融合改进A*算法和Morphin算法的移动机器人动态路径规划

在动态未知环境下对机器人进行路径规划，传统A*算法可能出现碰撞或者路径规划失败问题。为了满足移动机器人全局路径规划最优和实时避障的需求，提出一种改进A*算法与Morphin搜索树算法相结合的动态路径规划方法。首先通过改进A*算法减少路径规划过程中关键节点的选取，在规划出一条全局较优路径的同时对路径平滑处理。然后基于移动机器人传感器采集的局部信息，利用Morphin搜索树算法对全局路径进行动态的局部规划，确保更好的全局路径的基础上，实时避开障碍物行驶到目标点。MATLAB仿真实验结果表明，提出的动态路径规划方法在时间和路径上得到提升，在优化全局路径规划的基础上修正局部路径，实现动态避障提高机器人达到目标点的效率。

Mobile-robot dynamic path planning based on improved A* and Morphin algorithms

The traditional A* algorithm can experience collisions or path-planning failure in dynamic complicated environments. To meet global optimal requirements and achieve real-time obstacle avoidance in mobile-robot path planning, we propose a novel method that fuses an improved A* algorithm with a Morphin search tree algorithm. First, we improved the A* algorithm by reducing the selection of key nodes in the path-planning process and performing path smoothing when planning the global optimal path. Then, based on the local information obtained by the mobile-robot sensor, the Morphin search tree algorithm is used to dynamically localize the global path. Thus, obstacles are avoided both by ensuring a better global path and by real-time obstacle avoidance as the robot moves to the target. The MATLAB simulation results show that the proposed dynamic path-planning method improves both the time and path. The local path is corrected via the optimized global-path planning, dynamic obstacle avoidance, and the improved efficiency with which the robot reaches the target point.