Path planning of patrol robot based on modified grey wolf optimizer

运动规划 粒子群优化 趋同(经济学) 数学优化 路径(计算) 稳健性(进化) 轮盘赌 计算机科学 航程(航空) 算法 人口 适应度函数 机器人 工程类 遗传算法 数学 人工智能 社会学 航空航天工程 人口学 经济 几何学 化学 基因 程序设计语言 生物化学 经济增长
作者
Qian Zhang,Xucheng Ning,Yingying Li,Lei Pan,Rui Gao,Jing Wang
出处
期刊:Robotica [Cambridge University Press]
卷期号:41 (7): 1947-1975 被引量:2
标识
DOI:10.1017/s0263574723000231
摘要

Abstract The grey wolf optimizer (GWO) as a new intelligent optimization algorithm has been successfully applied in many fields because of its simple structure, few adjustment parameters and easy implementation. This paper mainly aims at the defects of GWO in path planning application, such as easily falling into local optimization, poor convergence and poor accuracy, and turn point grey wolf optimization (TPGWO) algorithm is proposed. First, the idea of cross-mutation and roulette is used to increase the initial population of GWO and improve the search range. At the same time, the convergence factor function is improved to become a nonlinear update. In the early stage, the search range is expanded, and in the later stage, the convergence speed is increased, while the parameters in the convergence factor function can be adjusted according to the number of obstacles and the map area to change the turning point of the function to improve the convergence speed and accuracy of the algorithm. The turning times and turning angles of the obtained path are added to the fitness function as penalty values to improve the path accuracy. The optimization test is carried out through 16 test functions, and the test results prove the convergence and robustness of TPGWO algorithm. Finally, the TPGWO algorithm is applied to the path planning of patrol robot for simulation experiments. Compared with the GWO algorithm and Particle Swarm Optimization, the simulation results show that the TPGWO algorithm has better convergence, stability and accuracy in the path planning of patrol robot.
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