A 6-DOF in-situ tracking system-based kinematic parameters online learning method for the parallel robot

Kinematic parameter errors of parallel robots are affected by manufacturing errors, assembly errors, and shape errors caused by heavy loads, resulting in a gradual decrease of their kinematic accuracy. This study proposes an online learning method for the kinematic parameter errors based on a six-degree-of-freedom (6-DOF) in-situ tracking system, which achieves their online identification. This method uses six high-precision measurement legs that are embedded in the parallel robot to achieve in-situ data measurement and adopts an online learning method to identify the kinematic parameter errors. Experimental results compared with the least squares method demonstrated that the proposed method effectively achieves online identification of the kinematic parameter errors, with position and orientation accuracy improved by 85.3% and 79.2%, respectively. Moreover, it can also maintain small position deviations even under varying loads, thus sustaining the high-accuracy motion of the parallel robot.