In many practical applications of robot path planning, finding the shortest path is critical, while the response time is often overlooked but important. To address the problems of search node divergence and long calculation time in the A* routing algorithm in the large scenario, this paper presents a novel center constraint weighted A* algorithm (CCWA*). The heuristic function is modified to give different dynamic weights to nodes in different positions, and the node weights are changed in the specified direction during the expansion process, thereby reducing the number of search nodes. An adaptive threshold is further added to the heuristic function to enhance the adaptiveness of the algorithm. To verify the effectiveness of the CCWA* algorithm, simulations are performed on 2-dimensional grid maps of different sizes. The results show that the proposed algorithm speeds up the search process and reduces the planning time in the process of path planning in a multi-obstacle environment compared with the conventional A* algorithm and weighted A* algorithm.

在机器人路径规划的许多实际应用中，找到最短路径至关重要，而响应时间往往被忽视但很重要。针对大场景下A*路由算法中搜索节点发散和计算时间长的问题，提出了一种新颖的中心约束加权A*算法（CCWA*）。修改启发式函数，对不同位置的节点赋予不同的动态权重，在扩展过程中沿指定方向改变节点权重，从而减少搜索节点的数量。在启发式函数中进一步增加了自适应阈值，以增强算法的自适应性。为了验证CCWA*算法的有效性，在不同尺寸的二维网格图上进行了模拟。