The efficiency of sampling-based motion planning brings wide application in autonomous mobile robots. The conventional rapidly exploring random tree (RRT) algorithm and its variants have gained significant successes, but there are still challenges for the optimal motion planning of mobile robots in dynamic environments. In this paper, based on Bidirectional RRT and the use of an assisting metric (AM), we propose a novel motion planning algorithm, namely Bi-AM-RRT*. Different from the existing RRT-based methods, the AM is introduced in this paper to optimize the performance of robot motion planning in dynamic environments with obstacles. On this basis, the bidirectional search sampling strategy is employed to reduce the search time. Further, we present a new rewiring method to shorten path lengths. The effectiveness and efficiency of the proposed Bi-AM-RRT* are proved through comparative experiments in different environments. Experimental results show that the Bi-AM-RRT* algorithm can achieve better performance in terms of path length and search time, and always finds near-optimal paths with the shortest search time when the diffusion metric is used as the AM.

翻译：基于采样的运动规划的效率为自主移动机器人带来了广泛应用。传统的快速探索随机树算法及其变体已取得显著成功，但在动态环境中移动机器人的最优运动规划仍面临挑战。本文基于双向快速探索随机树并引入辅助度量，提出了一种新颖的运动规划算法，即Bi-AM-RRT*。与现有基于快速探索随机树的方法不同，本文引入了辅助度量以优化机器人在含障碍物的动态环境中的运动规划性能。在此基础上，采用双向搜索采样策略以减少搜索时间。此外，我们提出了一种新的重连方法以缩短路径长度。通过在不同环境下的对比实验，证明了所提出的Bi-AM-RRT*算法的有效性和高效性。实验结果表明，Bi-AM-RRT*算法在路径长度和搜索时间方面均能实现更优性能，并且当采用扩散度量作为辅助度量时，始终能以最短的搜索时间找到近乎最优的路径。