DocumentCode :
652553
Title :
Improving Particle Swarm Optimization Algorithm for Distributed Sensing and Search
Author :
Yi Cai ; Zhutian Chen ; Huaqing Min
Author_Institution :
Sch. of Software Eng., South China Univ. of Technol., Guangzhou, China
fYear :
2013
fDate :
28-30 Oct. 2013
Firstpage :
373
Lastpage :
379
Abstract :
Distributed coordination is critical for a multi-robot 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. 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 than previous methods.
Keywords :
convergence; multi-robot systems; particle swarm optimisation; rescue robots; PSO algorithm; collective cleanup task; distributed coordination; distributed sensing and search; multi-robot system; particle swarm optimization algorithm; premature convergence problem; self-developed simulator; swarm intelligence based algorithm; Convergence; Heuristic algorithms; Multi-robot systems; Particle swarm optimization; Robot kinematics; Robot sensing systems; Collective cleanup; PSO algorithm; Swarm intelligence; Swarm robotics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 2013 Eighth International Conference on
Conference_Location :
Compiegne
Type :
conf
DOI :
10.1109/3PGCIC.2013.64
Filename :
6681257
Link To Document :
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