The paper deals with model-free error compensation for cable-driven parallel robots based on the sim2real deep transfer learning. Particular attention is paid to simulation-based error estimation for different payloads attached to the robot end-effector and the use of the Transfer Learning approach for error compensation. This allows to reduce physical experiments with a real robot and gather sufficient data set within a reasonable time, which is required for deep learning. The obtained results were applied and validated for underactuated 4-dof (degrees of freedom) cable-driven parallel robot. Model-free Deep learning-based methods for a considerable training dataset provides better accuracy than simple linear error compensators using model-based calibration procedure. The proposed sim2real Transfer Learning method allowed to speed up the process of robotics system integration and recalibration due to the significant sample efficiency improvement.
Model Free Error Compensation for Cable-Driven Robot Based on Deep Learning with Sim2real Transfer Learning
Lect. Notes Electrical Eng.
International Conference on Informatics in Control, Automation and Robotics ; 2020 ; Lieusaint - Paris, France July 07, 2020 - July 09, 2020
01.01.2022
18 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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