Continuous Path Tracking of Robots Based on Positioning Error Compensation With Iterative Learning Control

Due to the inaccuracy of absolute positioning, it is still challenging to apply industrial robots for precise and efficient tracking of the end-effector tool along a continuous path, especially for those frequently changing robotic operations within low-volume and high-mix scenarios. To overcome this problem, this work proposes a method of continuous path tracking of robots with iterative learning control (ILC) such that the pose errors of the tool along the path can be compensated by the robot. An optical tracking system is integrated to measure the tool pose. Developed from a comprehensive kinematic error model that incorporates errors in the Denavit–Hartenberg (D-H) parameters and errors induced by the installation of the robot base and the tool, the ILC algorithm can convert the tool pose error into the joint angle compensation for the robot. The robustness of the algorithm is analyzed to prove that the compensated path approaches the desired path as the number of iterations increases. An experimental test of continuous path tracking has been performed with a UR10 robot to validate the proposed method. It is demonstrated that the average and maximum position errors can be reduced from 14.57 and 20.31 to about 0.26 and 0.78 mm, respectively. The average and maximum orientation errors are reduced from 0.66° and 0.94° to about 0.08° and 0.20°, respectively. For a test of robotic laser cutting, the average dimensional accuracy of the rectangular workpiece can be reduced from 0.416 to about 0.154 mm, and the average shape accuracy is reduced from 0.101° to about 0.059°. Using the ILC algorithm and the measurement with the optical tracking system, our method can significantly enhance the tracking accuracy of the end-effector tool along continuous paths by autonomous adjustment of the joint angles without the need for human teaching.