【摘要】 针对A~*算法在移动机器人路径规划存在搜索效率低，路径斜穿障碍物顶点，路径拐弯多等问题。提出一种改进的A~*算法，首先在A~*算法的邻域扩展中采用避免斜穿障碍物顶点的策略；再引入障碍物因素对评价函数进行指数加权，减少不必要的搜索，提高A~*算法的效率和灵活性，使算法偏向于选择障碍物较少的路径；最后使用三次优化折线的策略，加入障碍物安全距离，减少路径上的冗余节点和拐弯。使用MATLAB进行实验仿真，结果表明，在20 m×20 m、40 m×40 m、60 m×60 m栅格地图环境下，改进A~*算法较传统A~*算法，搜索时间分别减少70.12%、84.31%、91.44%,扩展节点分别减少53.77%、71.20%、74.30%,路径累计拐弯角度分别减少70.48%、76.31%、82.18%,改进A~*算法能够有效的提高移动机器人路径规划的效率，路径更为平滑和安全，且在复杂环境中优势更为明显。更多还原

【Abstract】 An improved A~* algorithm is proposed to address the issues of low search efficiency, path diagonally crossing obstacle vertices, and excessive turns in mobile robot path planning. Firstly, a strategy is introduced to avoid diagonally crossing obstacle vertices during the neighborhood expansion in the A~* algorithm. Secondly, an exponential weight is applied to the evaluation function based on obstacle factors to reduce unnecessary search and improve the efficiency and adaptability of the A~* algorithm, favoring paths with fewer obstacles. Finally, a three-phase optimization strategy is employed, considering the obstacle safety distance, to minimize redundant nodes and turns in the path. MATLAB simulations are conducted in grid maps of sizes 20×20 m, 40×40 m, and 60×60 m. The results demonstrate that the improved A~* algorithm significantly reduces search time by 70.12%, 84.31%, and 91.44%, respectively, and reduces the number of expanded nodes by 53.77%, 71.20%, and 74.30%, respectively. Moreover, the accumulated turning angles in the path are reduced by 70.48%, 76.31%, and 82.18%, respectively. The improved A~* algorithm effectively enhances the efficiency of mobile robot path planning, resulting in smoother and safer paths, especially in complex environments.更多还原