自动化学报2026，Vol.52Issue(2)：335-348,14.DOI:10.16383/j.aas.c250319
多障碍场景下基于多策略进化机制的无人机三维路径规划
Multi-Strategy Evolutionary Mechanism for UAV 3D Path Planning in Multi-Obstacle Environments
摘要
Abstract
Aiming at the problems such as low convergence accuracy and insufficient stability in path planning for unmanned aerial vehicles(UAVs)in 3D multi-obstacle scenarios,a multi-strategy evolutionary particle swarm op-timization(MSEPSO)algorithm is proposed.In the initialization stage,aiming at the problem that particle swarm optimization is sensitive to the initial position of particles,the initial distribution of particles is optimized through Latin hypercube sampling to improve the population diversity;During the evolutionary stage,a"balance-memory-enhancement"evolutionary framework is designed,which utilizes a nonlinear iterative strategy to balance global de-velopment and local search.The personal history memory mechanism is adopted to enhance the global exploitation ability of the algorithm.Evolutionary particles are introduced to enhance the exploration ability of the population in the vicinity of the group's extreme values,reducing the probability of the algorithm getting stuck in local op-tima.Experimental results from comparisons on the CEC2020 test function set and in mountain/urban scenarios demonstrate that MSEPSO exhibits stable optimization performance,enabling the planning of safer paths with shorter lengths and higher smoothness.关键词
无人机/三维路径规划/粒子群算法/多策略进化Key words
UAVs/3D path planning/particle swarm algorithm/multi-strategy evolution引用本文复制引用
朱润泽,赵静,陆宁云,马亚杰,宋来收..多障碍场景下基于多策略进化机制的无人机三维路径规划[J].自动化学报,2026,52(2):335-348,14.基金项目
航空航天结构力学及控制国家重点实验室开放课题(MCMS-E-0123G04),直升机动力学全国重点实验室开放课题(2024-ZSJ-LB-02-05),江苏省研究生科研与实践创新计划项目(KYCX24_1214)资助Supported by State Key Laboratory of Aerospace Structural Mechanics and Control(MCMS-E-0123G04),National Key Laboratory Foundation of Helicopter Aeromechanics(2024-ZSJ-LB-02-05),and Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX24_1214) （MCMS-E-0123G04）
