改进蚁群算法的机器人路径规划研究

doi:10.16180/j.cnki.issn1007-7820.2020.01.003

Abstract

Abstract:

Aiming at the problem that the traditional ant colony algorithm had slow convergence speed and easy to fall into local optimum, an improved ant colony algorithm was proposed. Based on the traditional A ^* algorithm, the valuation function of the traditional A ^* algorithm was improved. which was further introduced into the ant colony algorithm. The modified heuristic function η was proposed to increase the attraction of the target point to the path search and improve the convergence speed. The pheromone volatilization factor ρ was improved, and the pheromone volatilization factor was dynamically changed, which promoted the global search ability of the algorithm and prevent it from falling into local optimum. The simulation results showed that the improved ant colony algorithm was nearly 50% faster than the traditional ant colony algorithm in convergence rate, and was superior to the traditional ant colony algorithm in the shortest path, which proved the effectiveness of the improved algorithm.

Key words: ant colony algorithm, A ^* algorithm, robot, heuristic factor, pheromonevolatil, path planning

CLC Number:

TP242.6

LIU Yongjian,ZENG Guohui,HUANG Bo,LI Xiaobin. Research on Robot Path Planning Based on Improved Ant Colony Algorithm[J].Electronic Science and Technology, 2020, 33(1): 13-18.

Figures/Tables 9

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	传统蚁群算法	改进的蚁群算法
最小路径长度	31	30
迭代次数	33	23

	传统蚁群算法	改进的蚁群算法
最小路径长度	37	32
迭代次数	36	27

Research on Robot Path Planning Based on Improved Ant Colony Algorithm

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