基于粒子群灰狼算法的路径规划

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中图分类号：TP301.6 文献标识码：A文章编号：2096-4706（2026）05-0051-06

（1.无锡太湖学院，江苏无锡214064；2.中国电子科技集团公司第五十八研究所，江苏无锡214000）

Path Planning Based on HHPSO-GWO

， （1.Wuxi Taihu University，Wuxi 214064，China; 2.The 58th Institute ofCETC，Wuxi 214000，China）

Abstract： To address the problems of lowcomputational efciency，premature convergenceor slow late convergence in path planing，a grid map is constructed anda Hierarchical Hybrid Particle Swarm Optimization-Grey Wolf Optimizer （HHPSOGWO）is proposed.Thealgorithmdivides thepopulationintoeliteandordinary particlesandimplements diferentiated leaing todynamicallybalancesearchperformance.Through simulationcomparisons，theoptimal path lengths planned by HHPSOGWO are 16.650 0 and44.2843 respectively，whichare improved by 30.33% and 16.24% compared with PsO，and by 6.98% （2号 and 11.93% compared with GWO.The numbers of convergence iterationsare only12 and9，which are much fewer than 47and 52ofthe PSOalgorithm，andbeterthan55and12of the GWOalgorithm.Theresultsshow thatthe algorithmcan improve the convergence speed and effectively shorten the path length， providing a new method for path planning.

Keywords： path planning; HHPsO-GWO; differentiated learning; optimal path

0 引言

在机器人领域，无论是工业生产线上的机械臂作业，还是服务机器人在复杂室内环境中的自主导航，都需要精确的路径规划来确保任务的高效执行。（剩余6708字）