EB-RRT: Optimal Motion Planning for Mobile Robots

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.