Kinematic Calibration Method for Large-Sized 7-DoF Hybrid Spray-Painting Robots

Abstract

Large-sized seven-degrees-of-freedom (7-DoF) hybrid spray-painting robots combine ample working space and high flexibility, making them lucrative for the spray painting of aircraft and rocket surfaces. However, their kinematic calibration is hindered by gravitational deformation, which problem is addressed in this study by introducing a rigid-flexible coupling error modeling method. The latter combines the finite element method (FEM) and stiffness matrix method to assess the spatial gravitational deformation of a hybrid robot, which is then introduced into a geometric error model to establish the rigid-flexible coupling error identification model. Given many redundant parameters in the identification model for 7-DoF robots, these parameters are classified and simplified using the nonlinear least-square regularization method for parameter identification. Combining the inverse solution of 7-DoF spray-painting robots with dynamic characteristics considered, an error compensation method for 7-DoF robots is proposed. The kinematic calibration test results strongly indicate that position errors are significantly reduced with gravity compensation taken into consideration, and error convergence speed increases, demonstrating that the kinematic calibration method is feasible and can effectively improve the accuracy of spray-painting robots. The mean errors in the X- and Y-directions are reduced by 20 and 17%, respectively, compared to the conventional method. The proposed method is instrumental in the accurate kinematic calibration of large-sized 7-DoF hybrid robots.