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

Abstract

This article proposes an online kinematic calibration method for simultaneous five-axis motion for solving the problems of low kinematics accuracy and a highly complex kinematic calibration of the five-axis motion platform. First, the ArUco markers are used in a vision system for large stroke detection, while kinematics models of the five-axis motion platform are established based on the screw theory. This article proposes an online kinematic parameter identification method for simultaneous motion along five axes, using a monocular camera as a measurement tool. Furthermore, the stability and effectiveness of the identification algorithm are verified by simulation and experiment. Specifically, a process trajectory commonly used to conduct experiments verifies the scheme’s influence on the kinematic accuracy. Experimental results show that the proposed kinematic calibration method reduces the average position error of the five-axis motion platform by 88.59% and the average direction error by 84.54%, thus proving that the proposed kinematic calibration method can significantly improve the kinematic accuracy of the five-axis motion platform and verifying the applicability and effectiveness of the proposed scheme.