An Adaptive Particle Swarm Optimization Algorithm for Distributed Search and Collective Cleanup in Complex Environment

Distributed coordination is critical for a multirobot system in collective cleanup task under a dynamic environment. In traditional methods, robots easily drop into premature convergence. In this paper, we propose a Swarm Intelligence based algorithm to reduce the expectation time for searching targ...

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Bibliographic Details
Main Authors: Yi Cai, Zhutian Chen, Jun Li, Qing Li, Huaqing Min
Format: Article
Language:English
Published: Hindawi - SAGE Publishing 2013-12-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1155/2013/560579
Description
Summary:Distributed coordination is critical for a multirobot system in collective cleanup task under a dynamic environment. In traditional methods, robots easily drop into premature convergence. In this paper, we propose a Swarm Intelligence based algorithm to reduce the expectation time for searching targets and removing. We modify the traditional PSO algorithm with a random factor to tackle premature convergence problem, and it can achieve a significant improvement in multi-robot system. It performs well even in a obstacle environment. The proposed method has been implemented on self-developed simulator for searching task. The simulation results demonstrate the feasibility, robustness, and scalability of our proposed method compared to previous methods.
ISSN:1550-1477