南京信息工程大学学报2025，Vol.17Issue(2)：256-264,9.DOI:10.13878/j.cnki.jnuist.20240506001
基于蚁群-动态窗口法的无人驾驶汽车动态路径规划
Dynamic path planning for autonomous vehicles via ant colony-dynamic window approach
摘要
Abstract
To address the issues of low search efficiency,long distance,and non-smooth paths in traditional path planning algorithms for autonomous vehicles,this study proposes an improvement by using key nodes of the optimized ant colony algorithm to replace the local target points in the dynamic window approach.Additionally,a tar-get distance evaluation sub-function is incorporated into the dynamic window approach's evaluation function to en-hance the efficiency and smoothness of path planning.Furthermore,a path decision-making method is employed to solve the problem of global path failure,enabling the vehicle to avoid obstacles and meet safety requirements of path planning.The improved ant colony algorithm utilizes the positional information of the start and end points to create an uneven initial pheromone distribution,thereby reducing time consumption during the initial search phase.By maintaining the global optimal paths and enhancing the pheromone concentration of excellent local paths,the phero-mone update mechanism is optimized to speed up path exploration efficiency.The planned path is further optimized to reduce redundancy in nodes and turning points,thereby shortening path length.Simulation results show that com-pared to traditional path planning algorithms,the proposed integrated algorithm achieves better performance in terms of distance,smoothness,and convergence,aligning with the safety requirements for autonomous vehicle operation.关键词
路径规划/蚁群算法/动态窗口法/动态避障/融合算法Key words
path planning/ant colony algorithm(ACO)/dynamic window approach(DWA)/dynamic obstacle avoidance/integrated algorithm分类
交通运输引用本文复制引用
郑琰,席宽,巴文婷,肖玉杰,余伟..基于蚁群-动态窗口法的无人驾驶汽车动态路径规划[J].南京信息工程大学学报,2025,17(2):256-264,9.基金项目
国家自然科学基金(71871111) （71871111）
