Reduced Error Model for Learning-based Calibration of Serial Manipulators

Nadia Schillreff, Frank Ortmeier

2020

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.

Paper Citation

in Harvard Style

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 - Volume 1: ICINCO, ISBN 978-989-758-442-8, pages 478-483. DOI: 10.5220/0009835804780483

in Bibtex Style

@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 - Volume 1: ICINCO,},
year={2020},
pages={478-483},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009835804780483},
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 - Reduced Error Model for Learning-based Calibration of Serial Manipulators
SN - 978-989-758-442-8
AU - Schillreff N.
AU - Ortmeier F.
PY - 2020
SP - 478
EP - 483
DO - 10.5220/0009835804780483