In a human–robot coexisting environment, it is pivotal for a mobile service robot to arrive at the goal position safely and efficiently. In this article, an elastic band-based rapidly exploring random tree (EB-RRT) algorithm is proposed to achieve real-time optimal motion planning for the mobile robot in the dynamic environment, which can maintain a homotopy optimal trajectory based on current heuristic trajectory. Inspired by the EB method, we propose a hierarchical framework consisting of two planners. In the global planner, a time-based RRT algorithm is used to generate a feasible heuristic trajectory for a specific task in the dynamic environment. However, this heuristic trajectory is nonoptimal. In the dynamic replanner, the time-based nodes on the heuristic trajectory are updated due to the internal contraction force and the repulsive force from the obstacles. In this way, the heuristic trajectory is optimized continuously, and the final trajectory can be proved to be optimal in the homotopy class of the heuristic trajectory. Simulation experiments reveal that compared with two state-of-the-art algorithms, our proposed method can achieve better performance in dynamic environments. Note to Practitioners —The motivation of this work stems from the need to achieve real-time optimal motion planning for the mobile robot in the human–robot coexisting environment. Sampling-based algorithms are widely used in this area due to their good scalability and high efficiency. However, the generated trajectory is usually far from optimal. To obtain an optimized trajectory for the mobile robot in the dynamic environment with moving pedestrians, we propose the EB-RRT algorithm on the basis of the time-based RRT tree and the EB method. Depending on the time-based RRT tree, we quickly get a heuristic trajectory and guarantee the probabilistic completeness of our algorithm. Then, we optimize the heuristic trajectory similar to the EB method, which achieves the homotopy optimality of the final trajectory. We also take into account the nonholonomic constraints, and our proposed algorithm can be applied to most mobile robots to further improve their motion planning ability and the trajectory quality.

中文翻译：

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EB-RRT：移动机器人的最佳运动计划

在人机共存的环境中，移动服务机器人安全有效地到达目标位置至关重要。本文提出了一种基于弹性带的快速探索随机树算法（EB-RRT），以实现动态环境中移动机器人的实时最优运动规划，该算法可以在当前启发式轨迹的基础上保持同伦最优轨迹。 。受EB方法的启发，我们提出了一个由两个计划者组成的分层框架。在全局计划程序中，基于时间的RRT算法用于为动态环境中的特定任务生成可行的启发式轨迹。但是，这种启发式轨迹不是最佳的。在动态重新规划器中，启发式轨迹上的基于时间的节点由于内部收缩力和来自障碍物的排斥力而更新。以此方式，启发式轨迹被连续优化，并且最终轨迹可以被证明在启发式轨迹的同伦类中是最优的。仿真实验表明，与两种最先进的算法相比，我们提出的方法可以在动态环境中实现更好的性能。执业者注意 —这项工作的动机源于需要在人机共存环境中为移动机器人实现实时最佳运动计划。基于采样的算法具有良好的可扩展性和高效率，因此在该领域得到了广泛的应用。但是，生成的轨迹通常远非最佳。为了在行人运动的动态环境中获得移动机器人的最优轨迹，我们在基于时间的RRT树和EB方法的基础上提出了EB-RRT算法。依靠基于时间的RRT树，我们可以快速获得启发式轨迹并保证算法的概率完整性。然后，我们类似于EB方法优化启发式轨迹，从而实现了最终轨迹的同伦最优性。我们还考虑了非完整约束，