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

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 nongeometric errors. To address this critical issue, this article proposes an e xtended Kalman filter-incorporated R esidual Neural Network-based C alibration (ERC) model for kinematic calibration. Its main ideas are two-fold: 1) adopting an e xtended Kalman filter (EKF) to address a manipulator's geometric errors; and 2) adopting a r esidual neural network to cascade with the EKF for eliminating the remaining nongeometric 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.