融合A^(*)和DWA算法的移动机器人路径规划方法  

Integration of A^(*)and DWA algorithms for mobile robot path planning

作　　者：袁新亚 戴娟 孙胜强 刘经纬 Yuan Xinya;Dai Juan;Sun Shengqiang;Liu Jingwei(University of Beijing Information Science&Technology Beijing Key Laboratory of High Dynamic Navigation Technology,Beijing 100192,China;Key Laboratory of Modern Measurement&Control Technology,Ministry of Education,Beijing 100192,China;School of Automation,Beijing Information Science&Technology University,Beijing 100192,China)

机构地区：[1]北京信息科技大学高动态导航技术北京市重点实验室,北京100192 [2]现代测控技术教育部重点实验室,北京100192 [3]北京信息科技大学自动化学院,北京100192

出　　处：《电子测量技术》2024年第4期95-103,共9页Electronic Measurement Technology

摘　　要：本研究旨在解决自主移动机器人在点到点路径规划中面临的搜索效率低下、易陷入局部最优解以及对未知动静态障碍物处理不够实时的问题。为此,将改进A^(*)算法与改进DWA进行了有效融合。在改进的A^(*)算法中,我们引入了基于障碍率的权重因子和双向优化策略,以提升搜索效率并生成更加平滑的路径。同时,改进的DWA算法融入了两种新的障碍物评价函数,并通过调整权重系数有效地避免了局部最优解问题。通过将改进的DWA算法与改进的A^(*)算法结合,实现了对未知动静态障碍物的高效实时避障。仿真实验结果显示,提出的改进A算法与传统A算法以及文献[23]的改进算法相比,在四种环境下的表现表明:路径转弯次数分别平均减少了30.14%和18.16%,搜索空间分别减少了35.09%和15.21%,规划时间分别降低了82.36%和38.26%。进一步地,结合改进的DWA算法后,路径规划时间、路径长度和平均运动速度相比融合传统DWA算法和文献[23]的融合算法分别平均减少了37.46%和9.82%,减少了4.59%和3.63%,提高了53.49%和7.09%。This study aims to tackle the challenges encountered by autonomous mobile robots in point-to-point path planning,encompassing issues such as low search efficiency,susceptibility to local optima,and inadequate real-time handling of unknown dynamic and static obstacles.To this end,we have carried out an effective integration of the enhanced A^(*)algorithm with the improved DWA.Within the enhanced A^(*)algorithm,we have introduced obstacle-rate-based weighting factors and a bidirectional optimization strategy,aiming to bolster search efficiency and facilitate the generation of smoother paths.Furthermore,the refined DWA algorithm integrates two novel obstacle evaluation functions and adeptly addresses the local optima issue through the adjustment of weight coefficients.By unifying the enhanced DWA algorithm with the improved A^(*)algorithm,we have enabled proficient real-time obstacle avoidance for unknown dynamic and static obstacles.Simulation results indicate that the improved A^(*)algorithm proposed in this paper,compared with the traditional A^(*)algorithm and the enhanced algorithm from reference[23],demonstrates significant performance improvements in four different environments.Specifically,the number of path turns decreased by an average of 30.14%and 18.16%,the search space was reduced by 35.09%and 15.21%,and the planning time was shortened by 82.36%and 38.26%,respectively.Furthermore,when integrated with the improved DWA algorithm,the time required for path planning,the length of the planned path,and the average motion speed were optimized compared to combining with the traditional DWA algorithm and the fusion algorithm from reference[23],showing an average reduction of 37.46%and 9.82%in planning time,a decrease of 4.59%and 3.63%in path length,and an increase of 53.49%and 7.09%in average motion speed.

关 键 词：自主移动机器人 改进A^(*)算法 改进DWA算法 点到点路径规划 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]