The Kinematic Calibration of a Drilling Robot with Optimal Measurement Configurations Based on an Improved Multi-objective PSO Algorithm

In aircraft assembly, robot drilling system has been widely used to enhance the efficiency and quality of assembly holes' manufacturing. However, the industrial robot used by the drilling robot has low absolute positioning accuracy, which cannot meet the hole positioning accuracy requirement of aircraft components, so the drilling robot must be calibrated. The kinematic calibration effect is sensitive to the selection of measurement configurations. Although different observability indexes have been proposed to evaluate the measurement configurations, it is difficult to obtain precise kinematic parameters and minimize the uncertainty of end-effector positions simultaneously with a single observability index. At the same time, current measurement configurations optimization algorithms are still prone to fall into local optimization trap and boundary optimization trap. In order to improve the calibration accuracy of the drilling robot, an improved multi-objective particle swarm optimization algorithm was proposed to search the measurement configurations in the limited workspace, and the “rebound” particle was proposed to avoid local convergence. The effectiveness of the proposed algorithm was verified by simulations and calibration experiments of a drilling robot with KUKA KR500L340-2. Results show that the proposed algorithm can effectively obtain the measurement configurations with the maximum comprehensive observability index, and the positions of the measurement configurations could avoid the search boundary. Meanwhile, more accurate kinematic parameters of the drilling robot can be calculated by using the optimal measurement configurations searched by the proposed algorithm, and the end-effector position accuracy is 26.94% higher than that calibrated with randomly selected measurement configurations.