基于改进免疫遗传优化蚁群算法的移动机器人路径寻优研究

doi:10.3969/j.issn.1000-1158.2019.03.25

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Abstract Aiming at the problem that the ant colony algorithm(ACO) is easy to fall into the local optimum and the convergence speed is slow in the path planning of mobile robots, an improved algorithm is proposed for the static path optimization of robots, which is called as improved immune genetic algorithm (IMGAC). The algorithm can automatically adjust the mutation probability and mutation mode according to the actual situation and automatically adjust the length of individual immunization bits. The improved mutation operator and immune operator are embedded in ant colony algorithm to improve the global optimization ability and convergence speed. Simulation and experiment show that compared with the classical ACO algorithm and the maximum and minimum ant system, the IMGAC algorithm can converge faster and have better global search ability. The IMGAC algorithm also improves to the result and efficiency of robot path optimization.

Key words： metrology path optimization mobile robot genetic algorithm ant colony algorithm

Received: 13 November 2017 Published: 19 April 2019

PACS:	TB973
	TP389.1

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	Articles by authors
	ZHAO Chun-fang
	LI Jiang-hao
	ZHANG Da-wei

Cite this article:

ZHAO Chun-fang,LI Jiang-hao,ZHANG Da-wei. Robot Path Optimization Research Based on Improved Immune Genetic Optimization Ant Colony Algorithm[J]. Acta Metrologica Sinica, 2019, 40(3): 505-510.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2019.03.25 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2019/V40/I3/505

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