Distributed Adaptation in Multi-Robot Search using Particle Swarm Optimization

We present an adaptive strategy for a group of robots engaged in the localization of multiple targets. The robotic search algorithm is inspired by chemotaxis behavior in bacteria, and the algorithmic parameters are updated using a distributed implementation of the Particle Swarm Optimization technique. We explore the efficacy of the adaptation, the impact of using local fitness measurements to improve global fitness, and the effect of different particle neighborhood sizes on performance. The robustness of the approach in non-static environments is tested in a time-varying scenario.


Published in:
Proceedings of the 10th International Conference on the Simulation of Adaptive Behavior, 393-402
Presented at:
10th International Conference on the Simulation of Adaptive Behavior 2008, Osaka, Japan, July 7-12, 2008
Year:
2008
Keywords:
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 Record created 2008-04-08, last modified 2018-09-13

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