关键词: 批量通知树算法, 反向生长搜索树, 批量采样点策略, 启发式函数, 快速扩展随机树（RRT）算法, 路径重规划

Abstract: Aiming at the problems of low success rate in searching paths in small samples, low planning efficiency in large samples, many redundant nodes in paths and inability to avoid unknown obstacles, a dynamic environment batch informed trees algorithm is proposed. The improved batch sampling point strategy is used to process the sample points evenly and equally spaced, and the number of batch sampling points and the location of biased sampling points are improved to make up for the low success rate of the search path. Adding a penalty term to improve the heuristic function makes up for the low efficiency of path planning. Then the path stretching optimization is introduced to reduce the path length and redundant nodes and narrow the sampling range. In the face of unknown obstacles, a temporary target point selection strategy is proposed by using the prior information of the reverse growth search tree, and the RRT algorithm of random points, turning angles and new nodes is improved to avoid excessive deviation of the replanned path and timely avoidance. Compared with other algorithms, the results show that batch notification tree algorithm in dynamic environment has higher success rate and efficiency, less path length and number of inflection points, higher performance of avoiding unknown obstacles, and closer to the global path.

Key words: batch informed trees algorithm, reverse growth search tree, batch sampling point strategy, heuristic function, rapidly exploring random tree(RRT) algorithm, path replanning