Calibration of five-axis motion platform based on monocular vision.

In order to solve the problem of high measurement cost and complex operation of position-independent geometric errors (PIGEs) calibration on a five-axis motion platform, this paper first proposes a low-cost pose measurement method, based on monocular vision, which can accurately determine the pose in the environment, even with image shadow and noise. Next, an improved method, combining pose measurement and kinematic parameters identification, is proposed to calibrate a five-axis motion platform. The kinematic error model of the platform and the pose planning of automatic image acquisition are established, providing the pose data and motor position data, required for calibration. Combined with the kinematic loop method, the kinematic parameters of the five-axis motion platform are identified, while the geometric structure parameters are accurately calibrated. Before and after calibration, a circular trajectory of the target coordinate system (TCS) origin, relative to the camera coordinate system (CCS), is used to test the comprehensive accuracy evolution of the five-axis motion platform, by comparing the position and orientation errors of the theoretical circle trajectory to the actual one. The experimental data show that, before and after calibration, the average position error of the five-axis motion platform is reduced by 79.46%, while the average direction error is reduced by 86.53%. The experimental results clearly demonstrate that the proposed calibration method significantly improves the comprehensive motion accuracy of the five-axis motion platform, and they verify the practical value and effectiveness of the calibration scheme. [ABSTRACT FROM AUTHOR]