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Proceedings Paper

A bio-inspired swarm robot coordination algorithm for multiple target searching
Author(s): Yan Meng; Jing Gan; Sachi Desai
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Paper Abstract

The coordination of a multi-robot system searching for multi targets is challenging under dynamic environment since the multi-robot system demands group coherence (agents need to have the incentive to work together faithfully) and group competence (agents need to know how to work together well). In our previous proposed bio-inspired coordination method, Local Interaction through Virtual Stigmergy (LIVS), one problem is the considerable randomness of the robot movement during coordination, which may lead to more power consumption and longer searching time. To address these issues, an adaptive LIVS (ALIVS) method is proposed in this paper, which not only considers the travel cost and target weight, but also predicting the target/robot ratio and potential robot redundancy with respect to the detected targets. Furthermore, a dynamic weight adjustment is also applied to improve the searching performance. This new method a truly distributed method where each robot makes its own decision based on its local sensing information and the information from its neighbors. Basically, each robot only communicates with its neighbors through a virtual stigmergy mechanism and makes its local movement decision based on a Particle Swarm Optimization (PSO) algorithm. The proposed ALIVS algorithm has been implemented on the embodied robot simulator, Player/Stage, in a searching target. The simulation results demonstrate the efficiency and robustness in a power-efficient manner with the real-world constraints.

Paper Details

Date Published: 1 May 2008
PDF: 9 pages
Proc. SPIE 6964, Evolutionary and Bio-Inspired Computation: Theory and Applications II, 696406 (1 May 2008); doi: 10.1117/12.782504
Show Author Affiliations
Yan Meng, Stevens Institute of Technology (United States)
Jing Gan, Stevens Institute of Technology (United States)
Sachi Desai, U.S. Army RDECON-ARDEC (United States)

Published in SPIE Proceedings Vol. 6964:
Evolutionary and Bio-Inspired Computation: Theory and Applications II
Misty Blowers; Alex F. Sisti, Editor(s)

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