期刊
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
卷 52, 期 9, 页码 5679-5691出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2021.3129794
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)
资金
- National Natural Science Foundation of China [62176109]
- National Key Research and Development Program of China [2017YFE0118900]
- CIE-Tencent Robotics X Rhino-Bird Focused Research Program [2021-01]
- Natural Science Foundation of Chongqing, China [cstc2020jcyjzdxmX0028]
- Chinese Academy of Sciences Light of West China Program
- Natural Science Foundation of Gansu Province [21JR7RA531, 20JR10RA639]
- Special Projects of the Centra Government in Guidance of Local Science and Technology Development
- Gansu Provincial Youth Doctoral Fund of Colleges and Universities [2021QB-003]
- Fundamental Research Funds for the Central Universities [lzujbky2021-it35, lzujbky-2021-65]
The study introduces an acceleration-level data-driven repetitive motion planning (DDRMP) scheme that combines structural learning and robot control, and demonstrates its feasibility through theoretical analysis and simulative experiments.
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.
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