Research on Path Planning for Mobile Robot Based on Grid and Hybrid of GA/SA

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Abstract:

Path planning is the kernel problem of the robot technology area. In this paper, the grid method is used to make environmental modeling, Since the Genetic Algorithm (GA) has its immanent limitations and the Simulated Annealing (SA) Algorithm has the advantages in some aspects, combined these two algorithms together just achieve the perfection. In view of this, a hybrid of GA and SA (GA-SA Hybrid) is proposed in this paper to solve path planning problem for mobile robot. The algorithm making the crossover and mutation probability adjusted adaptively and nonlinearly with the completion time, can avoid such disadvantages as premature convergence. The new algorithm has better capability of searching globally and locally. The simulation results demonstrate that the proposed algorithm is valid and effective.

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Periodical:

Advanced Materials Research (Volumes 479-481)

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1499-1503

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Online since:

February 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] Hwang C L,Yoon K. Multiple Attribute Decision Making: Methods and Applications[M]. Berlin:Springs verlag,1981.

Google Scholar

[2] Yanrong Hu, Simon X Yang. A knowledge based genetic algorithm for path planning of a mobile robot[C]. New Orleans:Proceedings of the 2004 IEEE International Conference on Robotics Automation,2004.4350-4355.

DOI: 10.1109/robot.2004.1302402

Google Scholar

[3] Jie Gao, Linyan Sun,Mitsuo Gen. A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems[J].Computers & Operations Research, 2008, 35(9), pp.2892-2907.

DOI: 10.1016/j.cor.2007.01.001

Google Scholar

[4] Cheng-Dong Wu Ying Zhang Meng-xin Li. A Rough Set GA-based Hybri Method for Robot Path Planning J . International Journal of Automation and Computing (UK) 2006 1 29 34 .

DOI: 10.1007/s11633-006-0029-5

Google Scholar

[5] Kuang Hanyu, Jin Jing and Su Yong, "Improving Crossover and Mutation for Adaptive Genetic Algorithm", Computer engineering and application, 2006.12, pp.93-96.

Google Scholar