基于自适应启发函数和逆向寻优策略的改进 A* 移动机器人路径规划算法

Improved A* Mobile Robot Path Planning Algorithm Based on Adaptive Heuristic Function and Reverse Optimization Strategy

摘要:

移动机器人大多数情况都是在室外和室内障碍物环境下进行移动；因此,在这些障碍物环境中,高效率、短路径和少转折点的路径规划算法对移动机器人导航至关重要；针对在室外和室内障碍物环境下 A* 算法无法同时保持高效率、短路径和少转折点的问题,提出了一种基于自适应启发函数和逆向寻优策略的改进 A* 算法；通过增加自适应权重系数、引入父节点的影响力并对搜索方向进行筛选,减少了搜索面积,提高了搜索效率；采用逆向寻优策略对路径进行进一步优化,缩短了路径长度,减少了转折点数量；为了评估改进 A* 算法的性能,在仿真实验中设置常见的室外和室内障碍物环境并与 A* 算法对比；仿真实验结果表明,改进 A* 算法在效率、路径长度和转折点数量方面具有显著优势,能够有效地应用于移动机器人的导航中。

Abstract:

Mobile robots often operate in both outdoor and indoor environments with obstacles. Therefore, in these environments, path planning algorithms that are efficient, short, and have fewer turning points are crucial for mobile robot navigation. To address the issue that the A* algorithm cannot maintain high efficiency, short paths, and few turning points simultaneously in outdoor and indoor environments with obstacles, an improved A* algorithm based on adaptive heuristic function and reverse optimization strategy is proposed. By increasing the adaptive weight coefficient, introducing the influence of the parent node, and filtering the search direction, the search area is reduced, and the search efficiency is improved. A reverse optimization strategy is used to further optimize the path, shorten the path length, and reduce the number of turning points. To evaluate the performance of the improved A* algorithm, common outdoor and indoor obstacle environments are set up in the simulation experiment and compared with the A* algorithm. The simulation experiment results show that the improved A* algorithm has significant advantages in efficiency, path length, and the number of turning points, and can be effectively applied to the navigation of mobile robots.