Nadia Schillreff, Frank Ortmeier: Reduced Error Model for Learning-based Calibration of Serial Manipulators. In: SciTePress, (Hrsg.): Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, 2020, ISBN: 978-989-758-442-8.

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.

BibTeX (Download)

@inproceedings{Schillreff2020,
title = {Reduced Error Model for Learning-based Calibration of Serial Manipulators},
author = {Nadia Schillreff and Frank Ortmeier},
editor = {SciTePress},
doi = {10.5220/0009835804780483},
isbn = { 978-989-758-442-8},
year  = {2020},
date = {2020-07-07},
booktitle = {Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
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.},
keywords = {robotics},
pubstate = {published},
tppubtype = {inproceedings}
}