An Error-Tolerant Design of Joint Planner for a Table Tennis Robot Using Variable-Sigmoid-Based Motion Template
针对乒乓球机器人运动规划时间短、需容忍球拍运动不确定性和关节限位两类误差的问题，提出一种基于可变S型运动模板的联合规划框架，实现了98%的平均规划成功率和90%的击球成功率，执行时间仅15毫秒。
Efficient motion planning with the error tolerance is crucial for dynamic robotic tasks, particularly robotic table tennis. This task demands simultaneous high efficiency and error tolerance. First, the incoming ball’s high speed allows only tens of milliseconds for motion planning. Second, two types of errors, namely, ball-paddle motion uncertainty and joint limit violation, must be tolerated to ensure a high success rate of planning (SRP) and striking. This article proposes an advanced joint planning framework designed for high efficiency and error tolerance. To tolerate the error of ball-paddle motion uncertainty, this work introduces a joint classification criterion according to the joint motion characteristics. To tolerate the error of joint limit violation, based on the classification criterion, this study also develops a robust reference trajectory generation scheme, named error-tolerant-variable-sigmoid-based motion template (ETVSMT), to fully consider the motion capabilities of different joints. The ETVSMT approach utilizes the variable-sigmoid-based motion template (VSMT) as the backbone and designs its basic and remedial portions to tolerate the error of hard joint limit violation. The implementation of the ETVSMT scheme results in an average SRP of 98% and a ball-striking success rate of up to 90%, with execution time on an Intel Xeon CPU as low as 15 ms. Furthermore, the proposed method ensures the outgoing ball lands on the opposite side of the table with an average landing error of 19.62 cm and a net-passing height error of 12.68 cm. The proposed method can also benefit other dynamic tasks like human–robot interaction to improve the planning efficiency.