Nadia Schillreff, Frank Ortmeier: Learning-based Kinematic Calibration using Adjoint Error Model . In: SciTePress, (Hrsg.): Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, 2018, ISBN: 978-989-758-321-6.

Abstract

A learning-based robot kinematic calibration approach based on the product-of-exponentials (POE) formula and Adjoint error model is introduced. To ensure high accuracy this approach combines the geometrical and non-geometrical influences like for e.g. elastic deformations without explicitly defining all physical processes that contribute to them using a polynomial regression method. By using the POE formula for kinematic modeling of the manipulator it is ensured that kinematic parameters vary smoothly and used method is robust and singularity-free. The introduced error parameters are presented in the form of Adjoint transformations on nominal joint twists. The calibration process then becomes finding a set of polynomial functions using regression methods that are able to reflect the actual kinematics of the robot. The proposed method is evaluated on a dataset obtained using a 7-DOF manipulator (KUKA LBR iiwa 7 R800). The experimental results show that this approach significantly reduc es positional errors of the robotic manipulator after calibration.

BibTeX (Download)

@inproceedings{Schillreff2018,
title = { Learning-based Kinematic Calibration using Adjoint Error Model },
author = {Nadia Schillreff and Frank Ortmeier},
editor = {SciTePress},
doi = {10.5220/0006870403820389},
isbn = {978-989-758-321-6},
year  = {2018},
date = {2018-08-01},
booktitle = {Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
abstract = {A learning-based robot kinematic calibration approach based on the product-of-exponentials (POE) formula and Adjoint error model is introduced. To ensure high accuracy this approach combines the geometrical and non-geometrical influences like for e.g. elastic deformations without explicitly defining all physical processes that contribute to them using a polynomial regression method. By using the POE formula for kinematic modeling of the manipulator it is ensured that kinematic parameters vary smoothly and used method is robust and singularity-free. The introduced error parameters are presented in the form of Adjoint transformations on nominal joint twists. The calibration process then becomes finding a set of polynomial functions using regression methods that are able to reflect the actual kinematics of the robot. The proposed method is evaluated on a dataset obtained using a 7-DOF manipulator (KUKA LBR iiwa 7 R800). The experimental results show that this approach significantly reduc es positional errors of the robotic manipulator after calibration.},
keywords = {Modeling, Parameter Identification},
pubstate = {published},
tppubtype = {inproceedings}
}