Motion control and navigation of multiple mobile robots for obstacle avoidance and target seeking: A rule-based neuro-fuzzy technique

Abstract

This paper describes a rule-based neuro-fuzzy technique for the navigation of 1000 robots in a cluttered environment. The planning and coordination between the mobile robots is extremely difficult. In the present analysis rule-based and rule-based neuro-fuzzy techniques are used to navigate multiple mobile robots in unknown and partially known environments. The aim of the robots is to reach a predefined goal. Based upon a reference motion, direction, distances between the robots and obstacles, and distances between the robots and targets, different types of rules are defined heuristically and refined later to find the steering angle. The control system combines a repelling influence related to the distance between robots and nearby obstacles and an attracting influence between the robots and targets. Then a hybrid rule-based neuro-fuzzy technique is analysed to find the steering angle of the robots. Simulation and experimental results show that the proposed rule-based neuro-fuzzy technique shows a better navigation performance in complex and unknown environments than the simple rule-based technique.

Keywords

mobile robots obstacle avoidance target seeking rule-based neuro-fuzzy navigation

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References

Newell

Simon

H. A.

Human problem solving, (Prentice Hall, Englewood Cliffs, NJ 1972)

Post

‘Formal reductions of the general combinatorial problem.’

Am. J. Math. 65 (1943): 197–268

Rosenbloom

P. S.

Laird

J. E.

Newell

McCarl

‘A preliminary analysis of the soar architecture as a basis for general intelligence.’

Artif. Intell. 47 (1-3) (1991): 289–525

Gürman

N. T.

‘The neural network model RuleNet and its application to mobile robot navigation.’

Fuzzy Sets Syst. 85 (2) (1997): 287–303

C. F.

Wahl

‘A neuro-fuzzy system architecture for behavior-based control of a mobile robot in unknown environments.’

Fuzzy Sets Syst. 87 (2) (1997): 133–140

Barfoot

C. W.

Ibrahim

M. Y.

‘Development of an adaptive fuzzy behavioural control system with experimental and industrial applications.’

Comput. Ind. Engng 34 (4) (1998): 807–811

Jelena

Nigel

‘Neuro-fuzzy control of a mobile robot.’

Neurocomputing 28 (1-3) (1999): 127–143

Acosta

Marichal

G. N.

Moreno

Méndez

J. A.

Rodrigo

J. J.

‘Obstacle avoidance using the human operator experience for a mobile robot.’

J. Intell. Robot. Syst. 27 (4) (2000): 305–319

10.

Marichal

G. N.

Acosta

Moreno

Mendez

J. A.

Rodrigo

J. J.

Sigut

‘Obstacle avoidance for a mobile robot: A neuro-fuzzy approach.’

Fuzzy Sets Syst. 124 (2) (2001): 171–179

11.

Althoefer

Krekelberg

Husmeier

Seneviratne

‘Reinforcement learning in a rule-based navigator for robotic manipulators.’

Neurocomputing 37 (1-4) (2001): 51–70

12.

Nefti

Oussalah

Djouani

Pontnau

‘Intelligent adaptive mobile robot navigation.’

J. Intell. Robot. Syst. 30 (4) (2001): 311–329

13.

Hui

N. B.

Mahendar

Pratihar

D. K.

‘Time-optimal, collision-free navigation of a car-like mobile robot using neuro-fuzzy approaches.’

Fuzzy Sets Syst. 157 (16) (2006): 2171–2204

14.

Mucientes

Moreno

D. L.

Bugarin

Barro

‘Design of a fuzzy controller in mobile robotics using genetic algorithms.’

Appl. Soft Comput. 7 (2) (2007): 540–546

15.

M. J.

Tan

T. P.

Loh

S. Y.

‘Control of a mobile robot using generalized dynamic fuzzy neural networks.’

Microprocess. Microsyst. 28 (9) (2004): 491–498

16.

M. J.

Deng

‘Obstacle avoidance of a mobile robot using hybrid learning approach.’

IEEE Trans. Ind. Electron. 52 (3) (2005): 898–905

17.

Pradhan

S. K.

Parhi

D. R.

Panda

A. K.

‘Potential field method to navigate several mobile robots.’

Appl. Intell. 25 (3) (2006): 321–333

18.

Pradhan

S. K.

Parhi

D. R.

Panda

A. K.

‘Neuro-fuzzy technique for navigation of multiple mobile robots.’

Fuzzy Optim. Decision Mak. 5 (2006): 255–288

19.

Huq

Mann

G. K. I.

Gosine

R. G.

‘Mobile robot navigation using motor schema and fuzzy context dependent behavior modulation.’

Appl. Soft Comput. 8 (1) (2008): 422–436

20.

Tunstel

Howard

Seraji

‘Rule-based reasoning and neural network perception for safe off-road robot mobility.’

Expert Syst. 19 (4) (2002): 191–200

21.

de Souza

M. A. F.

Ferreira

M. A. G. V.

‘Designing reusable rule-based architectures with design patterns.’

Expert Syst. Appl. 23 (4) (2002): 395–403

22.

Y. K.

S. Y.

‘Hybrid control for autonomous mobile robot navigation using neural network based behavior modules and environment classification.’

Auton. Robots 15 (2) (2003): 193–206

23.

Dietrich

Kozlenkov

Schroeder

Wagner

‘Rule-based agents for the semantic web.’

Electron. Commer. Res. Appl. 2 (4) (2003): 323–338

24.

McIntosh

B. S.

Muetzelfeldt

R. I.

Legg

C. J.

Mazzoleni

Csontos

‘Reasoning with direction and rate of change in vegetation state transition modeling.’

Environment Modell. Software 18 (10) (2003): 915–927

25.

Pradhan

S. K.

Parhi

D. R.

Panda

A. K.

‘Fuzzy logic techniques for navigation of several mobile robots.’

Appl. Soft Comput. (in press).

26.

Clementine Software, 2000. Version-5, available from http://www.spss.com/Clementine/.

27.

Peterson

J. L.

Petri net theory and the modelling of systems, (Prentice Hall, Englewood Cliffs, NJ. 1981)

28.

Haykin

Neural networks a comprehensive foundation, second edition, (Prentice Hall, Englewood Cliffs, NJ. 1999)