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Distributed gradient and particle swarm optimization for multi-robot motion planning

Published online by Cambridge University Press:  01 May 2008

Gerasimos G. Rigatos
Gerasimos G. Rigatos*
Affiliation:
Unit of Industrial Automation, Industrial Systems Institute, 26504 Rion Patras, Greece.
*
*Corresponding author. E-mail: [email protected]
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Summary

Two distributed stochastic search algorithms are proposed for motion planning of multi-robot systems: (i) distributed gradient, (ii) swarm intelligence theory. Distributed gradient consists of multiple stochastic search algorithms that start from different points in the solutions space and interact with each other while moving toward the goal position. Swarm intelligence theory is a derivative-free approach to the problem of multi-robot cooperation which works by searching iteratively in regions defined by each robot's best previous move and the best previous move of its neighbors. The performance of both approaches is evaluated through simulation tests.

Keywords

Multi-robot systemMotion planningStochastic search algorithmsDistributed gradientLyapunov stabilitySwarm intelligence
Type
Article
Information
Robotica , Volume 26 , Issue 3 , May 2008 , pp. 357 - 370
Copyright
Copyright © Cambridge University Press 2008

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