Online Serial Manipulator Calibration Based on Multisensory Process Via Extended Kalman and Particle Filters

Abstract

An online robot self-calibration method based on an inertial measurement unit (IMU) and a position sensor is presented in this paper. In this method, a position marker and an IMU are required to be rigidly attached to the robot tool to obtain the position of the manipulator from the position sensor and the orientation of the manipulator from the IMU in real time. An efficient approach that incorporates a Kalman filter (KF) and a particle filter to estimate the position and orientation of the manipulator is proposed in this paper. The use of these pose (orientation and position) estimation methods improves the reliability and accuracy of pose measurements. Finally, an extended KF is used to estimate the kinematic parameter errors. The primary advantage of this method over existing automated self-calibration methods is that it does not involve complex steps, such as camera calibration, corner detection, and laser alignment, which makes the proposed robot calibration procedure more autonomous in a dynamic manufacturing environment. Moreover, the reduction of complex steps improves the accuracy of calibration. Experimental studies on a GOOGOL GRB3016 robot show that the proposed method has better accuracy, convenience, and effectiveness.