基于改进灰狼优化算法的机器人全局路径规划  

Global Path Planning of Robot Based on Improved Gray Wolf Optimization Algorithm

作　　者：孙波 周健康 赵玉清 张悦[3] 彭浩[1] 赵伟 SUN Bo;ZHOU Jian-kang;ZHAO Yu-qing;ZHANG Yue;PENG Hao;ZHAO Wei(Faculty of Mechanical and Electrical Engineering,Yunnan Agriculture University,Kunming 650201,China;Faculty of Transportation Engineering,Kunming University of Science and Technology,Kunming 650093,China;College of Big Data,Yunnan Agricultural University,Kunming 650201,China)

机构地区：[1]云南农业大学机电工程学院,昆明650201 [2]昆明理工大学交通工程学院,昆明650093 [3]云南农业大学大数据学院,昆明650201

出　　处：《科学技术与工程》2024年第33期14287-14297,共11页Science Technology and Engineering

基　　金：云南省重大科技专项计划(202302AE090020)。

摘　　要：随着智能机器人导航技术的发展,路径规划的研究也得到了越来越多的关注和重视。提出了一种改进的灰狼优化算法,用于解决路径规划中机器人容易陷入局部最优以及收敛速度慢的问题。首先,创建了一个二维空间模型,模仿机器人的路径规划过程。为了加强算法的全局搜索性能,在全局搜索阶段的位置更新公式中引入了动态扰动系数,并将位置更新公式中的控制参数由线性递减改进为非线性递减。其次,在局部搜索阶段引入了反向学习选择策略,以平衡灰狼种群的多样性和算法的局部挖掘能力,提高了算法的收敛精度。选择8种常见测试函数进行检验,数据结果表明了改进算法的有效性。最后将改进后的灰狼优化算法与原始灰狼优化算法、粒子群算法进行了对比实验,数据显示在简单、一般、复杂环境下,改进后的平均路径距离较改进前分别缩短了11.99%、7.79%、5.78%,平均迭代次数分别降低了75.63%、59.78%、43.67%,表明改进后的算法在最优距离和避障效果等方面都明显优于其他对比算法。With the development of intelligent robot navigation technology,much more attention has been paid on the research of path planning.An improved gray wolf optimization(GWO)algorithm was proposed for local optimum and slow convergence seed in robot path planning.Firstly,a two-dimensional spatial model was used to imitate the path planning process of robots.In the global search process,a dynamic perturbation coefficient and nonlinear decreasing control parameters(which are improved from linear decreasing)were proposed in the formula of position updating for the aim of enhancing global search performance.Then,considering the diversity of gray wolf population and local mining ability of the algorithm,a reverse learning selection strategy was introduced,which led to the improvement of convergence accuracy of the algorithm.The testing data from eight common functions has shown the effectiveness of the improved algorithm.Finally,a comparative experiment has been made among the improved gray wolf optimization algorithm,the original gray wolf optimization algorithm and particle swarm optimization algorithm,and the data has shown that the average path distance is shortened by 11.99%,7.79%and 5.78%,and iteration times is reduced by 75.63%,59.78%and 43.67%,respectively in simple,general and complex environments,which indicates the effectiveness in optimal distance planning and obstacle avoidance of this improved algorithm.

关 键 词：灰狼优化算法 路径规划 动态扰动系数 非线性控制参数 反向学习策略 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]