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Optimum Path Planning for Mobile Robots Based on a Hybrid Genetic Algorithm
Auckland, New Zealand December 13-December 15
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/HIS.2006.53Sixth International Conference on Hyb ...
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Qing Li, Xinhai Tong, Sijiang Xie, Yingchun Zhang, "Optimum Path Planning for Mobile Robots Based on a Hybrid Genetic Algorithm," Hybrid Intelligent Systems, International Conference on, pp. 53, Sixth International Conference on Hybrid Intelligent Systems (HIS'06), 2006.
 
 
BibTex
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@article{ 10.1109/HIS.2006.53,
author = {Qing Li and Xinhai Tong and Sijiang Xie and Yingchun Zhang},
title = {Optimum Path Planning for Mobile Robots Based on a Hybrid Genetic Algorithm},
journal ={Hybrid Intelligent Systems, International Conference on},
volume = {0},
year = {2006},
isbn = {0-7695-2662-4},
pages = {53},
doi = {http://doi.ieeecomputersociety.org/10.1109/HIS.2006.53},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Hybrid Intelligent Systems, International Conference on
TI - Optimum Path Planning for Mobile Robots Based on a Hybrid Genetic Algorithm
SN - 0-7695-2662-4
SP
EP
A1 - Qing Li,
A1 - Xinhai Tong,
A1 - Sijiang Xie,
A1 - Yingchun Zhang,
PY - 2006
KW - null
VL - 0
JA - Hybrid Intelligent Systems, International Conference on
ER -
 
Qing Li, University of Science and Technology, China
Xinhai Tong, Beijing Electronic Science and Technology Institute, China
Sijiang Xie, Beijing Electronic Science and Technology Institute, China
Yingchun Zhang, Ryerson University, Canada
A hybrid genetic algorithm based optimum path planning approach for mobile robots is proposed in this paper. A new proposed self-adaptive algorithm for controlling the crossover and mutation probabilities is adopted to replace the adjustment algorithm in an improved genetic algorithm, which is specifically designed for optimum path planning of mobile robots. The simulation studies in varying environments are carried out to demonstrate the effectiveness of the proposed algorithm and the simulation results show that the hybrid genetic algorithm has provided faster search speed compared with the recently reported method.
Citation:
Qing Li, Xinhai Tong, Sijiang Xie, Yingchun Zhang, "Optimum Path Planning for Mobile Robots Based on a Hybrid Genetic Algorithm," his, pp.53, Sixth International Conference on Hybrid Intelligent Systems (HIS'06), 2006
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