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An Acceleration-Level Data-Driven Repetitive Motion Planning Scheme for Kinematic Control of Robots With Unknown Structure
Xie, Zhengtai1; Jin, Long1; Luo, Xin2; Hu, Bin1; Li, Shuai1
2021-12-06
摘要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.
关键词Robots Manipulators Service robots Kinematics Recurrent neural networks Planning Redundancy Acceleration level data-driven technology kinematic control of robots recurrent neural network (RNN) repetitive motion planning (RMP)
DOI10.1109/TSMC.2021.3129794
发表期刊IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
ISSN2168-2216
页码13
通讯作者Hu, Bin(bh@lzu.edu.cn)
收录类别SCI
WOS记录号WOS:000732097600001
语种英语