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Authors: Nadia Schillreff and Frank Ortmeier

Affiliation: Chair of Software Engineering, Otto-von-Guericke-University Magdeburg, Germany

ISBN: 978-989-758-442-8

ISSN: 2184-2809

Keyword(s): Modeling, Parameter Identification, Calibration.

Abstract: In this work a reduced error model for a learning-based robot kinematic calibration of a serial manipulator is compared with a complete error model. To ensure high accuracy this approach combines the geometrical (structural inaccuracies) and non-geometrical influences like for e.g. elastic deformations that are configuration-dependent without explicitly defining all underlying physical processes that contribute to positioning inaccuracies by using a polynomial regression method. The proposed approach is evaluated on a dataset obtained using a 7-DOF manipulator KUKA LBR iiwa 7. The experimental results show the reduction of the mean Cartesian error up to 0.16 mm even for a reduced error model.

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Paper citation in several formats:
Schillreff, N. and Ortmeier, F. (2020). Reduced Error Model for Learning-based Calibration of Serial Manipulators. In Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - ICINCO, ISBN 978-989-758-442-8; ISSN 2184-2809, pages 478-483. DOI: 10.5220/0009835804780483

@conference{icinco20,
author={Nadia Schillreff. and Frank Ortmeier.},
title={Reduced Error Model for Learning-based Calibration of Serial Manipulators},
booktitle={Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - ICINCO,},
year={2020},
pages={478-483},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009835804780483},
isbn={978-989-758-442-8},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - ICINCO,
TI - Reduced Error Model for Learning-based Calibration of Serial Manipulators
SN - 978-989-758-442-8
IS - 2184-2809
AU - Schillreff, N.
AU - Ortmeier, F.
PY - 2020
SP - 478
EP - 483
DO - 10.5220/0009835804780483

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