Efficient Industrial Robot Calibration via a Novel Unscented Kalman Filter-Incorporated Variable Step-Size Levenberg–Marquardt Algorithm

Abstract

Robots facilitate a critical category of equipments to implement intelligent production. However, due to extensively inevitable factors like structural errors and gear tolerances, the positioning error of an industrial robot is several millimeters, therefore failing in fulfilling the high-precision manufacture requirements. To address the critical problem, this work develops a novel calibration algorithm incorporates an Unscented Kalman Filter and a Variable Step-size Levenberg-Marquardt (UKF-VSLM) algorithm for efficient industrial robot calibration with the following two-fold ideas: a) developing a novel Variable Step-size Levenberg-Marquardt algorithm to address the local optimum issues encountered by a standard Levenberg-Marquardt algorithm; and b) incorporating an unscented Kalman filter into the proposed Variable Step-size Levenberg-Marquardt algorithm to suppressing the measurement noises during the calibration process. Empirical studies on an HSR JR680 industrial robot demonstrate that compared with state-of-the-art calibration algorithms, the calibration accuracy of the developed UKF-VSLM is 19.51% higher than that of the most accurate Levenberg-Marquardt algorithm measured by the maximum error. The empirical results strongly support the superior performance of the proposed algorithm in addressing robot calibration issues.