No abstract

No abstract

With the increasing application of computer vision in robot systems, it is vital to improve the positioning accuracy of robot end-effectors. Hand-eye calibration is the first step to realize it. However, in the process of calibration, there are some accidental factors leading to inaccurate calibration. In this paper, a data-driven hand-eye calibration approach based on Zhang's calibration method is proposed to complete eye-in-hand calibration. Re-projection error is used as an evaluate index to evaluate the positioning error. The calibration data with errors greater than the average error were filtered to reduce the accidental errors caused by uncertain factors. Finally, taking AUBO I5 collaborative robot as an example to verify the feasibility and effectiveness of the proposed approach. It shows that our approach can improve the positioning accuracy of the robot system.