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.


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    Titel :

    Model Free Error Compensation for Cable-Driven Robot Based on Deep Learning with Sim2real Transfer Learning


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Gusikhin, Oleg (Herausgeber:in) / Madani, Kurosh (Herausgeber:in) / Zaytoon, Janan (Herausgeber:in) / Akhmetzyanov, Aydar (Autor:in) / Rassabin, Maksim (Autor:in) / Maloletov, Alexander (Autor:in) / Fadeev, Mikhail (Autor:in) / Klimchik, Alexandr (Autor:in)

    Kongress:

    International Conference on Informatics in Control, Automation and Robotics ; 2020 ; Lieusaint - Paris, France July 07, 2020 - July 09, 2020



    Erscheinungsdatum :

    01.01.2022


    Format / Umfang :

    18 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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