Highly Accurate Manipulator Calibration via Extended Kalman Filter-Incorporated Residual Neural Network

Abstract

With the rapid development and wide applications of industrial manipulators, a vital concern rises regarding a manipulator's absolute positioning accuracy. The manipulator calibration models have proven to be highly efficient in improving the absolute positioning accuracy of an industrial manipulator. However, existing calibration models commonly suffer from the low calibration accuracy caused by the ignorance of non-geometric errors. To address this critical issue, this paper proposes an <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> xtended Kalman Filter-incorporated <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> esidual Neural Network-based <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> alibration (ERC) model for kinematic calibration. Its main ideas are two-fold: a) adopting an <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</u> xtended Kalman filter to address a manipulator's geometric errors; and b) adopting a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</u> esidual neural network to cascade with the <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</u> xtended Kalman filter for eliminating the remaining non-geometric errors. Detailed experiments on three real datasets collected from industrial manipulators demonstrate that the proposed ERC model has achieved significant calibration accuracy gain over several state-of-the-art models.