Deep Learning with Transfer Learning Method for Error Compensation of Cable-driven Robot

Aydar Akhmetzyanov, Maksim Rassabin, Alexander Maloletov, Mikhail Fadeev, Alexandr Klimchik

2020

Abstract

This paper proposes the application of Deep Learning methods for kinematic error compensation. Particular attention is paid to simulation-based error estimation and the use of the Transfer Learning method for error compensation to reduce physical experiments with a real robot. The obtained results were applied and validated for 4-dof (degrees of freedom) cable-driven parallel robot. The problem of error compensation for the cable-driven parallel robot is highly non-linear. Nevertheless, deep learning-based methods for a considerable training dataset provides better accuracy than simple linear error compensators. To overcome this drawback, we applied the transfer learning method and used the knowledge of robot kinematics simulated in Unity. Unity cable-driven robot simulation was implemented, and the central hypothesis was verified first in the simulated environment. The proposed Transfer Learning method allowed to speed up the process of robotics system integration and recalibration due to the significant sample efficiency improvement.

Paper Citation

in Harvard Style

Akhmetzyanov A., Rassabin M., Maloletov A., Fadeev M. and Klimchik A. (2020). Deep Learning with Transfer Learning Method for Error Compensation of Cable-driven Robot.In Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-442-8, pages 553-559. DOI: 10.5220/0009905605530559

in Bibtex Style

@conference{icinco20,
author={Aydar Akhmetzyanov and Maksim Rassabin and Alexander Maloletov and Mikhail Fadeev and Alexandr Klimchik},
title={Deep Learning with Transfer Learning Method for Error Compensation of Cable-driven Robot},
booktitle={Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2020},
pages={553-559},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009905605530559},
isbn={978-989-758-442-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Deep Learning with Transfer Learning Method for Error Compensation of Cable-driven Robot
SN - 978-989-758-442-8
AU - Akhmetzyanov A.
AU - Rassabin M.
AU - Maloletov A.
AU - Fadeev M.
AU - Klimchik A.
PY - 2020
SP - 553
EP - 559
DO - 10.5220/0009905605530559