基于改进RRT*FN算法的机械臂多场景运动规划

针对固定节点数的渐进最优快速扩展随机树(RRT*FN)算法精度低、对环境缺乏适应性等问题，提出了一种改进RRT*FN的机械臂运动规划算法。在迭代过程中，结合目标偏向随机采样和椭球子集采样的优势，构造新的启发式方法对采样区域进行约束，从而保证搜索路径更优。在扩展节点时，配置树中总节点数的预设值，并通过加权方法对树中叶子节点进行删减，避免了树规模的无限增长。在动态环境下，采用对节点剪枝与连接的启发式重规划方法，有效提高了对动态环境的适应能力。实验结果表明，该算法在规划过程中收敛速度更快，效率更高，具有较强的环境适应性。

Multi-scene Motion Planning of Manipulators Based on Improved RRT*FN Algorithm

Aiming at the problems of low precision, poor environmental adaptability of RRT*FN algorithm, an improved RRT*FN motion planning algorithm for manipulators was proposed. Firstly, in the iteration processes, combining the advantages of target biased random sampling and ellipsoid subset sampling, a new heuristic method was constructed to constrain the sampling areas, so as to ensure better search paths. Then, in expanding node processes, the preset values of total number of nodes in the tree were configured, and the leaf nodes in the tree were deleted by weighting method, which avoided the infinite growth of tree sizes. Finally, under the dynamic environment, a heuristic replanning method of node pruning and connection was adopted to effectively improve the adaptability of dynamic environment. Experimental results show that the algorithm herein has faster convergence speed and higher efficiency in planning processes, and has strong environmental adaptability.