Enhancing the absolute positioning accuracy of welding robots based on joint error compensation

This paper presented a novel method for improving the absolute positioning accuracy of robot end-effectors through joint space error prediction, addressing the issue of low accuracy in welding robots. A sampling method that considered both Cartesian and joint spaces was proposed, and a joint error compensation model based on Gaussian process regression (GPR) was established, utilizing the expected joint positions and joint errors as input features. A compensation strategy that integrated the error model into the robot controller was introduced. Experiments involving a laser tracker for positioning error compensation, including single-point multi-pose, spatial multi-point, and continuous welding trajectory tests, were conducted. The experimental results demonstrated that the proposed compensation method effectively reduced the mean absolute error (MAE) of welding robot positioning from approximately 0.6 mm to within 0.25 mm. This method significantly enhanced the absolute positioning accuracy of welding robot end-effectors, meeting the requirements for offline programming in welding applications.