Reduced Error Model for Learning-based Calibration of Serial Manipulators

Nadia Schillreff, Frank Ortmeier

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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