期刊
IEEE ACCESS
卷 8, 期 -, 页码 105447-105454出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.2999927
关键词
Neural network; robot accuracy; robot calibration; teaching-learning-based optimization
资金
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [2019R1D1A3A03103528]
- National Research Foundation of Korea [2019R1D1A3A03103528] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This paper proposes a new calibration method for enhancing robot positional accuracy of the industrial manipulators. By combining the joint deflection model with the conventional kinematic model of a manipulator, the geometric errors and joint deflection errors can be considered together to increase its positional accuracy. Then, a neural network is designed to additionally compensate the unmodeled errors, specially, non-geometric errors. The teaching-learning-based optimization method is employed to optimize weights and bias of the neural network. In order to demonstrate the effectiveness of the proposed method, real experimental studies are carried out on HH 800 manipulator. The enhanced position accuracy of the manipulator after the calibration confirms the feasibility and more positional accuracy over the other calibration methods.
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