中国机械工程2024，Vol.35Issue(6)：993-999,1009,8.DOI:10.3969/j.issn.1004-132X.2024.06.005
麻雀搜索算法-粒子群算法与快速扩展随机树算法协同优化的智能车辆路径规划
Cooperative Optimization of Intelligent Vehicle Path Planning Based on PSO-SSA and RRT
摘要
Abstract
Regarding the issues of long response time and low planning efficiency in the path plan-ning algorithms for smart vehicles facing diverse working scenarios,a multi-element collaborative op-timization strategy was proposed.Firstly,the vigilance mechanism of SSA was integrated with the population optimization characteristics of PSO,optimizing the inertia weight factor and learning factor in the PSO algorithm.Secondly,a"triangular wiring"search rule was introduced to perform bidirec-tional optimization(RRT-Connect)on the RRT algorithm.Subsequently,a complex environmental road simulation model was established using MATLAB software,and simulation tests were conducted on the proposed optimization solutions.The results demonstrate that,compared to single optimization approaches,the collaborative optimization algorithm exhibits significant advantages in terms of path length and planning time.Finally,real-vehicle tests are conducted on the application scenarios of the two collaborative optimization solutions,showing that in local path planning,the SSA-PSO algorithm has a shorter response time and higher planning efficiency,while in global path planning,the"trian-gular wiring"RRT-Connect algorithm exhibits greater advantages.关键词
路径规划/麻雀搜索算法/粒子群算法/三角布线/快速扩展随机树算法Key words
path planning/sparrow search algorithm(SSA)/particle swarm optimization(PSO)algorithm/triangular wiring/rapidly-exploring random tree(RRT)algorithm分类
交通工程引用本文复制引用
张志文,刘伯威,张继园,唐杰,张天赐..麻雀搜索算法-粒子群算法与快速扩展随机树算法协同优化的智能车辆路径规划[J].中国机械工程,2024,35(6):993-999,1009,8.基金项目
国家自然科学基金-区域创新发展联合基金(U20A20332) （U20A20332）
国家自然科学基金(52175063) （52175063）
