An Acceleration-Level Data-Driven Repetitive Motion Planning Scheme for Kinematic Control of Robots With Unknown Structure

• Published in: IEEE transactions on systems, man, and cybernetics. Systems man, and cybernetics. Systems, 2022-09, Vol.52 (9), p.5679-5691
• Main Authors: Xie, Zhengtai, Jin, Long, Luo, Xin, Hu, Bin, Li, Shuai
• Format: Article
• Language: English
• Subjects: Acceleration level; data-driven technology; Feasibility; kinematic control of robots; Kinematics; Learning; Manipulators; Motion planning; Planning; recurrent neural network (RNN); Recurrent neural networks; Redundancy; repetitive motion planning (RMP); Robot control; Robot dynamics; Robots; Service robots; Simulation
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2021.3129794

It is generally considered that controlling a robot precisely becomes tough on the condition of unknown structure information. Applying a data-driven approach to the robot control with the unknown structure implies a novel feasible research direction. Therefore, in this article, as a combination of the structural learning and robot control, an acceleration-level data-driven repetitive motion planning (DDRMP) scheme is proposed with the corresponding recurrent neural network (RNN) constructed. Then, theoretical analyses on the learning and control abilities are provided. Moreover, simulative experiments on employing the acceleration-level DDRMP scheme as well as the corresponding RNN to control a Sawyer robot and a Baxter robot with unknown structure information are performed. Accordingly, simulation results validate the feasibility of the proposed method and comparisons among the existing repetitive motion planning (RMP) schemes indicate the superiority of the proposed method. This work offers sufficient theoretical and simulative solutions for the acceleration-level redundancy problem of redundant robots with unknown structure and joint limits considered.