Static calibration and dynamic compensation of the SCORBOT robot using sensor fusion and LSTM networks

Abstract

ABSTRACT This paper presents both static calibration and dynamics compensation to reduce the positioning errors of the SCORBOT robot. First, a sensor fusion scheme is proposed to estimate the position and attitude of the end-effector of a robot instead of using laser trackers or coordinate measuring machines. The scheme integrates an extended Kalman filter (EKF) with the models of an inertial measurement unit (IMU) and a depth camera. Second, a static calibration scheme is presented to reduce the mechanism errors of robots. The scheme modifies the Denavit-Hartenberg (D-H) parameters provided by the manufacturer based on the least squares method. Third, a dynamic compensation scheme is proposed to reduce the errors caused by robot motions. The scheme establishes a long short-term memory (LSTM) network to compensate the joint angles, where the robot dynamics is integrated into the scheme. Finally, both simulations and experiments are performed to validate the proposed schemes.