基于地图预处理及改进A*算法的路径规划

Path planning based on map partition preprocessing and improved A*algorithm

针对移动机器人路径规划中栅格地图建模及A*算法搜索效率问题,设计了一种基于地图分区预处理及改进A*算法的路径规划。首先,基于K-Means聚类算法对栅格地图进行分区并量化各局部区域的复杂度;然后,改进A*算法的评价函数和子节点选择方式并依据地图区域的复杂度生成有效的搜索空间;最后,改进Floyd算法对路径进行双向平滑度优化处理,并通过添加防碰撞安全距离系数,使路径与障碍物保持安全距离。实验仿真结果表明,本文所设计的算法可提高A*算法的搜索效率和灵活性,增加路径的平滑度和安全性。

An path planning algorithm based on map partition preprocessing and improved A*algorithm is proposed for the problem of the raster map modeling and searching efficiency of A*algorithm for mobile robot.Firstly,the K-Means algorithm is applied to realize the partition of maps and quantify the complexity of each local area in the map.Then,the improved A*algorithm evaluation function and the selection mode of each sub-nodes are designed to generate the effective search space based on the complexity of region decomposition.Finally,the Floyd algorithm is introduced to solve the bi-directional smoothness optimization of search path,meanwhile,the path and the obstacle are kept at a safe distance by adding the anti-collision safety distance coefficient.Simulation experiment results show that the proposed algorithm can improve the search efficiency and flexibility of A*algorithm and increases the smoothness and security of the path.