基于改进灰狼优化算法的移动机器人路径规划  

A Path Planning Algorithm Based on Improved GWO for Mobile Robots

作　　者：张天瑞[1] 刘玉亭 ZHANG Tian-rui;LIU Yu-ting(School of Mechanical Engineering,Shenyang University,Shenyang 110044)

机构地区：[1]沈阳大学机械工程学院,辽宁沈阳110044

出　　处：《制造业自动化》2025年第4期31-39,共9页Manufacturing Automation

基　　金：国家自然科学基金面上项目(52075088);辽宁省研究生教育教学改革研究资助项目(LNYJG2022490)。

摘　　要：为提高移动机器人路径规划效率,解决灰狼优化算法在路径规划避障问题上存在的收敛效率低和易陷入局部最优的不足,提出了一种改进的灰狼优化算法。首先,加入Tent混沌映射对初始种群初始化,以增加种群的多样性,进而提高收敛速度;其次,加入非线性收敛因子改进策略,从而在降低局部最优解的同时提高全局搜索的效率;进而,将粒子群位置更新策略用于灰狼种群位置更新中,旨在增强灰狼个体的自主搜索能力;最后,选用标准测试函数与PSO、GWO算法对比,改进算法具有优越的收敛性能和寻优精度。环境仿真实验表明,改进算法在平均路径长度、迭代次数、寻优时长等指标均优于其他对比算法。To improve the path planning efficiency of mobile robots and solve the shortcomings of Gray Wolf Optimization algorithm such as low convergence efficiency and suscepitability to falling into local optimal in path planning obstacle avoidance,a Particle Swarm Optimization improved Gray Wolf Optimization algorithm is proposed.Firstly,Tent chaotic mapping is first added to initialize the initial population to increase the diversity of the population and thus improve the convergence speed.Secondly,the nonlinear convergence factor improvement strategy is added to improve the efficiency of global search while reducing the local optimal solution.And then,the particle swarm location updating strategy is applied to the gray wolf population location updating to enhance the autonomous searching ability of individual gray wolves.Finally,compared with PSO and GWO,the improved algorithm has superior convergence performance and optimization accuracy.Simulation results show that the improved algorithm is superior to other algorithms in average path length,iteration times and search time.

关 键 词：灰狼优化算法 粒子群算法 改进算法 全局路径规划 移动机器人 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]