Reachability Based Trajectory Generation Combining Global Graph Search in Task Space and Local Optimization in Configuration Space

In this paper, we propose a trajectory planning framework for a robot that exploits a pre-computed database of end-effector trajectories as the guidance of optimization-based inverse kinematics. We constructed a reachable graph of a robot offline, which represents feasible end-effector paths with corresponding configurations. When performing the online trajectory planning, we applied A* search to the reachable graph to find a feasible path between input start and goal globally in the task space. Its cost function has the separated term dependent on the robot, which comes from the manipulability of configurations preserved in the reachable graph, and that is dependent on the environment. Then, we solve optimization-based inverse kinematics to generate an optimal joint trajectory while utilizing the end-effector trajectory and its corresponding configurations as the guidance to avoid local optimum. We evaluated our framework quantitatively by comparing it with existing methods to confirm that it achieved a high success rate and quality of results while suppressing its computational time. We also qualitatively proved its practicality by applying it to the material handling task in the real-world. This result shows that it improved the performance of the optimization-based inverse kinematics avoiding local optimum and applicability to the different environments of the pre-computed motion database.