Workspace-Guided Rapidly-Exploring Random Tree Method for a Robot Arm

Motion planning for robotic arms is important for real, physical world applications. The planning for arms with high degree-of-freedom (DOF) is hard because its search space is large (exponential in the number of joints), and the links may collide with static obstacles or its joints (self-collision). In this paper we present a factor-guided sampling based motion planning algorithm that finds plans of motion from one arm configuration to a goal arm configuration in 2D and 3D space. Our algorithm finds a workspace-based roadmap with utilizing the locations of end-effector. Then, with the roadmap, a sampling based motion planner (RapidlyExploring Random Tree [1]) finds a configuration space (CSpace) based roadmap. The RRT is operated as a single-query mode which have no pre-planned roadmap. Our algorithm is unique in two ways: (a) it takes only polynomial time in the number of joints to find a workspace-based roadmap; and (b)it utilizes the topology of the arm and obstacles to factor the search space reduce the complexity of the planning problem using dynamic programming. Thus, our algorithm dramatically reduce the time to find a path between a start configuration and a goal configuration. The experimental results show that the proposed algorithm improves the performance of path planning for 2D. 1