Abstract: Aiming at the problems of the RRT (rapidly exploring random tree) path planning algorithm, such as large search range, poor goal guidance, local minimum and tortuous path, an improved RRT path planning algorithm with limited adaptive sampling area is proposed. The search space is divided into uniform levels, and the sampling area is dynamically adjusted according to the level of new node and the number of sampling points in the level, so as to reduce the search range. The new node improvement strategy is used to make the random tree adjust to the target point adaptively according to the environment information and change the expansion step to generate new nodes. Then the obstacle avoidance strategy is used to increase the target orientation and obstacle avoidance performance of the algorithm. The improved inverse optimization and cubic B-spline curve which inserts nodes and reduces the turning angle are used to optimize the path. Compared with RRT algorithm, the search time and iteration times of this algorithm are reduced by more than 70% in different path environments, and the optimized path is shorter and smoother.

Key words: RRT path planning algorithm, sampling area, obstacle avoidance strategy, path planning, path optimization

摘要： 针对RRT（rapidly exploring random tree）路径规划算法搜索范围大、目标导向差、容易陷入局部最小值以及路径曲折等问题，提出了一种限制自适应采样区域的改进RRT路径规划算法。将整个搜索空间划分成均匀的等级，根据新节点所在等级和该等级内采样点数量动态调整采样区域，减小搜索范围；利用新节点改进策略使随机树根据环境信息自适应地向目标点调整，并改变扩展步长生成新节点；利用障碍物躲避策略提高算法的目标导向性和躲避障碍物的性能；利用改进的逆向寻优和插入节点并减小转向角的三次B样条曲线对路径进行优化处理。该算法在不同的路径环境中相较于RRT算法的搜索时间和迭代次数均减少了70%以上，且经过优化的路径更短、更平滑。

关键词: RRT路径规划算法, 采样区域, 障碍物躲避策略, 路径规划, 路径优化