Camera-Mirror Binocular Vision-Based Method for Evaluating the Performance of Industrial Robots

Abstract

The dynamic and static property assessment of industrial robot is of great significance to optimize its operation accuracy. A method for both the kinematic calibration and robot path error measurement based on camera-mirror binocular vision (CMBV) is proposed. First, the structural parameters of CMBV are investigated for identifying proper imaging parameters. Second, the high-accuracy all-in-one artifact, its image processing, and vision system calibration are introduced. Thereafter, a CMBV-matching model based on distance error constraint is proposed to identify robot kinematic parameters. Finally, by comparing the vision measured data and the nominal one, both the path error and kinematic model parameters are measured. The verification results based on artifact and laser tracker verify that the proposed vision system and method is accurate enough to assess the robot performance. The results of vision measurement show that at 1.5 m/min, the maximum circular trajectory error of vision measurement is 2.57 mm, and after compensating the determined robot kinematic model parameters, the average positioning accuracy of the robot end-effector is increased by more than 73.36%, which means that the CMBV-based method outperforms the monocular pose measurement (MPM) method.