Obstacle space modeling and moving-window RRT for manipulator motion planning

摘要

A fast motion planner is presented for manipulator pick-and-place operation in cluttered workspace. This planner consists of two subalgorithms which are proposed to enhance the computational efficiency. First, based on the discretization and extraction of boundary surface from obstacle space, a new obstacle space modeling method is introduced to carry out the collision check promptly. Second, an optimized path searching subalgorithm named moving-window rapidly-exploring random tree is presented. The moving-window unilateral Gaussian random sampling strategy is used for fast convergence to the goal state in different environment. Finally, the proposed motion planner is simulated on a PUMA560 robot manipulator. The simulation results demonstrate that the proposed method enables real-time motion planning and can be implemented conveniently. More importantly, compared to the conventional methods, the developed method requires less storage space for obstacle space model, executes only once to check the collision rapidly for each segmented path. Besides, the stable performance in path searching offers higher robustness for the motion planner.