Monocular vision-based online kinematic calibration method for five-axis motion platform

Abstract

Aiming at the problems of low kinematics accuracy and a highly complex kinematic calibration of the five-axis motion platform, this paper proposes an online kinematic calibration method for simultaneous five-axis motion. First, the ArUco markers are used in vision systems for large stroke detection, and kinematics models of the five-axis motion platform are established through the screw theory. Following, this paper proposes an online kinematic parameter identification method for simultaneous five-axis motion, using a monocular camera as measurement tool. Furthermore, the stability and effectiveness of the identification algorithm are verified by simulation and experiment. Specifically, a process trajectory commonly used to carry out experiments, verifying the influence of the scheme on the kinematic accuracy. Experimental results show that the kinematic calibration method reduce the average position error of the five-axis motion platform by 88.59% and the average direction error by 84.54%. Therefore, the experiment proves that the kinematic calibration method can significantly improve the kinematic accuracy of the five-axis motion platform and it verifies the applicability and effectiveness of the proposed scheme.