Among the shortcomings of the A* algorithm, for example, there are many search nodes in path planning, and the calculation time is long. This article proposes a three-neighbor search A* algorithm combined with artificial potential fields to optimize the path planning problem of mobile robots. The algorithm integrates and improves the partial artificial potential field and the A* algorithm to address irregular obstacles in the forward direction. The artificial potential field guides the mobile robot to move forward quickly. The A* algorithm of the three-neighbor search method performs accurate obstacle avoidance. The current pose vector of the mobile robot is constructed during obstacle avoidance, the search range is narrowed to less than three neighbors, and repeated searches are avoided. In the matrix laboratory environment, grid maps with different obstacle ratios are compared with the A* algorithm. The experimental results show that the proposed improved algorithm avoids concave obstacle traps and shortens the path length, thus reducing the search time and the number of search nodes. The average path length is shortened by 5.58%, the path search time is shortened by 77.05%, and the number of path nodes is reduced by 88.85%. The experimental results fully show that the improved A* algorithm is effective and feasible and can provide optimal results.

A*算法的缺点中，例如路径规划中搜索节点多，计算时间长。本文提出了一种结合人工势场的三邻域搜索A*算法来优化移动机器人的路径规划问题。该算法将局部人工势场与A*算法进行融合改进，解决前进方向的不规则障碍物。人工势场引导移动机器人快速前进。三邻搜索法的A*算法进行准确的避障。移动机器人在避障时构建当前位姿向量，将搜索范围缩小到少于三个邻居，避免重复搜索。在基质实验室环境中，不同障碍物比例的网格图与A*算法进行比较。实验结果表明，提出的改进算法避免了凹形障碍物陷阱，缩短了路径长度，从而减少了搜索时间和搜索节点数量。平均路径长度缩短5.58%，路径搜索时间缩短77.05%，路径节点数减少88.85%。实验结果充分表明改进的A*算法是有效可行的，能够提供最优的结果。路径搜索时间缩短77.05%，路径节点数减少88.85%。实验结果充分表明改进的A*算法是有效可行的，能够提供最优的结果。路径搜索时间缩短77.05%，路径节点数减少88.85%。实验结果充分表明改进的A*算法是有效可行的，能够提供最优的结果。