基于GA优化与路径扩展启发式采样的BI-RRT\*路径规划方法

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中图分类号：U467.13 文献标识码：A DOI:10.3969/j.issn.1674-8484.2025.06.012

¹，¹，¹²（1.；2.）

BI-RRT* path planning method based on GA optimization and path extension heuristic sampling

ZHANG Bingli1, ZHANG Zhisen1,ZHANG Yangyang1,LIU An1,XU Yonghua² (1.SchoolofutomotiveandransportationEngineering,HefeiUniversityofechnologyHefeioo,Ca; 2.HefeiSoftecAutoElectronic Co.,Ltd.,Hefei 23060o,China)

Abstract:To address the issues of slow convergence and excessive path randomness in traditional BlRRT*,this paper proposed a two-stage optimization framework algorithm,GEP_BlRRT,which combined an improvedBl-RRT*algorithmwithevolutionarystrategies.Firstly，flexibleboundaryconstraints to theBl-RRT* algorithm were introduced toenhance search eficiency bylimiting the sampling range,and a metric function to obtain high-quality feasible paths was designed.Second,a path optimization was performed based on a genetic algorithm,inwhichanoptimization region centered on feasiblepaths was constructed and a multiobjective fitness functionwas designed to balance path smoothnessand safety,ultimatelyyielding theplanned path.Finaly,simulation experiments were conducted using MATLAB software.The strong robustness of GEP_

BIRRTacross three distinctenvironments was demonstrated by the results.The results show thatcompared to Informed-RRT*and traditional Bl-RRT*,the planning duration is reduced by 59.48% and 20.08% onaverage, respectively，withaverage path length reductions of 1.26% and 1.51% ,and cumulative turning angle reductions of 32.60% and 40.84% ,respectively.Italso efectivelyavoids dynamic obstacles,validating the superiorityand feasibility of the GEP_BIRRTalgorithm.

Keywords:path planning; path extension; geneticalgorithm; fast random search

智能车辆随着自动驾驶技术的快速发展正逐步走向规模化应用[]，其中路径规划是连接环境感知与运动控制的重要环节，在真实道路交通场景中，环境的不确定性以及道路拓扑的复杂性都对车辆自主避障能力提出了更高要求[2]。（剩余18525字）