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2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'06)
Multi-robot Path Planning Based on Cooperative Co-evolution and Adaptive CGA
Hong Kong, China
December 18-December 22
ISBN: 0-7695-2748-5
ASCII Text
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Dongyong Yang, Jinyin Chen, Naofumi Matsumoto, Yuzo Yamane, "Multi-robot Path Planning Based on Cooperative Co-evolution and Adaptive CGA," Intelligent Agent Technology, IEEE / WIC / ACM International Conference on, pp. 547-550, 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'06), 2006.
 
 
BibTex
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@article{ 10.1109/IAT.2006.94,
author = {Dongyong Yang and Jinyin Chen and Naofumi Matsumoto and Yuzo Yamane},
title = {Multi-robot Path Planning Based on Cooperative Co-evolution and Adaptive CGA},
journal ={Intelligent Agent Technology, IEEE / WIC / ACM International Conference on},
volume = {0},
year = {2006},
isbn = {0-7695-2748-5},
pages = {547-550},
doi = {http://doi.ieeecomputersociety.org/10.1109/IAT.2006.94},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Intelligent Agent Technology, IEEE / WIC / ACM International Conference on
TI - Multi-robot Path Planning Based on Cooperative Co-evolution and Adaptive CGA
SN - 0-7695-2748-5
SP547
EP550
A1 - Dongyong Yang,
A1 - Jinyin Chen,
A1 - Naofumi Matsumoto,
A1 - Yuzo Yamane,
PY - 2006
KW - null
VL - 0
JA - Intelligent Agent Technology, IEEE / WIC / ACM International Conference on
ER -
 
Dongyong Yang, Zhejiang University of Technology, China
Jinyin Chen, Zhejiang University of Technology, China
Naofumi Matsumoto, Ashikaga Institute of Technology, Japan
Yuzo Yamane, Ashikaga Institute of Technology, Japan
Multi-robot path planning is a challenge for mobile robots in AI. Multi-objective optimized algorithm based on cooperative co-evolution and CGA is brought up in this paper. Shortest path length, minimum time cost, smoothest and limited speed, obstacle-collide free and robot-collide free are the objectives and constraints to optimize. Linear combination of them is designed as evaluation function for CGA with self-adaptive crossover and mutation rate, combined with chaos disturbs. Finally 2D dynamic simulation has proved the efficiency of the algorithm.
Citation:
Dongyong Yang, Jinyin Chen, Naofumi Matsumoto, Yuzo Yamane, "Multi-robot Path Planning Based on Cooperative Co-evolution and Adaptive CGA," iat, pp.547-550, 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'06), 2006
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