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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-12-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2013/560579 |
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| 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. |
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| ISSN: | 1550-1477 |