The efficiency of sampling-based motion planning brings wide application in autonomous mobile robots. Conventional rapidly exploring random tree (RRT) algorithm and its variants have gained great successes, but there are still challenges for the real-time optimal motion planning of mobile robots in dynamic environments. In this paper, based on Bidirectional RRT (Bi-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, in order to increase the planning efficiency. Further, we present an improved 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.

翻译：采样型运动规划的高效性使其在自主移动机器人领域得到广泛应用。传统的快速探索随机树（RRT）算法及其变体已取得显著成功，但在动态环境下实现移动机器人实时最优运动规划仍面临挑战。本文基于双向RRT（Bi-RRT）并引入辅助度量（AM），提出了一种新型运动规划算法——Bi-AM-RRT*。与现有基于RRT的方法不同，本文引入AM以优化存在障碍物的动态环境中机器人运动规划的性能。在此基础上，采用双向搜索采样策略以提高规划效率。进一步，我们提出了一种改进的重连方法以缩短路径长度。通过不同环境下的对比实验验证了所提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。