融合改进A^(*)和DWA算法的室内机器人路径规划  

Indoor Robot Path Planning Incorporating Improved A^(*)Algorithms and DWA

作　　者：刘志超 李金凤 王海超 LIU Zhi-chao;LI Jin-feng;WANG Hai-chao(School of Information Engineering,Shenyang University of Chemical Technology,Shenyang 110142)

机构地区：[1]沈阳化工大学信息工程学院,辽宁沈阳110142

出　　处：《制造业自动化》2025年第2期51-58,共8页Manufacturing Automation

摘　　要：为了解决室内机器人路径规划中A^(*)算法效率低、冗余点多、无法动态避障的问题,提出了一种结合优化的A^(*)算法和动态窗口法的融合算法。在启发函数中引入父节点到目标点的距离,增强算法启发性;量化障碍物信息,设计障碍率函数动态调整启发函数权重;增加转弯代价,减少路径中不必要转弯;设计冗余点删除策略,删除冗余点、保证静态路径全局最优。加入越位角度,灵活选择A^(*)算法的关键节点作为动态窗口的局部目标点,避免路径陷入局部最优。实验结果表明,改进融合算法提高了搜索效率,降低了路径长度,解决了动态窗口法陷入局部最优问题,实现了实时避障。To address the issues of low efficiency,excessive redundancy,and the inability to dynamically avoid obstacles in indoor robot path planning when using the A^(*)algorithm,this paper proposes a fusion algorithm that combines an optimized A^(*)algorithm with the dynamic window approach.The proposed algorithm enhances the heuristic function by incorporating the distance from the parent node to the target node.It quantifies the obstacle information to dynamically adjust the weights of the heuristic function using an obstacle rate function.Additionally,it introduces a cost for turning to reduce unnecessary turns in the path and designs a strategy to remove redundant points,ensuring a globally optimal static path.It incorporates the offside angle,flexibly selects the key nodes of the A^(*)algorithm as local target points within the dynamic window to avoid the path getting trapped in local optima.The experimental results demonstrate that the improved fusion algorithm enhances the search efficiency,reduces the path length,resolves the issue of the dynamic window approach being trapped in local optima and enables the real-time obstacle avoidance.

关 键 词：机器人 路径规划 A^(*)算法 动态窗口法 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]