最优快速拓展随机树 ( RRT* ) 是一种渐进最优的移动机器人路径规划方法， Quick-RRT* 缩短了 RRT* 的初始路径长度，提高了路径收敛速度。为进一步提高 QuickRRT* 的收敛速度，文中提出了一种双树 Quick-RRT* 算法。首先，基于 Quick-RRT* 算 法在起点和终点分别生成一棵随机树，起点树和终点树轮流生长，两棵树的连接采用贪 婪法; 然后，对提出的算法的概率完备性和渐进最优性进行理论分析，证明了算法的概 率完备性和渐进最优性; 最后，基于 Matlab 平台，在 3 种环境下采用双树 Quick-RRT* 与 RRT* 、Quick-RRT* 和双向 RRT* 算法进行了对比仿真实验。结果表明，文中改进的 算法不仅可以在更短的时间内找到初始路径和次优路径，而且初始路径更短。

The optimal rapid expansion randomized tree ( RRT* ) is an asymptotically optimal path planning method for mobile robots． Quick-RRT* reduces the initial path length of RRT* and increases the path convergence speed． In order to further improve the convergence speed of Quick-RRT* ，this paper proposed a dual-tree Quick-RRT* ( Quick-RRT* -Connect) algorithm． Firstly，two random trees were generated at the start and end points respectively based on the Quick-RRT* algorithm． Two trees grew in turn and they were connected with greedy method． Then， the probability completeness and asymptotic optimality of the proposed algorithm were analyzed and testified． Finally，based on the Matlab platform，Quick-RRT* -Connect was compared with RRT* ，Quick-RRT* and RRT* - Connect in three environments． The results show that the improved algorithm can not only find initial path and suboptimal path in a shorter time，but also reduce the initial path length．