neural networks are shown in [4]. GAs find

good solutions in reasonable amounts of time, however, in

some cases GAs may require hundreds or more expensive

function evaluations, and depending of the cost of each

evaluation, the time of execution of the GA may take

hours, days or months to find an acceptable solution [4],

[19].

Computers with a chip multiprocessor (CMP) give the

opportunity to solve high performance applications more

efficiently using parallel computing. However, a disad

vantage of GAs is that they can be very demanding in

terms of computation load and memory.

The Genetic Algorithms have become increasingly

popular to solve difficult problems that may require con

siderable computing power, to solve these problems deve

lopers used parallel programming techniques, the basic

idea of the parallel programs is to divide a large problem

into smaller tasks and solve simultaneously using multiple

processors. The effort for efficient algorithms has led us to

implement parallel computing; in this way it's possible to

achieve the same results in less time. However, making

a GA faster is not the only advantage that can be expected

when designing a parallel GA. A PGA has an improved

power to tackle problems that are more complex since it

can use more memory and CPU resources [1].

The way in which GAs can be parallelised depends of

several elements, like how the fitness is evaluated and

mutation is applied, if single or multiples subpopulations

(demes) are used, if multiple populations are used, how

individuals are exchanged, how selection is applied

(globally or locally).

Existing different methods for implementing parallel

GAs and can be classified in the next general classes [20]:

Master-Slave parallelisation (Distributed fitness

evaluation),

Our Implementation is based on the Master-Slave

Synchronous parallelisation, and for that reason we des

cribe only this method, other methods can be reviewed in

[4], [19].

Master-slave GAs have a single population. One mas

ter node executes the operator's selection, crossover, and

mutation, and the evaluation of fitness is distributed

among several workers (slaves) processors. The workers

evaluate the fitness of every individual that they receive

from the master and return the results.AMaster-Salve GA

depending on whether it waits to receive the fitness values

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2.4. Parallel GeneticAlgorithms

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Static subpopulation with migration,

Static overlapping subpopulations (without migration),

Massively parallel genetic algorithms,

Dynamic demes (dynamic overlapping subpopulations),

Parallel Steady-state genetic algorithms,

Parallel messy genetic algorithms,

Hybrid methods.

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VOLUME 5, N° 1 2011

memory programming and Distributed memory [5], also

the parallelism can be implemented in two ways, implicit

parallelism, that some compilers perform automatically,

these are responsible to generate the parallel code for the

parts of the program that are parallel, and the explicit pa

rallelism which is implemented using parallel languages,

and the responsible of the parallelism is the programmer,

that defines the threads to work, the code of each thread,

the communication, etc., this last parallelism gets higher

performance.

-

In this case, we focus on the parallel genetic algorithms

for optimizing the architecture of a monolithic neural net-

work and Modular Neural network for recognition of per-

sons based on the face biometry implemented in multi-

core processors.

For determining the best architecture and parameters

for a neural network there is no particular selection crite-

rion, for example the number of layers and neurons per

layer for a particular application is chosen based on expe-

rience and to find an optimal architecture for the network

becomes a task of trial and error. In addition, there are

others methods that with a empirical expression can calcu-

late and determining the architecture of neural network for

a specific problem [23].

The database used for this research is The ORL Data-

base of Faces of the Cambridge University Computer La-

boratory [9]. This database contains ten different images of

40 persons with gestures, for our implementation not apply

any preprocessing for this time, the examples pictures

shown in Fig. 4.

3. Problem Statement

Fig. . Some images of the ORL database, the database is

composed by 400 images, there are images of 40 different

persons (10 images per person).

4

for the entire population before proceeding to the next

generation can be synchronous or asynchronous. In Fig. 3

we show a Master-Slaves Synchronous GA.

The improvement in actual processors is based on the

development of Chip Multiprocessors (CMPs) or Multi-

core processors, thus to increase the efficiency of a pro

cessor, increases the number of cores inside the processor

chip.

Multi-core processors technology is the implementa

tion of two or more “execution cores” within a single pro

cessor, some of the advantages of multi-core architectures

are shown in [10], [7]. These cores are essentially two or

more individual processors on a single chip. Depending

on the design, these processors may or may not share

a large on-chip cache; the operating system perceives each

of its execution cores as a discrete logical processor with

all the associated execution resources [5].

CMPs can achieve higher performance than would be

possible using only a single core. The low inter-processor

communication latency between the cores in a CMP helps

make a much wider range of applications viable candi

dates for parallel execution. The increasing complexity of

parallel multicore processors necessitates the use of

correspondingly complex parallel algorithms.

However to exploit these architectures is necessary to

develop parallel applications that use all the processing

units simultaneously. In order to achieve parallel execu

tion in software, hardware must provide a platform that

supports the simultaneous execution of multiple threads.

Software threads of execution are running in parallel,

which means that the active threads are running simulta

neously on different hardware resources, or processing

elements. Now it is important to understand that the paral

lelism occurs at the hardware level too.

The improvement measure or speedup takes as refere

nce, the time of execution of a program in a mono-proces

sor system regarding the time of execution of the same

program in a multiprocessor or multi-core system, which

is represented as follows:

speedup , (1)

Where is the time it takes to run the program in

a mono-processor system and is the time it takes to run

the same program in a system with execution units.

There are many models of parallel programming, the

two main choices and the most common are Shared-

2.5. Chip Mpltiprocessors (CMPs)

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t

ts

p

p


Articles 79

VOLUME 5, N° 1 2011

Fig. . Execution of Parallel Master-Slaves synchronous GA.3

ts

tp

For the implementation monolithic and Modular, the

database was the same, and the same structure of chromo-

some for optimization.

Neural networks were applied to a database of 40 per-

sons, and we used 5 images per person for training and 5

images per person for test. First, we implemented the tra-

ditional monolithic neural network, and before we imple-

mented a Parallel GA for optimizing layer and neurons per

layer. The training method for the neural network is the

Trainscg (Scaled Conjugate Gradient), with an error goal

of 0.01e-006 and between 100 and 150 generations.

The GeneticAlgorithm was tested in a Multi-core com-

puter with following characteristics: CPU Intel Core 2

Quad 2.4 GHz, Bus 1066 MHz, 8MB of L2 cache, Memo-

ry 6 GBytes DDR2 of main memory, all the experiments

were achieved in the MatLab Version R2009b using the

Parallel computing toolbox.

The Master-Slave Parallel genetic Algorithm was

codified with a binary chromosome of 23 bits, 2 bits for

number of layers, and 7 bits for number of neurons per

layer. The maximum number of layers is 3 and neurons

128, this is shown in Figure 5. The proposed algorithm was

implemented in a Shared Memory Multi-core machine

with 4 cores, taking one core as master and the remaining

cores as slaves.

3.1. Monolithic Neural Network Implementation

Parallel GeneticAlgorithm for Optimization

Fig. . Chromosome representation of the problem.

Fig. . Parallel GA Implementation.

5

6

The Genetic Algorithm has the following characte-

ristics:

Chromosome Size: The number of genes in each indi-

vidual for this application is 23 binary bits.

Population size: Defines the number of individuals that

will compose the population.

Population Size =20

Termination Criteria: Maximum number of genera-

tions for solving the problem.

Max Generations=50

Selection: We used Stochastic Universal Sampling

Selection Prob=0.9

Crossover: The selected individuals have a probability

of mating, acting as parents to generate two new indivi-

duals that will represent them in the next generation.

The crossing point is random with a probability of 0.7.

Mutation: Represents the probability that an arbitrary

bit in the individual sequence will be changed from its

original stat. Mutation Probability 0.8

The flow chart of Fig. 6 shows the parallel GA imple-

mentation. Other methods for solving a Parallel GA can be

seen in [21], [11], [4].

For the modular neural network implementation, we

develop a previous stage of normalization for the database;

this stage is a parallel one like the training stage. To the ori-

ginal database we apply an algorithm for standardize the

size of the image. Depending on the database, if neces-

sary, applies this stage.

In Fig. 7, show the general diagram of the system, the

original database it's distributed in all the slaves available

for normalization to form a new database, that is the input

to the modular neural network. In the system, we have

a synchronization step that execute the master to help coor-

dinate the process in all the slaves.

When we have the inputs of the system, the PGA start

creating a random initial population in the stage of syn-

chronization, the master divides the population and send it

to the slaves (in this case the cores of processor), and the

slaves take a part of the population to evaluate.

In all the Slaves, for each individual of the GA, load the

architecture of the network, read the images and put as

input in the Module that corresponds. The images are pro-

pagate in the network and calculate the error of training.

When finish the part of population the slaves send the re-

sults of evaluation to the master. Master waits to all slaves

finish to collect the entire population and make selection

based on the fitness of all the individuals.

The Master performs the operators of crossover, muta-

tion and generates the new population to be divided and

evaluated in the slaves, until the maximum number of ge-

nerations is reached. We used the method of integration

Gating Network.

The modular architecture consists in dividing the data-

base between the modules or core processors available.

The experimental results achieved with this PGA imple-

mentation presented in the following section.

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3.2. Modular Neural Network Implementation


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VOLUME 5, N° 1 2011

4. ResultsDifferent experiments developed to observe the per-

formance of parallel genetic algorithms; the results pre-

sented in this section, the results in time represent the aver-

age time execution of each neural network in the popu-

lation of the PGA.

First, we train the monolithic neural network without

optimization, in sequential form.After manually changing

the architecture for several times, we defined the architec-

ture of the neural network with the expression of Salinas

[23] as follows:

First hidden layer (2 * ( + 2)) = 84.

Second hidden layer ( + ) = 45.

Output layer ( ) = 40.

where corresponds to the number of individuals and m to

the number of images of each of them.

Table 1 shows the average of 20 trainings in sequential

form of the network in a dual-core and quad-core machi-

nes, in this experiment we enabled only one of the cores

available and one thread of execution in the processor for

simulating sequential execution. Fig. 8 shows the usage of

a dual-core machine in the training of the network.

No. Cores Epochs Error Goal Error Total Time

4 150 1.00E-06 0.00178 1:18 min

2 150 1.00E-06 0.00468 1:14 min

4.1. Monolithic Neural Network

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k

k m

k

k

Table . Average of 20 trainings in sequential form of the

network for dual-core and quad-core machines.

Fig. . Cores Usage in the sequential training of the network.

1

8

In the experiment of training with implicit parallelism

without optimization all the cores and threads available

are enabled. Matlab R2009b uses as a default the implicit

parallelism for the applications run on it and take all the

cores for execution automatically generating a thread of

execution per processor. The results obtained for Matlab

in a dual-core and quad-core machines shown in the

Table 2.

No. Cores Epochs Error Goal Error Total Time

4 150 1.00E-06 0.00113 1:58 min

2 150 1.00E-06 0.00384 1:41 min

The results show that the execution of serial training of

the network are more efficient that the implicit paralellism

of Matlab, because when a single core is working (Fig. 9)

all the cache memory is available for them.

We optimize the monolithic Neural Network with

a simple GA in the form of implicit parallelism. Table 3

shows the average of 20 training test for 2 and 4 cores.

Figures 10 and 11 show the usage of processor in dual-

core and quad-core machines.

Table . Average of 20 trainings of implicit parallelism

form of the network for dual-core and quad-core

machines.

Fig. . Cores Usage in the implicit parallelism in training

of the network.

2

9

Implicit Parallelism with GAoptimization


Articles 81

VOLUME 5, N° 1 2011

Fig. . General Diagram of the Modular Neural Network System.7

N. Cores

2

4

Ind

20

20

Gen

30

30

Cross

0.7

0.7

Mut

0.8

0.8

Error

3.0121e-004

9.7361e-005

Time/ nework

1:51min

4:10 min

Average time

33 min

83 min

Table . Average of 20 training of implicit parallelism of Simple GA for optimization of the network with for dual-core

and quad-core machines.

3


Articles82

In this experiment, we utilize the matlab pool that en-

ables the parallel language features within the MATLAB

language by starting a parallel job, which connects this

MATLAB client with a number of labs. The average re-

sults for 20 executions are shown in table 4. Fig. 12 and 13

show the usage of the processor for training with explicit

parallelism in a dual-core and quad-core machines.

Fig. . CPU Performance Implicit Parallelism with 2 cores.

Fig. . CPU Performance Implicit Parallelism with 4 cores.

Fig. . CPU Performance Explicit Parallelism with 2 cores.

10

11

12

Explicit Parallelism with Parallel GAoptimization

Fig. . CPU Performance Explicit Parallelism with 4 cores.

Fig. . CPU Performance Explicit Parallelism with 2

cores modular neural network.

13

14

Table 5 shows a comparison between all the training

experiments, and observed that.

The results presented in this section are in Table 6 that

shows the average of 10 trainings in sequential form of the

Modular network in a dual-core and quad-core machines.

In the parallel implementation of the modular neural

network utilize the Matlab pool starting a parallel job. The

average results for 10 executions are shown in the Table 7.

Fig. 15 shows the performance of CPU for the parallel

training of a Modular Neural Network.

4.2. Modular Neural Network

VOLUME 5, N° 1 2011

N. Cores

2

4

N. Cores

2

4

No. Cores

Average time/ network

No. Cores

2

4

N. Cores

2

4

Epochs

150

150

Epochs

150

150

RNA

Modular Network Sequential

GA-RNA

PGA-Modular Network GA. Explicit Parallelism

Sequential GA. Implícit Parallelism GA. Explicit Parallelism

2

1:18 min

Time

2:05 min

2:13 min

4

1:14 min

2

1:58 min

Time

1:40 min

0:58 sec

2

1:03 min

4

1:41 min

Speedup

1.46

3.67

4

0:35 sec

Ind

20

20

Gen

30

30

Error Goal

1.00E-06

1.00E-06

Error Goal

1.00E-06

1.00E-06

Cross

0.7

0.7

Error

0.0005

0.0031

Error

0.00012

0.00025

Mut

0.8

0.8

Error

3.3121e-004

4.5354e-004

Time p/red

2:13 min

2:05min

Time p/red

0:58 min

1:40 min

Time/ nework

1:03min

0:35 sec

Time p/gen

20:11 min

20:49 min

Time p/gen

9:37 min

16:36 min

Average time

21 min

12 min

Table 8. Results of experiments Sequential and Parallel per network and the speedup.

Table . Average of 20 training of explicit parallelism of PGA for optimization of the network with for dual-core and

quad-core machines.

4

Table . Table of Results of experiments Sequential and Parallel per network.5

Table 6. Average of 10 trainings in sequential form of the modular network for dual-core and quad-core machines.

Table 7. Average of 10 training of explicit parallelism of PGA for optimization of the modular network with for dual-core

and quad-core machines.


Articles 83

Fig. . CPU Performance Explicit Parallelism with 4

cores modular neural network.

15

In Table 8 we show the results of a modular neural net-

work with a sequential training and with a parallel train-

ing, for this experiment modules of the modular network

are trained in parallel, the optimization of the network is

done in about half (dual core) or about a quarter (quad-co-

re) time, we obtain a speedup of 1.46 in a dual core machi-

ne and 3.67 in a Quad core machine. This are the first re-

sults obtained, we continue with experiments.

We have presented the experiments with training of

the monolithic and modular neural network for database

of face; we used different implementations of parallelism

to show that the parallel GA using multi-core processor

offers best results in the search for optimal neural network

architectures in less time.

The genetic algorithms take considerable time to suc

cessfully complete convergence depending of applica

tion, but most of the times achieve satisfactory optimal so-

lutions. Genetic Algorithms can be parallelized to speed-

up its execution; and if we use Explicit Parallelization we

can achieve much better speedup than when using implicit

Parallelization, anyway it's necessary to make more tests.

Multi-core computers can help us solve high perfor

mance applications in a more efficient way by using pa

rallel computation. The future work consists in conside

ring larger size databases and implementing a modular

neural network in a multi-core cluster applying different

techniques of parallel processing

Tiju

ana Institute of Technology, Tijuana, México. E mails

[email protected], [email protected]

5. Conclusions and Future Work

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.

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AUTHORS

*

References

Martha Cárdenas , Patricia Melin, Laura Cruz

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1385.

[2] C. Baldwin, K. Clark,

, Mit Press, Cambridge, 2000.

[3] T.W. Burger, Intel

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[4] E. Cantu-Paz,

, KluwerAcademic Publisher, 2001.

[5] M. Cárdenas, J. Tapia, O. Montiel, R. Sepúlveda,

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of Modularity

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rence Guide

Efficient and Accurate Parallel Genetic

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IV Regional Academic Encounter

[6] O. Castillo, P. Melin, “Hybrid intelligent systems for

time series prediction using neural networks, fuzzy logic

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[8] C.A. Coello, G.B. Lamont, D.A. Van Veldhuizen,

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Springer: Heidelberg, 2004.

[9] The Database of Faces, Cambridge University Com-

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dtg/attarchive/facedatabase.html

[10] J. Dongarra, I. Foster, G. Fox, W. Gropp, K. Kennedy,

L. Torczon, A. White,

, Morgan Kaufmann PublishersL San Francisco,

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[11] S. González,

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[12] K. Hornik, “Some new results on neural network appro-

ximation,'' , vol. 6, 1993, pp. 1069-

1072.

[13] A. Jeffrey, V. Oklobdzija,

, 2 edition,

CRC press, 1993.

[14] P. Kouchakpour, A. Zaknich, T. Bräunl,

, Elsevier Science

Inc, ISSN:0020-0255, 2007.

[15] P. Melin, O. Castillo

,

Springer, 2005.

[16] P. Melin, O. Castillo,

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[17] B. Morcego,

, PhD thesis, Uni-

versitat Politecnica de Catalunya, Barcelona, Spain,

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[18] I. Quiliano,

, February 2007, in Spanish,

http://lisisu02.usal.es/~airene/capit7.pdf

[19] M. Mitchell, ,

MIT Press, 1998.

[20] M. Nowostawski, R. Poli, “Parallel Genetic Taxo-

nomy”, In: Proceedings of the Third International Con-

ference in Knowledge-Based Intelligent Information

Engineering Systems, December 1999, pp. 88-92. DOI:

10.1109/KES.1999.820127.

[21] A. Ross, K. Nandakumar, A.K. Jainet,

, Springer 2006.

[22] P. Salazar, "Biometric recognition using techniques of

hand geometry and voice with computer vision for

feature extraction, neural networks and fuzzy logic ",

Master thesis,

, ITT, 2008, p. 57.

[23] R. Salinas,

, University of Santiago of Chile,

2000, pp. 5-9. http://cabierta.uchile.cl/revista/17/

IEEE Transactions on Neural

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IEEE International Symposium on Cluster Computing

and the Grid (CCGrid 2007).

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VOLUME 5, N° 1 2011


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[24] R. Serrano,

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[25] M. Shorlemmer, “Basic Tutorial of Neural Networks”.

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[26] M. Soto Castro,

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ana Institute of Technology, 2006.

Multicore computing applied to Genetic

Algorithms

Artificial Intelligence Research Institute

Face and Voice Recognition in real time

using Artificial Neural Networks

VOLUME 5, N° 1 2011

Abstract:

1. Introduction

The most prevalent P2P application today is file sha

ring, both among scientific users and the general public.

A fundamental process in file sharing systems is the search

mechanism. The unstructured nature of real-world large-

scale complex systems poses a challenge to the search me

thods, because global routing and directory services are

impractical to implement. This paper presents a new ant-

colony algorithm, Adaptive Neighboring-Ant Search

(AdaNAS), for the semantic query routing problem

(SQRP) in a P2P network. The proposed algorithm incor

porates an adaptive control parameter tuning technique

for runtime estimation of the time-to-live (TTL) of the ants.

AdaNAS uses three strategies that take advantage of the

local environment: learning, characterization, and explo

ration. Two classical learning rules are used to gain ex

perience on past performance using three new learning

functions based on the distance traveled and the resources

found by the ants. The experimental results show that the

AdaNAS algorithm outperforms the NAS algorithm where

the TTL value is not tuned at runtime.

-

-

-

-

-

parameter tuning, search algorithm, peer-to-

peer, adaptive algorithm, local environment, ant-colony

algorithms.

peer-to-peer

-

peers

-

Semantic Query Routing Pro-

blem -

-

Keywords:

Although popular for other uses, the World Wide Web

is impractical for user-to-user file sharing as it requires

centralized infrastructure such as an HTTP server. In the

past decade, a new class of networks called

(P2P) systems began to spread as a solution to the increa

sing demand of file sharing among Internet users. In P2P

networks, the users interconnect to offer their files to one

another [1]. The participants, called , may connect

and disconnect freely, and do so constantly, which triggers

frequent changes in the network structure [2].

One of the main advantages is that peers are equal in

terms of functionality and tasks which are developed. This

produces high fault tolerance and auto-organization: peers

form unstructured networks with an acceptable connec

tivity and performance. The

(SQRP) consists in deciding, based on a set of key

words, to which neighbor to forward the query to search

files related with the keywords [2], [3].

The lack of global structure caused that flooding-based

search mechanisms have been mainly employed. Flood

ing-based mechanisms are simple, but unfortunately

generate vast amounts of traffic in the network and may

produce congestion on Internet. Existing approaches for

SQRP in P2P networks range from simple broadcasting

techniques to sophisticated methods [1], [4], [5]. The

latter includes proposals based on [6]

that are specifically suited for handling routing tables

in telecommunications. There exist few algorithms used

for SQRP, including SemAnt [3] and Neighboring-Ant

Search (NAS) [7], the latter based on the former. In this

work we propose an algorithm as an extension to NAS,

called the Adaptive Neighboring-Ant Search (AdaNAS).

AdaNAS is hybridized with three local strategies: lear

ning, structural characterization and exploration. These

strategies are aimed to produce a greater amount of results

in a lesser amount of time. The time-to-live (TTL) para

meter is tuned at runtime based on the information acqui

red by these three local strategies.

ant-colony systems

-

-

-

-

-

-

-

2. Background

A P2P network is a distributed system that can be mo

deled mathematically as a , = ( ), where is

a set of and x is a set of (symmetrical)

. For more information on graph theory, we

recommend the textbook by Diestel [8]. Each peer in the

network is represented by a node (also called a ) of

the graph. The direct communications among the peers are

represented by the connections (also called the ) of

the graph. We denote by the number of nodes in the

system and identify the nodes by integers, = 1, 2, 3, ..., .

Two nodes that are connected are called ; the set

of all neighbors of a node is denoted by ( ). The number

of neighbors of a node is called degree and is denoted by

. Two nodes and are said to be connected if there exists

at least one sequence of connections that begins at , tra

verses from node to node through the connections of the

graph, and ends at . Such sequences of connections are

called routes or paths and the number of connections tra

versed is the length of the route.

For the purpose of analyzing the structure and behavior

of complex systems modeled as graphs, numerous charac-

terization functions have been proposed [9]. There are two

main types of these functions: those based on global infor

In order to place the research in context, this section is

divided in four parts. The first part models a P2P network

with graph theory, and in the second part we continue with

structural characterization. The third part describes the

basic ant-colony algorithms for SQRP algorithms and the

last part explains parameter tuning and adaptation.

2.1. Graph Theory

2.2. Structural Characterization using Degree

Distribution

graph G V,E V

nodes E V V

connections

vertex

edges

n

V n

neighbors

i i

i

k i j

i

j

�

�

i

ADAPTIVE ANT-COLONY ALGORITHM FOR SEMANTIC QUERY ROUTING

Claudia Gómez Santillán, Laura Cruz Reyes, Elisa Schaeffer, Eustorgio Meza, Gilberto Rivera Zarate


Articles 85

mation that require information on the entire graph simul

taneously and those based on local information that only

access the information of a certain node and its neigh

borhood

.

-

-

-

-

-

-

-

-

-

-

i

�

� �

�

( ) at a time.

The degree of a node is a local measure of network.

( ) denotes the number of nodes that have degree ,

normalized by . The measure of can be interpreted as

the probability that a randomly chosen node has degree .

The values of for [0, 1] (supposing that there

can only be at most one connection between each pair of

distinct nodes) form the of the graph.

Whereas the degrees themselves are local properties, ob

taining the degree distribution is a global computation.

The degree distribution is widely used to classify net

works according to the that produce

such distributions.Among the first and most famous gene

ration models are the of Erdös

and Rényi [10], and Gilbert [11] that yield a binomial dis

tribution that at the limit, approaches the Poisson distribu

tion and most of the nodes in the graph have similar

degrees [12].

In the past decade, another type of generation models

became popular as various studies revealed that the degree

distribution of some important real-world networks (in

cluding the WWW, the Internet, biological and social

systems) was not Poisson distribution at all, but rather

a power-law distribution [13], [14], [15], ( ) ~ with

values of typically ranging between two and three. The

models that produce such distributions are called

network models. The notable structural property in

networks with power law distribution is the presence of

a small set of extremely well-connected nodes that are

called , whereas a great majority of the nodes has

a very low degree [16], [17]. This property translates into

high fault tolerance under random flaws, but high

vulnerability under deliberate attack [14].

Metaheuristics offer solutions that are often close to

the optimum, but with a reasonable amount of resources

used when compared to an exact algorithm. Unfortuna-

tely, the metaheuristics are usually rich in parameters. The

choice of the values for the parameters is nontrivial and in

many cases the parameters should vary during the runtime

of the algorithm [18], [19].

The process of selecting the parameter values is known

as tuning. The goal of offline tuning is to provide a static

initial parameter configuration to be used throughout the

execution of the algorithm, whereas online tuning, also

known as or , is the process

of adjusting the parameter values at runtime. We design

a discrete model for adaptation based on the proposed by

Holland in 1992 [20]. We assume that the system takes

actions at discrete steps = 1, 2, 3,..., as this assumption

applies to practically all computational System. The pro

posed model is described in section four.

i

i

P k k

n

i k

k n

degree distribution

generation models

uniform random graphs

P k k

scale-

free

hubs

parameter control adaptation

t

ki

P k

P k

( )

( )

In this section we present the problem focused in this

work. First, we describe the semantic query routing pro

blem (SQRP) as a search process. Then, strategies for

solve SQRP are shown including our proposed algorithm

��

2.3. ParameterTuning andAdaptation

3. SQRP Search Strategies

which uses an adaptive strategy for adjusting an important

parameter for the search process: TTL.

SQRP is the problem of locating information in a net

work based on a query formed by keywords. The goal in

SQRPis to determine shorter routes from a node that issues

a query to those nodes of the network that can appropria

tely answer the query by providing the requested informa

tion. Each query traverses the network, moving from the

initiating node to a neighboring node and then to a neigh

bor of a neighbor and so forth, until it locates the requested

resource or gives up in its absence. Due to the complexity

of the problem [2], [3], [5], [21], [22], [23], solutions

proposed to SQRPtypically limit to special cases.

The general strategies of SQRP algorithms are the

following. Each node maintains a local database of docu

ments called the . The search mechanism is

based on nodes sending messages to the neighboring nodes

to query the contents of their repositories. The

are messages that contain keywords that describe searched

resource for possible matches. If this examination pro

duces results to the query, the node responds by creating

another message informing the node that launched the

query of the resources available in the responding node. If

there are no results or there are too few results, the node

that received the query forwards it to one or more of its

neighbors. This process is repeated until some predefined

stopping criteria is reached. An important observation is

that in a P2P network the connection pattern varies among

the net ( ), moreover the connec

tions may change in time, and this may alter the routes

available for messages to take.

The most popular technique for searching in P2P sys

tems is flooding, where each message is assigned a posi

tive integer parameter known as the (TTL) of

the message. As the message propagates from one node to

another, the value of TTL is decreased by one by each for

warding node. When TTL reaches zero, the message will

be discarded and no longer propagated in the system. The

main disadvantage of flooding is the rapid congestion of

the communication channels [24]. Another widely used

search strategy is the [21]. A random walk in

a graph is a route where the node following the initiating

node is chosen uniformly at random among its neighbors.

Wu [23] propose an algorithm called .

The main idea in the AntSearch algorithm is using phero

mone values to identify the free-riders, prevent sending

messages to those peers in order to reduce the amount of

redundant messages. The estimation of a proper TTL value

for a query flooding is based on the popularity of the re

sources. Wu use three metrics to measure the per

formance of the AntSearch. One is the

for a query with a required number of results, :

a good search algorithm should retrieve the number of

results over but close to . The second one is the

that defines the total amount of query messages divi

ded by the number of searched results; this metric measure

3.1. SQRPDescription

3.2. SQRPAlgorithms

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-

-

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r repository

queries q

heterogeneous topology

time-to-live

random walk

et al. AntSearch

et al.

number of searched

files R

R cost per

result

i

i

3.2.1. AntSearch


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VOLUME 5, N° 1 2011

rule

i, j

i

t

I

I k i

I I

query ant

Forward Ant

query ant

Forward Ant

R update ant

backward ant que

ry ant

Forward Ant

survival

rule

update ant

Forward Ant

hits hops

Forward Ant

A agents

P M O

A A A A

P

O

A

P A

A M

Forward Ant

A

A

itself. The rule evaluates the length of the shortest

known route that begins with the connection ( ) from the

current node to a node that contains good results for the

query . The form in which the algorithm operates is ex

plained in detail later in Sections 4 and 5

In this section, we present a multi-agent model in order

to describe the adaptive behavior ofAdaNAS.

The environment is the P2P network, in which two

stimuli or inputs are observed:

: the occurrences of the documents being searched,

: the degree of the node .

The environment has the following order to send sti-

muli: observing has a higher priority than observing .

AdaNAS is an ant-colony system, where each ant is mo-

deled as an agent.AdaNAS has four agent types:

The is accountable for attending the users'

queries and creating the ; moreover it

updates the pheromone table by means of evaporation.

There is a for each node in the net and it stays

there while the algorithm is running.

The uses the learning strategies for ste

ering the query and when it finds resources creates the

backward ant. It finishes the routing process when its

TTL is zero or the amount of found resources is en

ough that is denoted by then, it creates an .

The is responsible for informing to

the amount of resources in a node found by the

. In addition, it updates the values of some

learning structures that are the bases of the

which will be explaining later (Section 4.2.3).

The drops pheromone on the nodes of the

path generated by the . The amount of

pheromone deposited depends on quantity of found

resources ( ) and number of edges traveled ( )

by the .

The system is subdivided into four parts: the structures

to adapt to the environment (called ), the

adaptation plan , the memory , and the operators .

Typically, has various alternative states , , ,...

among which one is to be chosen for the system, according

to the observations made on the environment. On the other

hand, is typically a set of rules, one or more which can be

applied. These rules apply the operations in the set . An

operator is either a deterministic function, denoted as ( ,

) , or a stochastic function to a probability distri

bution over a set of states for selecting . The memory

permits the system to collect information on the condition

of the environment and the system itself, to use it as a base

for the decision making. The observations of the environ

ment are taken as stimuli that trigger the operators.

The routing process implemented in the

is required to be adaptive, thus is defined in function of

this agent. The possible states for are five:

-

.

-

-

-

-

4. AdaNAS Model

4.1. The General Model

�

�

�

�

�

�

1

2

1 2

1 2 3

i

i

j k

k

-

�

how many average query messages are generated to gain

a result. Finally, search latency is defined as the total time

taken by the algorithm.

Algorithms that incorporate information on past search

performance include the SemAnt algorithm [3], [25] that

uses an ant-colony system to solve SQRPin a P2Penviron

ment. SemAnt seeks to optimize the response to a certain

query according to the popularity of the keywords used in

the query. The algorithm takes into account network para

meters such as bandwidth and latency. In SemAnt, the que

ries are the ants that operate in parallel and place phero

mone on successful search routes. This pheromone evapo

rates over time to gradually eliminate old or obsolete infor

mation. Also Michlmayr [3] considers parameter tuning

for the SemAnt algorithm, including manual adjustment of

the TTL parameter from a set of possible values 15, 20, 25,

30, 35 and concludes that 25 is the best value for the para

meter. The adjustment of the TTL is made without simul

taneous tuning of the other parameters.

NAS [7] is also an ant-colony system, but incorporates

a local structural measure to guide the ants towards nodes

that have better connectivity. The algorithm has three main

phases: an evaluation phase that examines the local repo

sitory and incorporates the classical lookahead technique

[4], a transition phase in which the query propagates in the

network until its TTL is reached, and a retrieval phase in

which the pheromone tables are updated.

Most relevant aspects of former works have been in

corporated into the proposed NAS algorithm. The frame

work of AntNet algorithm is modified to correspond to the

problem conditions: in AntNet the final addresses are

known, while NAS algorithm does not has a priori know

ledge of where the resources are located. On the other

hand, differently to AntSearch, the SemAnt algorithm and

NAS are focused on the same problem conditions, and

both use algorithms based onAntNet algorithm.

However, the difference between the SemAnt and NAS

is that SemAnt only learns from past experience, whereas

NAS takes advantage of the local environment. This

means that the search in NAS takes place in terms of the

classic local exploration method of Lookahead [4], the

local structural metric DDC[26] which measures the diffe

rences between the degree of a node and the degree of its

neighbors, and three local functions of the past algorithm

performance. This algorithm outperforms methods propo

sed in the literature, such as Random-Walk and SemAnt

[7].

The proposed algorithm in this work,

(AdaNAS) is largely based on the NAS

algorithm, but includes the adaptation of the TTL parame

ter at runtime, in addition to other changes. The mecha

nism that may extend the TTL for an ant is called the

. It incorporates information on past queries relying

on the learning strategies included in AdaNAS, basic cha

racteristics of SQRP and a set of parameters that are adjus

ted according to the results found when using the

3.2.2. SemAnt

3.2.3. Neighboring-Ant Search

3.2.4. Adaptative Neighboring-Ant Search

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Adaptive Neigh

boring Ant Search

survi

val rule

survival

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Articles 87

VOLUME 5, N° 1 2011

A Forward Ant

query ant

A

A

A Forward Ant

A X Forward Ant

I I

I I

M

i j

t

concepts C

M n

t

i

C i

M D M N M H

n

i t

D j

N H

Fig. 1. AdaNAS Adaptive Model General.

P

M M M

P

M Forward Ant survi

val rule

P

M D

P

1

2

3

4

5

1 2

1 2

1

2 3 4

1

2 3 4

2

1

3

2

4

: No route has been assigned and the is

at the initial node. The ant can be only activated

when the send it a query and can only

receive once time each stimulus.

: Aroute has been assigned and TTL has not reached

zero.

: TTLis zero.

: used the survival rule to extend

TTL.

= : Terminal state is reached by the .

The Figure 1 shows the AdaNAS adaptive model.

According to the stimuli -the number of documents found

(dotted line, ) and degree of the node (solid line, ) - an

operator is selected. The line style for state transitions

follows that of the stimuli: dotted line for transitions pro

ceeding from and solid for .

The memory is basically composed of four struc

tures that store information about previous queries. The

first of these structures is the three dimensional phero

mone table . The element is the preference for mo

ving from node to a neighboring node when searching

by a keyword . In this work, we assume that each query

contains one keyword and the total number of keywords

(or ) known to the system is denoted by .

The pheromone table = is split into bi-dimen

sional tables, j, , one for each node. These tables only

contain the entries for a fixed node and hence have at

most dimensions x ( ) . The other three structures are

also three-dimensional tables = , = and = ,

each splits into local bi-dimensional tables in the same

manner. The information in these structures is of the follo

wing kind: currently being at node and searching for ,

there is a route of distance starting at the neighbor

that leads to a node identified in that contains hits

or matching documents.

The adaptive plans Pare the following:

: Created when a resource is

found; modifies , and .

: Modifies the pheromone table

= when the reached 0 and -

can not to proceed.

: Selects the next node apply-

ing the inherent learning stored in pheromone trails

and in the memory structure = .

: Proceeds when the learning

-

-

-

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-

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i,j,t

j,t

i,j,t

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The backward ant.

The update ant.

The transition rule.

The survival rule.

stored in , and permits to extend TTL and

determines how much TTLmust be extended.

: A variation of tran-

sition rule that eliminates the pheromone and de-

gree effects.

The operators ofAdaNAS are the following:

: ( , ) :

The ant has been just created and documents were found

from the initial node ( ), so updates the

short-time memory ( ) and no change in the agent state of

the system is produced.

: ( , ) :

The ant has been just created and must select a neighbor

node ( ) according with the transition rule ( ), this will

produce a tracked route by the ant ( ).

: ( , ) :

The ant has assigned a route ( ) and documents were

found from initial node ( ), so updates the

short-time memory ( ) and no change in the agent state of

the system is produced.

: ( , ) | , :

When the ant has a partial route ( ), it must select the next

node from the neighborhood ( ), so it applies the

transition rule ( ). The application of the transition rule

can cause than TTLis over ( ) or not ( ).

: ( , )

Idem , but now the ant has TTL= 0 ( ).

: ( , )

The ant is over TTL ( ) and with neighborhood

information ( ), so it applies the survival rule ( ). When

is applied the ant performs its activity in an extended

time to live ( ).

: ( , ) X

The ant is over TTL ( ) and with neighborhood

information ( ), so it decides that the path must end. In

order to reach its final state ( ), the must

create an which performs the pheromone

update ( ).

: ( , )

Idem , but now the ant performs it activity in an

extended time to live ( ).

: ( , ) | ,

In an extended TTL by the modified transition rule ( ),

the ant must choose a vertex from the neighborhood ( )

like the next node in the route. The application of

transition rule can cause than TTL is over ( ) or not ( )

again.

The general model is illustrated in Figure 1 where can

be observed the transitions among states of the

.

An ant-colony algorithm has rules that determine its

behavior. These rules define why the ants construct and

evaluate the solution and why the pheromone is updated

and used. Although the pheromone is the main learning

structure, AdaNAS has three more: , and , for know

the distances toward the nodes that contain in its reposi

tory matching documents. AdaNAS own several behavior

rules: the , the , the

M M M

P

O

O A I P A

I backward ant

P

O A I P A

I P

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A

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P

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updating ant

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transition rule update rules survival

2 3 4

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The modified transition rule.

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VOLUME 5, N° 1 2011

rule transition rule

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p q

r r x

x

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p

x r i t

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p q

and the modified .

The transition rule considers two structures to deter-

mine the next state: and . This transition rule is used by

an ant that is searching the keyword and is located in the

node . The rule is formulates in the following Equation 1:

(1)

where is a pseudo-random number, is a algorithm para-

meter that defines the probability of using of the exploita-

tion technique, ( ) is the set of neighbors nodes of , is

the set of nodes previously visited by , and Equation 2,

defined by:

(2)

where is the parameter that defines the degree impor-

tance, defines the distance importance toward the near-

est node with matching documents ( ), intensifies the

local metrics contribution (degree and distance), inten-

sifies pheromone contribution ( ), ( ) is a normali-

zed degree measure expressed in Equation 3:

(3)

and is the exploration technique expressed, in Equation 4:

(4)

where ( i ( ) ) is a roulette-wheel random

selection function that chooses a node i depending on its

probability which indicates the probability of the ant

for moving from to searching by keyword and it is

defined in Equation 5:

(5)

The tables and were described in the previous

section. The exploration strategy is activated when

and stimulates the ants to search for new paths. In case that

, the exploitation strategy is selected: it prefers nodes

that provide a greater amount of pheromone and better

connectivity with smaller numbers of hops toward a re-

source. As is shown in the transition rule, is the inten-

sifier of the pheromone trail, and is the intensifier of the

local metrics, this means that the algorithm will be only

steered by the local metrics when = 0, or by the phero-

mone when = 0. In this work the initial values are = 2

and = 1.

There are two basic update rules in an ant colony algo-

rithm: the evaporation and increment of pheromone. The

evaporation method of AdaNAS is based on the technique

4.2.1. Transition Rule

4.2.2. Update Rules

3

�

�

�

�

� �

� �

�

�

�

�

�

�

� �

�

d

i

r,i,t

r,i,t

x,r,i,t

x,r,i,t

1

2

2

1

2

1 1

2

{ }

used in SemAnt [3], while the increment strategy is based

on the proposed in NAS [7]. Both update rules are

described below.

Pheromone Evaporation Rule, the pheromone evapo-

ration is a strategy whose finality is avoid that the edges

can take very big values of pheromone trail causing

a greedy behavior on the algorithm. Each unit time the

query ant makes smaller the pheromone trail of the node

where the query ant is, by multiplying the trail by the eva-

poration rate , which is a number between zero and one.

To avoid very low values in the pheromone the rule incor-

porates a second term consisting of the product , where

is the initial pheromone value. The Equation 6 expres-

ses mathematically the evaporation pheromone rule.

(6)

, when a

finishes, it must express its performance in terms of phero-

mone by means of an whose function is to

increase the quantity of pheromone depending on amount

of documents found and edges traversed by .

This is done each time that an passes on one

node. The Equations 7 and 8 describe the -

.

(7)

where is the preference of going to when the Forward

Ant is in and is searching by keyword , ( ) is the

amount of pheromone dropped on by a

generated by the and can be expressed like:

(8)

where ( ) is the amount of documents found by the

from to end of its path, and ( ) is

the length of the trajectory traversed by the

from to the final node in its route passing by .

(the backward ant) updates the memory structures

= , = , and = . These structures are used in

the survival rule ( ) to increase time to live. This survival

rule can be only applied when TTL is zero. The survival

rule can be expressed mathematically in terms of the

structures , and as see in Equation 9:

(9)

where ÄTTL( ) is the increment assigned to the TTL

of ant (that is, number of additional steps that the ant will

be allowed to take) when searching for resources that

match to , currently being at node . The number of addi-

tional steps for arriving in the node ( ) is

determined from the shortest paths generated by previous

ants, and is taken when its associated efficiency ( ) is

better than which is a measure of current performance

�

��

�

�

��

�

�

�

0

0

Pheromone Increment Rule Forward Ant

update ant

Forward Ant

update ant

pheromone in

crement rule

s

r t x

backward ant

Forward Ant x

hits x, s

Forward Ant x s hops x, r

Forward Ant x

r s

P

M D M N M H

P

H D N

x, i, t

x

t i

D x, i, t

x, i, t

Zx

r,s,t

r,s,t

r,s,t

4.2.3. Survival Rules

1

2 3 4

4

i, (x, i, t), t�


Articles 89

VOLUME 5, N° 1 2011


Articles90

of the ant . The auxiliary functions are shown in Equa-

tions10 and 11:

(10)

(11)

where ( ) is the set of neighbors of node i and is the set

of nodes previously visited by the ant . The tables of hits

, of distances , and of nodes were explained in the

previous section. The function ( ) determines which

node that is neighbor of the current node and that has not

yet been visited has previously produced the best efficien-

cy in serving a query on , where the efficiency is measu-

red by ( ).

The is a special case of -

(see Equations 4 and 5) where = 0, = 0 and

= 1. This rule is greedy and provokes the replication of

paths generated by previous ants. This rule takes place

when TTL has been extended canceling the normal t -

. Mathematically can be express in Equations 12

and 13, like:

(12)

where is the , is the current

node in the path, is the searched keyword, is the set of

nodes visited by the and

(13)

where is a parameter that defines the influence of

that is the needed distance for arriving in the known near-

est node with documents with keyword , from passing

by and is the distance intensifier.

AdaNAS is a metaheuristic algorithm, where a set of

independent agents called ants cooperate indirectly and

sporadically to achieve a common goal. The algorithm has

two objectives: it seeks to maximize the number of resour-

ces found by the ants and to minimize the number of steps

taken by the ants. AdaNAS guides the queries toward

nodes that have better connectivity using the local structu-

ral metric degree [26]; in addition, it uses the well known

technique [25], which, by means of data struc-

tures, allows knowing the repository of the neighboring

nodes of a specific node.

The AdaNAS algorithm performs in parallel all the

queries using query ants. The process done by is

represented in Algorithm 1. Each node has only a query

ant, which generates a for attending only

one user query, assigning the searched keyword to the

. Moreover, the realize periodically

the local pheromone evaporation of the node where it is.

In theAlgorithm 2 is shown the process realized by the

, as can be observed all Forward Ants act in

parallel. In an initial phase (lines 4-8), the ant checks the

local repository, and if it founds matching documents then

x

i x

x

H D N

x, i, t

i

t

x, i, t

modified transition rule tran

sition rule W

q

ransi

tion rule

l modified transition rule r

t x

Forward Ant x

w D

t r

i

lookahead

query ant

Forward Ant x

t

Forward Ant query ants

Forward Ant

�

�

�

�

�

4.2.4. Modified Transition Rule

2

1

d

m

i r,i,t

5. AdaNASAlgorithm

creates a .Afterwards, it realizes the search

process (lines 9-25) while it has live and has not found

documents.

The search process has three sections: Evaluation of

results, evaluation and application of the extension of

TTLand selection of next node (lines 24-28).

: Query ant algorithm

create ForwardAnt ( )

x

pheromone evaporation

, the evaluation of results (lines 10-15)

implements the classical Lookahead technique. That is,

the ant located in a node , checks the lookahead struc-

ture, that indicates how many matching documents are in

each neighbor node of . This function needs three para-

meters: the current node ( ), the keyword ( ) and the set of

known nodes ( ) by the ant. The set indicates

what nodes the lookahead function should ignore, because

their matching documents have already taken into ac-

count. If some resource is found, the creates

a and updates the quantity of found mat-

ching documents.

(lines 16-23) is evaluation and

application of the extension of TTL. In this section the ant

verifies if TTL reaches zero, if it is true, the ant intends to

extend its life, if it can do it, it changes the normal -

modifying some parameters (line 21) in order to

create the .

(lines 24-30) of the search process

phase is the selection of the next node. Here, the

(normal or modified) is applied for selecting the next

node and some structures are updated. The final phase

occurs when the search process finishes; then, the -

creates for doing the pheromone

update.

The Algorithm 3 presents the parallel behavior for

each which inversely traverses the path

given by the . In each node that it visits, it

tries to update the structures and , which will be

used for future queries (lines 7-11). The update is realized

if the new values point to a nearer node (line 7). After that,

it informs to of the initial node of the path how

many documents the found and which path

used (line 13).

The Algorithm 4 presents the concurrent behavior for

each which inversely traverses the path given

by the . In each node that it visits, it updates

the pheromone trail using the Equation 6 (line 5).

In this section, we describe the experiments we carried

during the comparisons of the AdaNAS and NAS

algorithms.

backward ant y

R

query ant w located in the node r

the system is running

the user queries to find R documents with

keyword t

x r,t,R

x r

r

r t

known known

Forward Ant

backward ant

transi

tion rule

modified transition rule

transition

rule

r

For

ward Ant an update ant z

backward ant

Forward Ant

D, H N

ant query

Forward Ant

update ant

Forward Ant

Algorithm 1

1 in parallel for each

2 While do

3 if

then

4

5 activate

6 End

7 apply

8 End

9 end of in parallel

The first section

The second section

The third section

6. Experiments

VOLUME 5, N° 1 2011


Articles 91

Algorithm 2:

1 in parallel for each ( )

2 initialization:

3 initialization:

4

5 if then

6

7 activate

8 End

9 while do

10 = look ahead( )

11 if > 0 then

12 create backward ant y( )

13 activate y

14

15 End

16 if > 0 then

17 = 1

18 Else

19 if ( < ) ( ( )

> 0) then

20 = ( )

21 change parameters:

22 End

23 End

24

25 [ ( ( ))

25

27

28

29 End

30

31 activate

32 kill


6.1. Generation of the test data

Algorithm 3:

1 initialization:


3 for do

4

Forward ant algorithm

( )

> 0

create backward ant y( )

y

< 0 <

=1, =0, =0

+ 1

=

= ( )

add to path( )

create update ant ( )

z

x

A SQRP instance is formed by three separate files:

topology, repositories, and queries. We generated the

experimental instances following largely those of NAS

reported by Cruz [7] in order to achieve comparable

results. The structure of the environment in which is

carried out the process described is called , and

refers to the pattern of connections that form the nodes on

the network. The generation of the topology ( ) was based

on the method of Barabási [27] to create a scale-free

network. We created topologies with 1024 nodes; the

number of nodes was selected based on recommendations

in the literature [3], [28].

The ( ) of each node was generated

using “topics” obtained from ACM Computing Classi

fication System taxonomy (ACMCCS). This database

contains a total of 910 distinct topics. Also the content are

scale-free: the nodes contain many documents in their

repositories on the same topic (identified by keywords)

and only few documents on other topics.

Backward ant algorithm

= 0

( )

= 1 to 1

=

ForwardAnt x r,t,R

La_ results r,t,known

la results

path, la results, t

results results + la results

TTL

TTL TTL

results R and TTL x, results, hops

TTL TTL+ TTL x, results, hops

Known= known r r

TTL = TTLmax, hops= 0

path=r, =r, known=r

Results = get_ local_ documents r

results

path, results, t

TTL and results R

q W

Hops= hops

r

r l x,r,t

r

z x, path, t

et al.

topology

T

et al.

local repository R

hops

backward ant y path, results, t

I path

R path

�

�

�

�

� � �

�

�

d 2

-

( 1)i �

5

6

7 if then

8

9

10

11 End

12 End

13 Send

14 kill


Algorithm 4:


2 for do

3

4

5

6 End

7 kill


Average hops

Average hits

Average efficiency

6.2. Parameters

6.3. Results

s path

hops hops

D

H

N

results, path path1

update ant z path, t, x

i path

R path

s path

x

queries Q

Q

=

= + 1

Dr, s, t > hops

= hops

= result

= path

( ) to the query ant located in

y

Update ant algorithm

( )

= 1 to 1

=

=

= + ( )

z

For the generation of the ( ), each node was

assigned a list of possible topics to search. This list is limi-

ted by the total amount of topics of the ACMCCS. During

each step of the experiment, each node has a probability of

0.1 to launch a query, selecting the topic uniformly at ran-

dom within the list of possible topics of the node reposi-

tory. The probability distribution of determines how

often the query will be repeated in the network. When the

distribution is uniform, each query is duplicated 100 times

in average.

The topology and the repositories were created static,

whereas the queries were launched randomly during the

simulation. Each simulation was run for 15,000 queries

during 500 time units, each unit has 100 ms. The average

performance was studied by computing three performan-

ce measures of each 100 queries:

, defined as the average amount of links

traveled by a ForwardAnt until its death, that is, reach-

ing either the maximum amount of results required R

or running out of TTL.

, defined as the average number of re-

sources found by each ForwardAnt until its death.

, defined as the average of resour-

ces found per traversed edge (hits/hops).

The configuration of the algorithms used in the experi-

mentation is shown in Tables 1 and 2. The first column is

the parameter, the second column is the parameter value

and the third column is a description of the parameter.

These parameter values were based on recommendations

of the literature [3], [6], [7], [29], [30].

The goal of the experiments was to examine the effect

of the strategies incorporated in the AdaNAS algorithm

and determine whether there is a significant contribution

to the average efficiency. The main objective of SQRP is

to find a set of paths among the nodes launching the que-

i

r, s, t

r, s, t

r, s, t h

i

i

r, s, t r, s, t r, s, t

�

� � ��

( 1)�

�

�

�

VOLUME 5, N° 1 2011


Articles92

ries and the nodes containing the resources, such that the

efficiency is greater, this is, the quantity of found resour-

ces is maximized and the quantity of steps given to find the

resources is minimized.

Figure 2 shows the performed during

15,000 queries with AdaNAS and NAS algorithms on an

example instance. NAS starts off approximately at 13.4

hits per query; at the end, the average hit increases to 14.7

hits per query. ForAdaNAS the average hit starts at 16 and

after 15,000 queries the average hit ends at 18.3. On the

other hand, Figure 3 shows the performed

during a set of queries with NAS andAdaNAS. NAS starts

approximately at 17.4 hops per query; at the end, the aver-

age hops decrease to 15.7 hops per query. ForAdaNAS the

average hops starts at 13.7 and after 15, 000 queries the

average hops ends at 9.1. Finally, Figure 4 shows the -

performed during a set of queries. NAS

starts approximately at 0.76 hits per hop; at the end, it

increases to 0.93 hits per hop. For AdaNAS the average

efficiency starts at 1.17 hits per hop and after 15, 000 que-

average hits

average hops

aver

age efficiency

ries the average efficiency ends at 2.

The adaptive strategies of AdaNAS show an incre-

ment of 24.5% of found documents, but the biggest contri-

bution is a reduction of hops in 40%, giving efficiency

approximately twice better on the final performance of

NAS. This observation suggests that the use of degree in-

stead of DDC was profitable. In addition, the incorpora-

tion of the survival rule permits to improve the efficiency,

because it guides the Forward Ants to nodes that can

satisfy the query. Moreover, in future works it will be im-

portant to study adaptive strategies for other parameters as

well as the initial algorithm parameter configuration in

search of further improvement in the efficiency.

Figure 5 shows the results of the different experiments

applied to NAS and AdaNAS on thirty runnings for each

ninety different instances generated with the characte

ristics described in Section 6.1. It can been seen from it

that on all the instances the AdaNAS algorithm outper

forms NAS. On average, AdaNAS had efficiency 81%

better than NAS.

-

-

VOLUME 5, N° 1 2011

Table 1. Parameter configuration of the NAS algorithm.

PARAMETER

�

�

�

�0

0q

R

TTL

Wmax

PARAMETER

�

�

�1

2

0

0

T

q

R

TTL

w

w

w

max

h

d

i

VALUE

0.07

0.07

2

0.009

0.9

10

10

0.5

VALUE

00.07

2

1

0.009

0.9

10

10

0.5

1

1

Global pheromone evaporation factor

Local pheromone evaporation factor

Intensifier of pheromone trail

Pheromone table initialization

Relative importance between exploration and exploitation

Maximum number of results to retrieve

Initial TTL of the Forward Ants

Relative importance of the resources found and TTL

Local pheromone evaporation factor

Intensification of local measurements (degree and distance) in transition rule.

Intensification of pheromone trail in the in the transition rule.

Pheromone table initialization

Relative importance between exploration and Exploitation in the transition rule.

Maximum number of results to retrieve

Initial TTL of the Forward Ants

Relative importance of the hits and hops in the increment rule

Degree's influence in the transition rule

Distance's influence in the transition rule

DEFINITION

DEFINITION

Table 2, Parameter configuration of the AdaNAS algorithm.

Fig. 2. Learning evolution in terms of the number of resources found for AdaNAS and NAS algorithms.


Articles 93

7. ConclusionsFor the solution of SQRP, we proposed a novel algo-

rithm called AdaNAS that is based on existing ant-colony

algorithms, which is a state of art algorithm. AdaNAS

algorithm incorporates parameters adaptive control tech-

niques to estimate a proper TTL value for dynamic text

query routing.

In addition, it incorporates local strategies that take

advantage of the environment on local level; three func-

tions were used to learn from past performance. This com-

bination resulted in a lower hop count and an improved hit

count, outperforming the NAS algorithm. Our experi-

ments confirmed that the proposed techniques are more

effective at improving search efficiency. Specifically the

AdaNAS algorithm in the efficiency showed an improve-

ment of the 81% in the performance efficiency over the

NAS algorithm.

As future work, we plan to study more profoundly the

relation among SQRP characteristics, the configuration of

the algorithm and the local environment strategies emplo-

yed in the learning curve of ant-colony algorithms, as well

as their effect on the performance of hop and hit count

measures.

ACKNOWLEDGMENTS

This research was supported in part by CONACYT, DGEST and

IPN.

VOLUME 5, N° 1 2011

Fig. 3. Learning evolution in terms of the length of the route taken for AdaNAS and NAS algorithms.

Fig. 4. Learning evolution in terms of the efficiency (hits/ hop) for AdaNAS and NAS algorithms.

Fig. 5. Comparison between NAS and AdaNAS experi menting with 90 instances.-


Articles94

AUTHORS

References

Claudia Gómez Santillán

Laura Cruz Reyes*, Gilberto Rivera Zarate

Eustorgio Meza, Elisa Schaeffer

-

-

.

-

.


Instituto Tecnológico de

Ciudad Madero (ITCM). 1ro. de Mayo y Sor Juana I. de la

Cruz s/n CP. 89440, Tamaulipas, México. Tel.: (52) 833

3574820 Ext. 3024 and Instituto Politécnico Nacional,

Centro de Investigación en CienciaAplicada y Tecnología

Avanzada (IPN-CICATA). Carretera Tampico-Puerto

Industrial Alt., Km.14.5. Altamira, Tamps., México. Tel.:

018332600124. E-mail: [email protected].

Instituto

Tecnológico de Ciudad Madero (ITCM). 1ro. de Mayo y

Sor Juana I. de la Cruz s/n CP. 89440, Tamaulipas,

México. Tel.: (52) 833 3574820 Ext. 3024. E-mails:

[email protected] and [email protected]

CIIDIT & FIME,

Universidad Autónoma de Nuevo León (UANL), Av.

Universidad s/n, Cd. Universitaria, CP.66450, San Nicolás

de los Garza, N.L., México. Phone: (52)8113404000

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VOLUME 5, N° 1 2011

Abstract:

1. Introduction

2.

In this paper, we propose a new optimization algo-

rithm for soft computing problems, which is inspired on

a nature paradigm: the reaction methods existing on che-

mistry, and the way the elements combine with each other

to form compounds, in other words, quantum chemistry.

This paper is the first approach for the proposed method,

and it presents the background, main ideas, desired goals

and preliminary results in optimization.

Keywords: natural computing, novel optimization me-

thod, chemical reactions paradigm.

Several works have proved the relevance of compu-

ting techniques to solve diverse kinds of problems, inclu-

ding forecasting, control and pattern recognition among

others [1], [2], [3].

These techniques not only comply with their objec-

tive, but they also pro mote the creation of new ways to

give solutions and improve the actual methods as well

[4],[5], [6].

One of the main difficulties when designing the struc-

ture of a solution method is the tuning of the parameters;

which are the key to the success of these applications. The-

se parameters will vary depending on the complexity of

the problem and the method used to find the solution; and

in some cases, they stem from our own ability to conceptu-

alize the problem itself, taking in account, the inputs of the

system and the expected output values.

Due to these facts, several optimization strategies ba-

sed on nature paradigms have arisen. From Ant Colony

Optimization, to Particle Swarm Optimization among

others, these strategies had emerged as an alternative way

to solve problems [7], [8], [9], [10], [11], [12], [13].

For this work, we will be observing the process in

which the different elements existing in nature are created,

behave and interact with each other to form chemical

compounds.

The structure of this paper is the following. Section 2

shows a brief description of the chemical method that

inspired this investigation; section 3 describes the pro-

posed method and first approach; section 4 shows the

preliminary experiment results; in section 5 we describe

the current and future work and section 6 shows some

references.

Chemical ParadigmIn order to have a better understanding of the process

that we intend to model, we present some general defi-

nitions [14],[15].

Chemistry is the study of matter and energy and the

interaction between them, including the composition, the

properties, the structure, the changes which it undergoes,

and the laws governing those changes. A substance is

a form of matter that has a defined composition and cha-

racteristic properties. There are two kinds of substances:

elements and compounds.

An element is a substance that cannot be broken down

into simpler substances by ordinary means. It is apparent

from the wide variety of different materials in the world

that there are a great many ways to combine elements.

Compounds are substances formed by two or more

elements combined in definite proportions through a che-

mical reaction. There are millions of known compounds,

and thousands of new ones are discovered or synthesized

each year.

A chemical reaction is a change in which at least one

substance changes its composition and its sets of proper-

ties; they are classified into 4 types.

Acombination reaction is a reaction of two reactants to

produce one product. The simplest combination reactions

are the reactions of two elements to form a compound.

After all, if two elements are treated with each other, they

can either react or not.

The second type of simple reaction is decomposition.

This reaction is also easy to recognize. Typically, only one

reactant is given. A type of energy, such as heat or electri-

city, may also be indicated. The reactant usually decompo-

ses to its elements, to an element and a simpler compound,

or to two simpler compounds.

Binary compounds may yield two elements or an ele-

ment and a simpler compound. Ternary (three-element)

compounds may yield an element and a compound or two

simpler compounds. These possibilities are shown in the

Figure 1.

Type 1: combination reactions: (B+C BC).

Type 2: decomposition reactions: (BC B+C).

�

�

Fig. . Decomposition possibilities.1

A NEW OPTIMIZATION ALGORITHM BASED ON

A PARADIGM INSPIRED BY NATURE

Leslie Astudillo, Patricia Melin, Oscar Castillo


Articles 95

Type 3: substitution reactions: (C +AB AC + B).

Type 4: double-substitution reactions: (AB + CD CB

+AD).

�

�

Elements have varying abilities to combine. Among

the most reactive metals are the alkali metals and the

alkaline earth metals. On the opposite end of the scale of

reactivities, among the least active metals or the most

stable metals are silver and gold, prized for their lack of

reactivity. Reactive means the opposite of stable, but

means the same as active.

When a free element reacts with a compound of diffe-

rent elements, the free element will replace one of the

elements in the compound if the free element is more reac-

tive than the element it replaces. In general, a free metal

will replace the metal in the compound, or a free nonmetal

will replace the nonmetal in the compound. A new com-

pound and a new free element are produced.

Double-substitution or double-replacement reactions,

also called double-decomposition reactions or metathesis

reactions, involve two ionic compounds, most often in

aqueous solution. In this type of reaction, the cations sim-

ply swap anions. The reaction proceeds if a solid or a cova-

lent compound is formed from ions in solution. All gases

at room temperature are covalent. Some reactions of ionic

solids plus ions in solution also occur. Otherwise, no reac-

tion takes place.

Just as with replacement reactions, double-replace-

ment reactions may or may not proceed. They need a dri-

ving force. In replacement reactions the driving force is

reactivity; here it is insolubility or co-valence.

3.

4.

Modeling the Chemical Paradigm

Preliminary experimental resultsFigure 2 shows the De Jong’s first function also called

the sphere model, which is continuous, convex, unimodal

Now that we have described the natural paradigm that

we intent to mimic, the next step is to define the general

structure of our optimization algorithm; which, initially

will be developed in 5 phases: a combination algorithm,

a decomposition algorithm, a substitution algorithm,

a double-substitution algorithm and the final algorithm,

which will be the combination of all the previous four.

The steps to consider in this optimization method will

be as follows:

1. First, we need to generate an initial pool of elements/

compounds.

2. Once we have the initial pool, we have to evaluate it.

3. Based on the previous evaluation, we will select some

elements/compounds to “induce” a reaction.

4. Given the result of the reaction, we will evaluate the

obtained elements/compounds.

5. Repeat the steps until the algorithm meets the criteria

(desired result or maximum number of iterations is

reached).

In order to start testing the phases of the algorithm, we

will be applying these to the following (but not restricted

to) functions: De Jong's and Rosenberg's functions [16],

[12].

and is represented by the equation:

(1)

The domain is given by:

(2)

And has a global minimum represented by:

( ) = 0; ( ) = 0; = 0 : . (3)

The fist approach to solve this equation is given by

applying a minimization algorithm based on the decom-

position reactions.

The main idea of this particular algorithm is, given

a random set of initial numbers, decompose each one into

smaller numbers in a way that can be represented by a bi-

nary tree, where each ensuing node will be decomposed as

well into smaller numbers, to lead the result into the mini-

mum of the function.

To start from the simplest option, in these early experi-

ments all decomposed elements are considered to have the

same value, and they are given by:

Decomposed_Element( )=Element/ (4)

where is the element index and is the number of de-

composed elements generated. Because the resulting va-

lues are the same for each decomposed element, only one

will be selected to be evaluated in the function.

Let’s consider an initial pool of 5 elements (randomly

generated); each one will be decomposed in 3 sub ele-

ments throughout 10 iterations. Table 1 shows the final

and average values of the best and worst result reached by

the algorithm throughout 30 experiments.

f x x i i n

Fig. 2. De Jong's First function in 2D.

n m

n m

Table 1. Worst and best results throughout 30 experiments

evaluating the first De Jong’s Function.


Articles96

VOLUME 5, N° 1 2011

Experiment

number

1

13

Minimum

Value

3.799e-14

7.829e-09

Average

value

1.656e-06

0.341

Comments

Best result

Worst result

Fig. 5. Plot of the average and standard deviations per

iteration in 30 experiments evaluating the De Jong’s first

function: the sphere model.

Fig. 6. The weighted sphere model in 2D.

f x x i i n


evaluating the first De Jong’s Function.

Figure 6 shows the axis parallel hyper-ellipsoid, also

known as the weighted sphere model. It a continuous,

convex and unimodal function and is represented by the

equation:

(5)

The domain is given by:

(6)


( ) = 0; ( ) = 0; = 0 : . (7)

The Table 3 shows the final and average values of the

best and worst result reached by the algorithm throughout

30 experiments.

Figure 7 shows the minimized values trough the 10

iterations of experiment number 28, which reached the

minimum value overall the 30 experiments.


iterations of experiment number 1, which reached the mi-

nimum value overall the 30 experiments.

In Figure 4 we can see the behavior of the algorithm

along the 30 experiments, where every experiment is

represented by “Cycles” of 10 iterations each.

Table 2 shows the standard deviation calculated by

iteration throughout 30 experiments.

Figure 5 shows the plot of the average and standard

deviations calculated per iteration in 30 experiments.

Fig. 3. Minimum value reached in experiment no. 1.

Fig. 4. Minimum values reached 30 experiments.

Table 2. Standard deviation per trial in 30 experiments

evaluating the first De Jong’s Function


Articles 97

VOLUME 5, N° 1 2011

Trial

1

2

3

4

5

6

7

8

9

10

Standard Deviation

0.769404299

0.085489367

0.009498819

0.001055424

0.000117269

1.30E-05

1.45E-06

1.61E-07

1.79E-08

1.99E-09

Experiment

number

28

23

Minimum

Value

8.85E-17

7.91-13

Average

value

7.00-05

0.62

Comments

Best result

Worst result

Fig. 7. Minimum value reached in experiment no. 28,

evaluating the weighted sphere model.

Fig. 8. Minimum values reached 30 experiments evalua-

ting weighted sphere model.




along the 30 experiments, where every experiment is re-

presented by “Cycles” of 10 iterations each.

Table 4 shows the standard deviation calculated by ite-

ration throughout 30 experiments.


deviations calculated per iteration in 30 experiments,



iteration in 30 experiments evaluating the weighted

sphere model.

Fig. 10. The Rosembrock’s Valley in 2D.

f x x i i n


evaluating the Rosembrock’s Valley Function.

Figure 10 shows the Rosembrock’s Valley function,

also known as banana function or the second function of

De Jong. The global optimum lies inside a long, narrow,

parabolic shaped flat valley. It is represented by the

equation:

(8)

The test area is usually restricted to hypercube:

(9)


( ) = 0; ( ) = 1; = 1 : . (10)

The Table 5 shows the final and average values of the

best and worst result reached by the algorithm throughout

30 experiments.


Articles98

VOLUME 5, N° 1 2011

Trial

1

2

3

4

5

6

7

8

9

10

Standard Deviation

1.525

0.056

0.0020

7.75e-05

2.87e-06

1.06e-07

3.93e-09

1.45e-10

5.40e-12

2.00e-13

Experiment

number

24

30

17

(Iteration 2)

Minimum

Value

0.99966

0.99997

6.88e-06

Average

Value

1.053

1.013

1.23

Comments

Best final result

Worst final result

Best result

(Minimum value)


iterations of experiment number 24, which reached the

minimum final value overall the 30 experiments..


along the 30 experiments, where every experiment is

represented by “Cycles” of 10 iterations each.

As we can see in Table 5 and Figure 12, the last value

reached in each experiment, may not be the “best value”

found by the algorithm.

Figure 13 shows the behavior of trial with the mini-

mum result through the 30 experiments. In this experi-

ment, the final value was 0.9997, but the minimum value

(6.88e-06) was found in iteration no. 2.

Fig. 11. Minimum value of experiment no. 24, evaluating

the Rosembrock’s Valley Function.

Fig. 12. Minimum values reached in 30 experiments

evaluating Rosembrock’s Valley Function.

Fig. 13. Experiment 17, which had the minimum value in

30 experiments.

Table 6 shows the standard deviation calculated by

iteration throughout 30 experiments.


deviations calculated per iteration in 30 experiments,


.




iteration in 30 experiments evaluating the Rosembrock’s

Valley Function.

5. ConclusionsIn this paper, we introduced the first stage of a new

optimization method that tries to mimic the chemical reac-

tions. The was applied

in 3 benchmark functions to evaluate the first develop-

ment phase of the optimization algorithm. This

by itself finds or guides the result

to a certain minimum value, but, due the nature of some

functions, it is necessary to introduce the second phase of

this optimization method: The

, which will be able to guide the algorithm to find

an optimum value when it is not necessarily the “smallest”

one. At the time, more functions are being evaluated to

pursue the tuning of the algorithm itself

Decomposition Reaction Method

Decompo-

sition Reaction Method

Combination Reactions

Method

ACKNOWLEDGMENTS

The authors would like to thanks CONACYT and Tijuana Insti-

tute of Technology for the facilities and resources granted for the

development of this research.


Articles 99

VOLUME 5, N° 1 2011

Trial

1

2

3

4

5

6

7

8

9

10

Standard Deviation

13.443

0.796

0.205

0.024

0.004

0.0014

0.0012

0.00053

0.00018

6.44e-05


Articles100

AUTHORS

References

Leslie Astudillo*, Patricia Melin, Oscar Castillo -


Tiju-

ana Institute of Technology, Tijuana México, E-mails:

[email protected],

[email protected],

[email protected].

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lar Neural Networks with Interval Type-2 Fuzzy Logic

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[2] Astudillo L., Castillo O.,Aguilar L., “Hybrid Control for

an Autonomous Wheeled Mobile Robot Under Per-

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, 2007, pp. 594-603.

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, Piscataway, NJ, 1995, pp. 1942-

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[10] Angeline P. J., “Using Selection to Improve Particle

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[12] Haupt R.L., Haupt S.E., “

”, 2 Edition, Wiley Interscience, 2004.

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[14] Chang R., “ ”, 5 edition, McGraw-

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[15] Goldberg D., “

”, 3 edition, Schaum's Outline Series, McGraw-

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[16] (single and multi-objec-

tive functions), http://www.geatbx.com/docu/fcnindex-

01.html#P89_3085.

Schaum's Outline of Beginning Chemi-

stry

GEATbx: Example Functions

rd

VOLUME 5, N° 1 2011

Abstract:

1. Introduction

Swarm Intelligence is the part of Artificial Intelligence

based on study of actions of individuals in various decen-

tralized systems. The optimization algorithms which are

inspired from intelligent behavior of honey bees are

among the most recently introduced population based

techniques. In this paper, a novel hybrid algorithm based

in Bees Algorithm and Particle Swarm Optimization is

applied to the Knapsack Problem. The Bee Algorithm is

a new population-based search algorithm inspired by the

natural foraging behavior of honey bees, it performs

a kind of exploitative neighborhood search combined with

random explorative search to scan the solution, but the

results obtained with this algorithm in the Knapsack

Problem are not very good. Although the combination of

BA and PSO is given by BSO, Bee Swarm Optimization,

this algorithm uses the velocity vector and the collective

memories of PSO and the search based on the BA and the

results are much better. We use the Greedy Algorithm,

which it's an approximate algorithm, to compare the

results from these metaheuristics and thus be able to tell

which is which gives better results.

Keywords: swarm optimization, PSO, BA, BSO, Knap-

sack Problem.

Evolutionary and meta-heuristic algorithms have been

extensively used as search and optimization tools during

this decade in several domains from science and enginee-

ring to the video game industry, and others.

Many demanding applications that involve the solu-

tion of optimization problems of high complexity, a lot of

these belonging to a special class of problems called NP-

hard have been solved by various methods [8]. Metahe-

uristic algorithms are now considered among the best

tools must to find good solutions with a reasonable invest-

ment of resources.

As described by Eberhart and Kennedy [4] Particle

Swarm Optimization or PSO algorithm is part of the

Swarm Intelligence and is a metaheuristics that use the

social-psychological metaphor; a population of indivi-

duals, referred to as particles, adapts by returning stochas-

tically toward previously successful regions. The PSO

simulate a society where each individual contributes with

his knowledge to the society. These metaheuristics have

proved their ability to deal with very complicated optimi-

zation and search problems.

The behavior of a single ant, bee, termite or wasp often

is too simple, but their collective and social behavior is of

paramount significance. The collective and social beha-

vior of living creatures motivated researchers to undertake

the study of today what is known as Swarm Intelligence

[5]. Two fundamental concepts, self-organization and

division of labor, are necessary and sufficient proper-

ties to obtain swarm intelligent behavior.

The Bee Algorithm or BA [10] is also part of the

Swarm Intelligence and this mimics the honey bees and

who this search their food foraging. This algorithm is ba-

sed on a random search on the neighborhood for combi-

natorial and functional optimization.

The Knapsack Problem is a classical combinatorial

problem [3], [15] can be described as follows: “Imagine

taking a trip to which you can only carry a backpack that,

logically, has a limited capacity. Given a set of items, each

with a weight and a value, determine the number of each

item to include in a bag so that the total weight is less than

a given limit and the total value is as large as possible”,

this problem can be considerate as NP-easy problem but

some studies show that the Knapsack Problem is an NP-

Hard problem [2].

Actually the Knapsack Problem can be modeled by

different ways [16] for example multi-dimensional and

multiple Knapsack problem [1], [14], [17], quadratic

Knapsack problem [6], [13].

In the present paper we introduce the Bee Swarm Opti-

mization or BSO. This algorithm is a hybrid metaheu-

ristic between the PSO and the BA. The BSO use the better

characteristics from both algorithms, the Social Metaphor

from the PSO and the random search in the neighborhood

from the BA, and give us a better result. The experiments

were made on seven types of instances from uncorrelated,

to uncorrelated similar weight. All these instances probe

the algorithm varying the parameters of the profits and the

weight. The algorithms were probed with different me-

thods for generating the initial populations [9] like ran-

dom solutions, uniformly distributed solutions and greedy

base solutions. Another important characteristic is the

fitness's Confidence Interval for saying what metaheuris-

tic with who initial population is better. It was necessary to

have a comparison point for the metaheuristics and was

use the Greedy Algorithm [3], this is a deterministic algo-

rithm who gives an approximate result for the Knapsack

Problem.

2. Knapsack ProblemThe Knapsack problem [15] is the typical combina-

torial problem that has been studied since many years ago

and was proved that it is a NP-Hard problem [12]. The ba-

sic problem is the 0-1 Knapsack Problem or Binary

Knapsack Problem and it have a search space of 2 – 1n

APPLICATION OF THE BEE SWARM OPTIMIZATION BSOTO THE KNAPSACK PROBLEM

Marco Aurelio Sotelo-Figueroa, Rosario Baltazar, Juan Martín Carpio


Articles 101

possible solutions.

The Knapsack Problem can be described as follows:

“there are objects, each of this objects have a profit and

weight, and needs to select those whose sum of their bene-

fits is maximized subject to the sum of the weight of the

same objects should not exceed an amount determined”.

It can be formulated mathematically by numbering each

of its objects or items from 1 to and introducing it to

a vector of binary variables = 1,2,3,..., , where each

variable represented here will take the value 1 or 0 depen-

ding on whether it is selected or not.

The solution to the Knapsack Problem is select a sub-

set of objects from the binary vector , solution vector, that

satisfies the constraint on the equation (2) and the same

time maximize the objective function on the equation (1).

(1)

(2)

where:

represents the profit.

The Knapsack Problem is affected by the relationship

between the profit and the weight of the objects; these

types of instances are the following:

the profits and Weight are distributed

uniformly between one and a maximum number.

(3)

the Weight is distributed uniformly

between one and a maximum number and the profits are

distributed uniformly around the weight and an ratio.

(4)

the Weight is uniformly distri-

buted between one and a maximum number; the profits

are the Weight plus one constant.

(5)

the profits are distributed

uniformly between one and a maximum number and the

Weight is the profits plus one constant.

(6)

the Weight is distributed

uniformly between one and a maximum number and the

profits are the Weight plus one random number between

one and a maximum number.

n

n

x n

x

z

Uncorrelated:

V

Weakly correlated:

V

R

Strongly correlated:

V

K

Inverse strongly correlated:

V

K

Almost strongly correlated:

V

S

j

�

�

�

�

�

�

represents the j-th object.

indicates whether the j object is part of the solution.

is the j-th object profit.

is the j-th object weight.

is the volume or capacity of the knapsack.

j

x

p

w

c

j

j

j

2.1. Types of Knapsack Problem Instances

(7)

the profits and Weight have the same

value and are distributed uniformly between one and

a maximum number.

(8)

the profits are distribu-

ted uniformly between one and a maximum number

and the Weight is distributed uniformly between one and

a maximum number plus a constant.

(9)

Subset-sum:

V

Uncorrelated similar Weight:

V1

V2 K

3. The Greedy Algorithm

4. BeeAlgorithm (BA)

This algorithm gives a intuitive approach considering

the profit and weight of each item, it is know as efficiency

and it's based on the Equation (10) and the main is to try to

put the items with highest efficiency into the Knapsack.

It is necessary sort all the items based on the efficiency,

using the Equation (11), before to apply the Greedy to the

problem.

(10)

(11)

The Bee Algorithm [10] or BA is a bio-inspired meta-

heuristic behavior of honey bees and how they searching

for plant to obtain the necessary pollen for honey pro-

duction.

A colony of bees search in a large territory looking for

new sources of food and begins to thrive on discovering

new food sources. When these sources have much pollen

are visited by large numbers of bees and when the pollen

decreases the number of bees collected from these sources

decreases too.

When season for recollecting pollen start, the colony

sent so many bees, which are called scouts bees to

reconnoiter randomly the territory to inform at the colony

where are the best food sources. Once the harvesting

season starts the colony maintains a certain percentage of

their scout bees in order to detect new and better sources of

food. When scout bees have returned to the colony and

found a better food source than the currently is using the

colony, makes the Dance by means of which transmits the

exact position of the source food and then the colony

began to send more bees to the food source.

Fig. 1. The BA Algorithm applied to the 0-1 Knapsack

Problem.


Articles102

VOLUME 5, N° 1 2011

z = p� xj j

n

j = 1

w x� j j c�

p V w Vj j� �[1, ]; [1, ]

w V p w R w Rj j j j� � � �[1, ] [ , ];

w V p = w Kj j j� �[1, ];

p V w = p Kj j j� �[1, ];

S U w V p = w S� � �[1, ] [1, ];; j j j

w V p = wj j j� [1, ];

p V w Vj j� � �[1, ]; [1, ]1 2 K

e =j

� � �

pj

wj

n

j = 1

x =j { If the object is selectedj

0 otherwise

p0

w0

p1

w1

...pn

wn

get better results from the PSO and use the exploration and

a search radius from the BA to indicate which is where

they look for a better result.

The first thing that the BSO do is update the position of

the particles through the velocity vector and then select

a specified number of particles, in this case it is proposed

to select the best as being the new food supply that the

scout bees discovered, and conducted a search of the area

enclosed by the radius search and if there are a better solu-

tion in this area than the same particle around which are

looking for then the particle is replaced with the position

of the best solution found in the area.

For this metaheuristic to work properly you need

a way to determine what the next and earlier particle posi-

tion, if we cannot determine this then it is impossible to ap-

ply this metaheuristic because you would not know what

the next and previous particle to search in that area.

We defined the next and before solutions like binary

operations, adding and subtracting one number to the so-

lution vector. For example if we have a Knapsack Problem

with 5 elements the solution vector will have 5 spaces and

in each space can be 0 or 1, we can see the example of the

next and before solution vector in the Figure 3.

The algorithm of the BSO applied to the Knapsack

Problem implemented in the present paper is the follo-

wing.

To Test the BSO was used the Generator of Knapsack

Test Instances [11]; it requires the number of elements and

the coefficients range to generate a test instance. Were ge-

nerate the seven types of test instances described, and was

use the same parameters for each metaheuristic to find

which of this are better for the Knapsack Problem. Each

metaheuristic was run 100 times for obtaining their aver-

age and standard deviation, and was use that data for cal-

culating the Confidence Interval at the 97% of confidence.

We use 3 different Initial Population, this because is

importance [9] of start with a good solution the metaheu-

Fig. 3. Example of the previous and next solution vector.

Fig. 4. The BSO Algorithm applied to the 0-1 Knapsack

Problem.

7. Experiments

5. Particle Swarm Optimization (PSO)

6. Bee Swarm Optimization (BSO)

The Particle Swarm Optimization [4], [7] or PSO is

a Bio-inspired metaheuristic in flocks of birds or schools

of fish. It was developed by J. Kennedy and R. Eberthart

based on a concept called social metaphor, this metaheu-

ristics simulates a society where all individuals contribute

their knowledge to obtain a better solution, there are three

factors that influence for change in status or behavior of an

individual:

The Knowledge of the environment or adaptation

which speaks of the importance given to the expe-

rience of the individual.

His Experience or local memory is the importance

given to the best result found by the individual.

The Experience of their neighbors or Global memory

referred to how important it is the best result I have

obtained their neighbors or other individuals.

In this metaheuristic each individual is called particle

and moves through a multidimensional space that repre-

sents the social space or search space depends on the di-

mension of space which depends on the variables used to

represent the problem.

For the update of each particle using something called

velocity vector which tells them how fast it will move the

particle in each of the dimensions, the method for upda-

ting the speed of PSO is given by equation (12), and it is

updating by the equation (13).

(12)

(13)

where:

is the velocity of the i-th particle

is adjustment factor to the environment.

is the memory coefficient in the neighborhood.

is the coefficient memory.

is the position of the i-th particle.

is the best position found so far by all particles.

is the best position found by the i-th particle

The Bee Swarm Optimization or BSO is a hybrid me-

taheuristic population between the PSO and the BA. The

main idea of BSO is based on taking the best of each me-

taheuristics to obtain better results than they would obtain.

The BSO use the velocity vector and the way to upda-

ting it, equation (12), and applies the social metaphor to

�

�

�

�

�

�

�

�

�

�

v

x

B

B

i

i

Global

Local

�

�

�

0

1

2

Fig. 2. The PSO Algorithm applied to the 0-1 Knapsack

Problem.


Articles 103

VOLUME 5, N° 1 2011

v = * v + x B x Bi i i Global i Local� � � � �0 1 2( ) + ( )

x = x + vi i i

ristics, the initial population use were the followings:

we use the GreedyAlgorithm.

we use the number generator

uniformly distributed.

we use the random number generator of

Java.

We determinate the size of the Knapsack obtaining the

average size of the instance and considerate it average like

the size of the elements and multiply it by 20 for conside-

rate 20 elements.

Parameters Values

Elements 50

Coefficients Range [0,100]

Parameters Values

Elements 50

Iterations 100

Elements for Neighborhood 10

Search Radio 3

Parameters Values

Elements 50

Iterations 100

1

0.5

0.8

�

�

�

Greedy:

Uniformly Distributed:

Random:

Table 1. Parameters used in the Generator of Knapsack

Test Instances.

Table 2. Parameters used in the BA.

Table 3. Parameters used in the PSO.

�

�

�

0

1

3

Table 4. Parameters used in the BSO.

Parameters Values

Elements 50

Iterations 100

Elements for Neighborhood 10

Search Radio 3

1

0.5

0.8

In this work we show the results obtained by testing

each Type Instances with the different metaheuristics and

the three different kinds of initial population, we show the

Average profit, the Best profit and Worst profit, their fit-

ness's Standard Deviation for each metaheuristic and their

fitness's Confidence Interval. We also show the metaheu-

ristics behavior through their graphic. In the graphics the

red squares represent the results obtained by the Greedy

Algorithm, the blue dots represent the Initial Population

based on the Greedy, the green triangles represent the

Initial Population Uniformly Distributed and the yellow

diamonds represent the Initial Population Random.

To test the Uncorrelated instance the Knapsack size

was defined at 1037. We can see the results of each meta-

heuristic with different initial populations in the Table 5

and their behavior in the Figures 5, 6 and 7. The result

provide by the Greedy Algorithm was 1751 and as can be

seen the BA gives the worst results, always equal or under

the Greedy's result, while the PSO and BSO gives better

results but the Standard Deviation from the BSO is smal-

ler than the PSO and theirAverage from the BSO is better.

�

�

�

0

1

3

8. Results

8.1. Uncorrelated


Articles104

VOLUME 5, N° 1 2011

Table 5. Results obtained from the Uncorrelated Knapsack Problem.

Metaheuristic

PSO

BA

BSO

Initial Population

Greedy

Uniformly Distributed

Random

Greedy


Random

Greedy


Random

Average

1753.79

1751.39

1750.96

1751

1397.43

1440.25

1753.52

1753.07

1752.65

Best

1758

1758

1758

1751

1522

1550

1758

1758

1758

Worst

1751

1725

1716

1751

1291

1296

1751

1733

1733

�

2.29

6.51

6.3

0

51.08

50.66

2.1

4.12

4.74

Confidence Interval

[1753.08, 1754.49]

[1749.39, 1753.38]

[1749.02, 1752.89]

[1751.00, 1751.00]

[1381.75, 1413.10]

[1424.70, 1455.79]

[1752.87, 1754.16]

[1751.80, 1754.33]

[1751.19, 1754.10]

Fig. 5. Graphic behaviors of BA metaheuristics apply to the Uncorrelated problem.

Fig. 6. Graphic behaviors of PSO metaheuristics apply to the Uncorrelated problem.

Fig. 7. Graphic behaviors of BSO metaheuristics apply to the Uncorrelated problem.


Articles 105

VOLUME 5, N° 1 2011


Articles106

8.2. Weakly correlated

To test the Weakly Correlated instance, the Knapsack size was defined at 914. We can see the results of each metaheuristic

with different initial populations in the Table 6 and their behavior in the Figures 8, 9 and 10. The result provide by the Greedy

Algorithm was 1044 and as we can see the BA gives the worst results, only with the initial population based on the Greedy

improve the Greedy's result, the BSO and PSO gives better results but the BSO improve the PSO's results and gives a smaller

Standard Deviation.

Table 6. Results obtained from the Weakly Correlated Knapsack Problem.

Fig. 8. Graphic behaviors of BA metaheuristics apply to the weakly correlated problem.

Fig. 9. Graphic behaviors of PSO metaheuristics apply to the weakly correlated problem.

VOLUME 5, N° 1 2011

Metaheuristic

PSO

BA

BSO

Initial Population

Greedy


Random

Greedy


Random

Greedy


Random

Average

1049.83

1040.34

1040.03

1046

965.14

973.1

1050.87

1045.18

1045.19

Best

1053

1053

1053

1046

987

1006

1053

1053

1053

Worst

1046

1027

1023

1046

941

957

1046

1038

1033

�

2.11

5.52

6.14

0

9.23

9.46

1.77

3.71

4.14

Confidence Interval

[1049.18, 1050.48]

[1038.64, 1042.03]

[1038.14, 1041.91]

[1046, 1046]

[962.30, 967.97]

[970.19,976.00]

[1050.32, 1051.41]

[1044.04, 1046.31]

[1043.91, 1046.46]


Articles 107

Fig. 10. Graphic behaviors of BSO metaheuristics apply to the weakly correlated problem.

Table 7. Results obtained from the Strongly Correlated Knapsack Problem.

Fig. 11. Graphic behaviors of BA metaheuristics apply to the strongly correlated problem.

8.3. Strongly correlated

To test the Strongly Correlated instance, the Knapsack size was defined at 1026. We can see the results of each meta-

heuristic with different initial populations in the Table 7 and their behavior on the Figures 11, 12 and 13. The result provide by

the GreedyAlgorithm was1332 and as we can see the BAgives the worst results, only with the initial population based on the

Greedy improve the Greedy's result, the BSO and PSO gives better results but the BSO improve the PSO's results and gives

a smaller Standard Deviation.

VOLUME 5, N° 1 2011

Metaheuristic

PSO

BA

BSO

Initial Population

Greedy


Random

Greedy


Random

Greedy


Random

Average

1345.82

1328.83

1331.78

1332

1265.39

1276.78

1345.8

1337.16

1337.24

Best

1346

1343

1336

1332

1295

1296

1346

1346

1346

Worst

1342

1314

1317

1332

1245

1260

1342

1326

1331

�

0.67

6.28

4.69

0

10.42

7.99

0.8

3.46

3.47

Confidence Interval

[1345.61, 1346.02]

[1326.90, 1330.75]

[1330.33, 1333.22]

[1332, 1332]

[1262.19, 1268.58]

[1274.32, 1279.23]

[1345.55, 1346.04]

[1336.09, 1338.22]

[1336.17, 1338.30]


Articles108

Fig. 12. Graphic behaviors of PSO metaheuristics apply to the strongly correlated problem.

Fig. 13. Graphic behaviors of BSO metaheuristics apply to the strongly correlated problem.

Table 8. Results obtained from the Inverse Strongly Correlated Knapsack Problem.

8.4. Inverse strongly correlated

To test the Inverse Strongly Correlated instance was define the Knapsack size at 1182. We can see the results of each

metaheuristic with different initial populations in the Table 8 and their behavior in the Figures 14, 15 and 16. The result

provide by the Greedy Algorithm was 1051 and we can see the BA gives the worst results, only with the initial population

based on the Greedy improve the Greedy's result, the BSO and PSO gives better results but the BSO improve the PSO's results

and gives a smaller Standard Deviation.

VOLUME 5, N° 1 2011

Metaheuristic

PSO

BA

BSO

Initial Population

Greedy


Random

Greedy


Random

Greedy


Random

Average

1051.34

1036.42

1034.65

1051

1011.7

1003.55

1051.87

1044.81

1044.01

Best

1052

1052

1049

1051

1036

1021

1052

1052

1052

Worst

1051

1017

1021

1051

998

990

1051

1038

1033

�

0.47

5.86

5.82

0

7.06

5.84

0.33

3.99

3.79

Confidence Interval

[1051.19, 1051.48]

[1034.62, 1038.21]

[1032.86, 1036.43]

[1051, 1051]

[1009.53, 1013.86]

[1001.75, 1005.34]

[1051.76, 1051.97]

[1043.58, 1046.03]

[1042.84, 1045.17]


Articles 109

Fig. 14. Graphic behaviors of BA metaheuristics apply to the inverse strongly correlated problem.

Fig. 15. Graphic behaviors of PSO metaheuristics apply to the inverse strongly correlated problem.

Fig. 16. Graphic behaviors of BSO metaheuristics apply to the inverse strongly correlated problem.

VOLUME 5, N° 1 2011


Articles110

8.5. Almost strongly correlated

To test the Almost Strongly Correlated instance was define the Knapsack size at 124. We can see the results of each


provide by the GreedyAlgorithm was 1524 as we can see the BAgives the worst results, only with the initial population based

on the Greedy improve the Greedy's result, the BSO and PSO gives better results but the average from the PSO is better than

the BSO while the Standard Deviation from the BSO is smaller than the PSO.

Table 9. Results obtained from the Almost Strongly Correlated Knapsack Problem.

Fig. 17. Graphic behaviors of BA metaheuristics apply to the almost correlated problem.

Fig. 18. Graphic behaviors of PSO metaheuristics apply to the almost correlated problem.

VOLUME 5, N° 1 2011

Metaheuristic

PSO

BA

BSO

Initial Population

Greedy


Random

Greedy


Random

Greedy


Random

Average

1554.62

1538.19

1541.33

1552

1491.92

1501.51

1554.97

1550.06

1550.84

Best

1555

1555

1555

1552

1508

1517

1555

1555

1555

Worst

1552

1521

1529

1552

1474

1488

1554

1542

1543

�

0.78

6.39

5.54

0

7.38

6.73

0.17

5.01

4.83

Confidence Interval

[1554.37, 1554.86]

[1536.22, 1540.15]

[1539.62, 1543.03]

[1552, 1552]

[1489.65, 1494.18]

[1499.44, 1503.57]

[1554.91, 1555.02]

[1548.52, 1551.59]

[1549.35, 1552.32]


Articles 111

Fig. 19. Graphic behaviors of BSO metaheuristics apply to the almost correlated problem.

Table 10. Results obtained from the Subset-sum Knapsack Problem.

Fig. 20. Graphic behaviors of BA metaheuristics apply to the subset-num problem.

8.6. Subset-sum

To test the Subset-num instance was define the Knapsack size at 960. We can see the results of each metaheuristic with

different initial populations in the Table 10 and their behavior in the Figures 20, 21 and 22. The result provide by the Greedy

Algorithm was 949 and in this instance the PSO and the BSO gives the same results with Standard Deviation zero, better that

the Greedy's result, and the BAapproaching to this results.

VOLUME 5, N° 1 2011

Metaheuristic

PSO

BA

BSO

Initial Population

Greedy


Random

Greedy


Random

Greedy


Random

Average

960

960

960

959.82

959.85

959.99

960

960

960

Best

960

960

960

960

960

960

960

960

960

Worst

960

960

960

959

958

959

960

960

960

�

0

0

0

0.38

0.38

0.1

0

0

0

Confidence Interval

[960, 960]

[960, 960]

[960, 960]

[959.70, 959.93]

[959.73, 959.96]

[959.95, 960.02]

[960, 960]

[960, 960]

[960, 960]


Articles112

Fig. 21. Graphic behaviors of PSO metaheuristics apply to the subset-num problem.

Fig. 22. Graphic behaviors of BSO metaheuristics apply to the subset-num problem.

Table 11. Results obtained from the Uncorrelated Similar Weight Knapsack Problem.

To test the Uncorrelated Similar Weight instance was define the Knapsack size at 200118. We can see the results of each


provide by the GreedyAlgorithm was 1558 as we can see the BAgives the worst results, only with the initial population based

on the Greedy equals the Greedy's result, the BSO and PSO gives better results but the average from the BSO is better than the

PSO while the Standard Deviation from the PSO is smaller than the BSO.

8.7. Uncorrelated SimilarWeight

VOLUME 5, N° 1 2011

Metaheuristic

PSO

BA

BSO

Initial Population

Greedy


Random

Greedy


Random

Greedy


Random

Average

1563.79

1562.18

1561.38

1558

1303.6

1312.81

1563.72

1562.22

1562.28

Best

1564

1564

1564

1558

1406

1404

1564

1564

1564

Worst

1558

1548

1551

1558

1243

1242

1558

1551

1548

�

1.03

3.15

3.63

0

35.45

35.8

1.23

3.35

3.26

Confidence Interval

[1563.47, 1564.10]

[1561.21, 1563.14]

[1560.26, 1562.49]

[1558, 1558]

[1292.71, 1314.48]

[1301.82, 1323.79]

[1563.34, 1564.09]

[1561.19, 1563.24]

[1561.27, 1563.28]


Articles 113

Fig. 23. Graphic behaviors of BA metaheuristics apply to the uncorrelated similar weight problem.

Fig. 24. Graphic behaviors of PSO metaheuristics apply to the uncorrelated similar weight problem.

Fig. 25. Graphic behaviors of BSO metaheuristics apply to the uncorrelated similar weight problem.

VOLUME 5, N° 1 2011


Articles114

9. ConclusionsThere are many metaheuristics to solve the Knapsack

Problem; in this work we introduce the Bee Swarm

Optimization which is a hybrid metaheuristic between the

Bee Algorithm and the Particle Swarm Optimization. The

experiments were designed with the same parameters for

the three metaheuristics to give them the same charac-

teristics in order to be equal between them, and the initial

population was generated by a pseudorandom number

generator, the generator provide by Java, a uniformly dis-

tributed generator and a greedy generator, using the Gre-

edyAlgorithm.

After applying the BSO, with the three initial popu-

lation, to the different instances tests we can see that these

metaheuristics give a small Confidence Interval and in

most cases its Confidence Interval is better that the PSO's

and BA's. In the worst case the Confidence Interval is the

same that the PSO's. In the Uncorrelated instances, Sub-

set-sum and Uncorrelated similar weight, the results were

similar between PSO and BSO, and in the Weakly corre-

lated instances and Strongly correlated the results were

better for BSO than for PSO. Finally in the Inverse Stron-

gly correlated and Almost Strongly Correlated the results

were much better for BSO than for PSO.

We can see in all the graphics the metaheuristic beha-

vior, and we can observe that the BAis the worst metaheu-

ristic because it always yields a highly variable result, be-

cause of this it's Standard Deviation is so high, only with

the Greedy base initial population gives the same result

that the provide by the Greedy Algorithm or improve it

a little. The PSO yields more consistent results, it's graphs

show that this metaheuristic in the most of the cases gives

good results.

We can conclude that have a good initial population

for the metaheuristics is essential to have good results and

we can say that the BSO is an effective metaheuristic to

solve the Knapsack Problem, with all the initial popula-

tion used, because each time that it's runned, it gives

a good solution, and this solution is better that the solu-

tions obtained by the other metaheuristics. Overall the

results present a low Standard Deviation and thus a short

Confidence Interval.

- Instituto Tecnológico de León,

Av. Tecnológico S/N, 37290, León, Guanajuato, México.

E-mails: [email protected],

[email protected],

[email protected] .


AUTHORS

Marco Aurelio Sotelo-Figueroa*, Rosario Baltazar,

Juan Martín Carpio

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