Robust Geometry Self-Calibration Based on Differential Kinematics for a Redundant Robotic Inspection System

Industrial measurements increasingly employ a redundant robotic system with an external turntable due to its efficiency, flexibility, and automation advantages. However, the actual viewpoint pose reached by the robotic system deviates from its simulated design pose due to orientation errors, resulting in poor measurement quality of complex surfaces. Geometry calibration is necessary for reducing robot orientation errors. However, most calibration methods ignore the factors of multiple error coupling and robotic absolute positioning error propagation, ultimately affecting calibration accuracy. In order to resolve this problem, this article proposes a robust self-calibration method for the simultaneous identification of geometric pose parameters. First, considering the overall synchronized control of orientation errors, a comprehensive error transfer model is derived as the theoretical basis. Then, the error model receives the system orientation errors as input, which are detected from a multiview visual measurement process on a calibration panel. Finally, the error model is solved using a self-adaptive Levenberg–Marquardt (SALM) algorithm, and the identified error vectors are used for online compensation of the kinematic model, improving the orientation accuracy of the robotic system. Simulations and experiments verify the accuracy and robustness of the proposed method. The experiment results show that the root mean square error (RMSE) detection deviation of the measuring point cloud compensated by self-calibration is reduced by 96.9% from 2.215 to 0.068 mm compared with before calibration. This self-calibration method is simple, economical, and general and can be further extended to other multiaxis automation systems.