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Soft Computing

Erschienen in:

30.05.2016 | Methodologies and Application

An improved ant colony algorithm for robot path planning

verfasst von: Jianhua Liu, Jianguo Yang, Huaping Liu, Xingjun Tian, Meng Gao

Erschienen in: Soft Computing | Ausgabe 19/2017

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Abstract

To solve the problems of convergence speed in the ant colony algorithm, an improved ant colony optimization algorithm is proposed for path planning of mobile robots in the environment that is expressed using the grid method. The pheromone diffusion and geometric local optimization are combined in the process of searching for the globally optimal path. The current path pheromone diffuses in the direction of the potential field force during the ant searching process, so ants tend to search for a higher fitness subspace, and the search space of the test pattern becomes smaller. The path that is first optimized using the ant colony algorithm is optimized using the geometric algorithm. The pheromones of the first optimal path and the second optimal path are simultaneously updated. The simulation results show that the improved ant colony optimization algorithm is notably effective.

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Titel: An improved ant colony algorithm for robot path planning
verfasst von: Jianhua Liu
Jianguo Yang
Huaping Liu
Xingjun Tian
Meng Gao
Publikationsdatum: 30.05.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 19/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-016-2161-7

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