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

作　　者：郑琰[1] 席宽 巴文婷 肖玉杰 余伟[1] ZHENG Yan;XI Kuan;BA Wenting;XIAO Yujie;YU Wei(College of Automobile and Traffic Engineering,Nanjing Forestry University,Nanjing 210037,China;School of Marketing and Logistics Management,Nanjing University of Finance&Economics,Nanjing 210023,China)

机构地区：[1]南京林业大学汽车与交通工程学院,南京210037 [2]南京财经大学营销与物流管理学院,南京210023

出　　处：《南京信息工程大学学报》2025年第2期256-264,共9页Journal of Nanjing University of Information Science & Technology

基　　金：国家自然科学基金(71871111)。

摘　　要：针对传统路径规划算法在无人驾驶汽车应用中搜索效率低、距离较长和路径不平滑的问题进行改进,使用改进蚁群算法最优路径的关键节点替代动态窗口法的局部目标点,并在动态窗口法评价函数中加入目标距离评价子函数,提高路径规划的效率和平滑性,同时采用路径决策方法解决全局路径失效问题,使车辆摆脱障碍困境,满足路径规划安全性的要求.改进后的蚁群算法利用起止点的位置信息使初始信息素分布不均匀,减少搜索初期阶段的时间消耗;通过维护全局最优路径和强化优秀局部路径的信息素浓度,优化信息素更新机制,提高路径探索效率;对规划路径进行二次优化,优化节点和冗余转折点,减少路径长度.仿真结果表明,相比传统路径规划算法,利用本文提出的融合算法所得到的路径在距离、平滑度和收敛性方面都具有更好的表现,且符合无人驾驶汽车安全行驶的要求.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 target distance evaluation sub-function is incorporated into the dynamic window approach's evaluation function to enhance 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 pheromone 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 compared 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 

分 类 号：U436.6[交通运输工程—道路与铁道工程] TP18[自动化与计算机技术—控制理论与控制工程]