Constraint-aware Online Joint-Space Motion Planning for Robotic Table Tennis

Abstract

In the research of table tennis robots, robot motion planning plays a crucial role as it determines the performance of striking balls back. However, to deal with the table tennis ball with high speed and spin, the robot system requires real-time motion planning with high success rate. In addition, due to the high-dimensional search space caused by the high degrees of freedom of robot motion and various types of collision constraints, searching for a feasible trajectory within a limited time is a complex problem. To address these issues, an optimization-based constraint-aware online joint-space motion planning algorithm is proposed to generate feasible trajectories achieving a high success rate and high efficiency. Specifically, a septic polynomial is employed to represent the trajectory of each joint in joint space, which can search for feasible solutions under multiple constraints while ensuring real-time computation. Five different types of constraints are considered during planning to generate a safe and feasible trajectory. Moreover, a fast remedial measure is applied to further enhance the success rate of motion planning. The proposed method achieves an average success rate over 97% in simulation and surpasses a 65% success rate on a real 5-degree-of-freedom robot system.