基于改进RRT*FN的移动机器人路径规划算法

针对复杂环境下移动机器人的全局最优路径规划，提出一种基于目标偏置扩展和贝塞尔(Bezier)插值方法的改进RRT*FN路径规划算法.改进算法在未找到初始路径时采用一定概率进行随机点的目标偏置选择，确定初始路径后使用启发式采样方法，使随机采样点围绕初始路径进行迭代选择，提高路径规划的导向性.当改进算法还未找到初始路径时，删除树中远离目标点并且没有子节点的节点；当改进算法找到初始路径时，删除树中远离最优路径且没有子节点的节点，保留高性能节点，提高算法收敛到最优路径的效率.利用贝塞尔(Bezier)插值方法平滑路径.在MATLAB仿真平台和ROS机器人仿真平台分别进行2D和3D的对比实验，结果验证了所提算法的有效性和优越性.

Mobile Robot Path Planning Algorithm Based on Improved RRT*FN

Aiming at the global optimal path planning of mobile robots in complex environments， an improved RRT*FN path planning algorithm based on target bias extension and Bezier interpolation method is proposed. The improved algorithm adopts a certain probability to select the target bias of random points when the initial path is not found. After determining the initial path， the heuristic sampling method is used to make random sampling points iteratively select around the initial path， which improves the guidance of path planning. When the improved algorithm has not found the initial path， the nodes far from the target point and no child nodes in the tree are deleted. When the improved algorithm finds the initial path， the nodes far from the optimal path and without child nodes in the tree are deleted， and the high-performance nodes are reserved to improve the efficiency of algorithm convergence to the optimal path. The path is smoothed by Bezier interpolation. 2D and 3D comparative experiments were carried out on MATLAB simulation platform and ROS robot simulation platform respectively. The effectiveness and superiority of the proposed algorithm are verified.