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Collision-free trajectory control for multiple robots based on neural optimization network

Published online by Cambridge University Press:  09 March 2009

Jihong Lee  and
Zeungnam Bien
Jihong Lee
Affiliation:
Department of Electrical Engineering, KAIST, P.O. Box 150, Chongyangni, Seoul, (Korea)
Zeungnam Bien
Affiliation:
Department of Electrical Engineering, KAIST, P.O. Box 150, Chongyangni, Seoul, (Korea)
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Summary

A collision-free trajectory control for multiple robots is proposed. The proposed method is based on the concept of neural optimization network. The positions or configurations of robots are taken as the variables of the neural circuit, and the energy of network is determined by combining various functions, in which one function is to make each robot approach to its goal and another helps each robot from colliding with other robots or obstacles. Also a differential equation of the circuit which tends to minimize the energy is derived. A new method for describing collision between articulated arms is presented and some heuristic method to improve the feasibility and the safety of the trajectory is proposed. Also illustrative simulation results for mobile robots and articulated robot arms are presented.

Keywords

Trajectory controlMultiple robotsNeural networkCollision-free
Type
Article
Information
Robotica , Volume 8 , Issue 3 , July 1990 , pp. 185 - 194
Copyright
Copyright © Cambridge University Press 1990

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References

REFERENCES

1.Lozano-Perez, T. and Wesley, M.A., “An Algorithm for Planning Collision-Free Paths among Polyhedral Obstacles” Commun. ACM 22, No. 10, 560–570 (10, 1979).CrossRefGoogle Scholar
2.Suh, S.H. and Shin, K.G., “A Variational Dynamic Programming Approach to Robot-Path Planning with a Distance-Safety Criterion” IEEE J. Robotics Automation RA-4, No. 3, 334–349 (01, 1988).CrossRefGoogle Scholar
3.Kim, B.K. and Shin, K.G., “Minimum-Time Path Planning for Robot Arms and Their Dynamics”, IEEE Trans. Syst., Man, Cybern. SMC-15, No. 2, 213–223 (03/04, 1985).CrossRefGoogle Scholar
4.Fujimura, K. and Samet, H., “A Hierarchical Strategy for Path Planning Among Moving Obstacles” IEEE Trans. Robotics Automation RA-5, No. 1, 61–69 (02, 1989).CrossRefGoogle Scholar
5.Lee, B.H. and Lee, C.S.G., “Collision-Free Motion Planning of Two Robots” IEEE Trans. Syst., Man Cybern. SMC-17, No. 1, 21–32 (01/02, 1987).CrossRefGoogle Scholar
6.Shin, Y.S. and Bien, Z., “A Novel Method of Collision-Free Trajectory Planning for Two Robot Arms” Proc. IEEE Int. Conf. Systems, Man, and Cybernetics 2, 791–794 (1988).Google Scholar
7.Rumelhart, D.E., Hinton, G.E. and Williams, R.J., Parallel Distributed Processing vol. 1 (The MIT Press, Cambridge, Mass., 1986) pp. 318–364.CrossRefGoogle Scholar
8.Hopfield, J.J. and Tank, D.W., “Computing with Neural Circuits: A Model” Science 233, 625–633 (08, 1986).CrossRefGoogle ScholarPubMed
9.Tank, D.W. and Hopfield, J.J., “Simple “Neural” Optimization Networks: An A/D Converter, Signal Decision Circuit, and a Linear Programming Circuit” IEEE Trans. Circuits Systems CAS-33, No. 5, 533–541 (05, 1986).CrossRefGoogle Scholar
10.Tsutgumi, K. and Matsumoto, H., “Neural computation and Learning Strategy for Manipulator Position Control” Int. Conf. Neural Networks vol. IV, (1987) pp. 525–534.Google Scholar
11.Bowyer, A. and Adrian, J., A Programmer's Geometry (Butterworth, London, 1983) pp. 48–52.Google Scholar