In order to solve the problems such as local minima and slow convergence rate in the process of path planning,an improved adaptive step size RRT* algorithm ( KNN-RRT*) based on the asymptotically optimal fast extended random tree ( P-RRT*) of target gravity function is proposed. Firstly,AdaGrad meth-od is introduced to adjust the adaptive step coefficient on the basis of target gravity to reduce the problem of random point sampling falling into the local minimum. Secondly,KDTree is used to store nodes and quickly search for adjacent nodes with k proximity to improve the efficiency of the algorithm. The quality of the search path is optimized with cubic B-spline curves. Finally,experiments based on KNN-RRT* algorithm are carried out under different obstacle environments,and the experiments show that the path search time and path quality of the algorithm are significantly improved,and the stability of the algorithm is improved.
关键词
机械臂运动规划/渐进最优快速搜索随机树/避障规划/路径优化
Key words
motion planning of robotic arm/fast search random tree/obstacle avoidance planning/path optimization
万方数据