Repetitive Path Planning with Experience-Based Bidirectional RRT

Abstract

Repetitive motion planning in semi-structured environments is involved in many robotics applications, such as manufacturing and in-orbit service of spatial robotic arms. In these scenarios, the environment is mainly static, but it may contain some movable obstacles that the robot needs to avoid, and the planning problems that the robot will encounter are very similar. However, most current sampling-based motion planning methods ignore the correlation among tasks, and instead plan each task instance from scratch, or assume that the environment is highly structured. In order to overcome this problem, this paper proposes an experience-based bidirectional rapidly-exploring random trees* (EBRRT*) for repetitive path planning in semi-structured environments. First, an adaptive sampler learned from the demonstration paths by a Gaussian mixture model (GMM) is constructed, which can guide the planner to generate samples in task-related regions. Second, an experience graph is proposed to capture the exploration of configuration space by previous similar tasks. Subsequent tasks can retrieve locally valuable information from the graph to avoid exploring the configuration space repeatedly. In addition, the validity of edges contained in the experience graph is lazily verified during the planning process so that the experience graph can adapt to small environmental changes. Finally, the proposed method is verified in a 2D simulation environment and a 7-DOF manipulator scene. Experimental results show that the proposed method can return a high-quality path in less time than other state-of-the-art sample-based motion planning for repetitive path planning in semi-structured environments.