融合改进A^(*)算法与DWA算法的室内移动机器人路径规划研究  

Research on Path Planning for Indoor Mobile Robots by Integrating Improved A^(*)Algorithm and DWA Algorithm

作　　者：王世刚 李迪 WANG Shigang*;LI Di(Department of Automation Guangxi University of Science and Technology,Liuzhou 545616,China)

机构地区：[1]广西科技大学自动化学院,广西壮族自治区柳州545616

出　　处：《传感器世界》2024年第4期20-27,共8页Sensor World

基　　金：广西科技基地和人才专项(No.桂科AD22080004)。

摘　　要：针对A^(*)算法进行路径规划时搜索节点过多、路径不平滑、不安全,动态窗口法进行路径规划时路径冗余、局部最优等问题,文章改进A^(*)算法并融合改进A^(*)与动态窗口法,用于室内移动机器人全局及局部路径规划。在A^(*)算法改进部分,添加移动机器人安全膨胀系数,将搜索邻域优化为5邻域搜索,动态调节评价函数,将路径平滑处理。动态窗口法改进部分,融合改进A^(*)算法的关键点到动态窗口法中作为目标点。实验结果表明,改进的A^(*)算法搜索节点减少,能与障碍物保持安全距离,路径平滑;改进的动态窗口法剔除冗余路径,避免局部最优。通过复杂地图验证,均达到预期要求。When using the A^(*)algorithm for path planning,there are too many search nodes,the path is not smooth,and the path is unsafe,when using Dynamic Window Approach for path planning,there is path redundancy and local optimum.The article improves the A^(*)algorithm and integrates it with the Dynamic Window Approach for global and local path planning of indoor mobile robots.In the A^(*)algorithm improvement section,add a safety expansion coefficient for mobile robots,optimize the search neighborhood to 5 neighborhood searches,dynamic adjustment evaluation function,smooth the path.In the improvement section of the Dynamic Window Approach,the key points of the improved A^(*)algorithm are integrated into the Dynamic Window Approach as the target points.The experimental results show that the improved A^(*)algorithm reduces the number of search nodes,maintains a safe distance from obstacles,and ensures a smooth path;Improved Dynamic Window Approach to eliminate redundant paths and avoid local optima.Verified through complex maps,all achieved the expected requirements.

关 键 词：融合算法 关键点引导 室内移动机器人 全局路径规划 局部路径规划 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]