A Combined Calibration Method for Workpiece Positioning in Robotic Machining Systems and a Hybrid Optimization Algorithm for Improving Tool Center Point Calibration Accuracy

Abstract

This paper addresses the machining requirements for large aerospace structural components using robotic systems and proposes a method for rapid workpiece positioning that combines the simplicity of vision-based positioning with the precision of contact-based methods. To enhance the accuracy of robot calibration, a novel approach utilizing a ruby probe for sphere-to-sphere contact calibration of the Tool Center Point (TCP) is introduced. A robot contact calibration model is formulated, transforming the calibration process into a nonlinear least squares (NLS) optimization problem. To tackle the challenges of NLS optimization, a hybrid LM-D algorithm is developed, integrating the Levenberg–Marquardt (L-M) and DIviding RECTangle (DIRECT) algorithms in an iterative process to achieve the global optimum. This algorithm ensures computational efficiency while maximizing the likelihood of finding a globally optimal solution. An iterative convergence termination criterion for TCP calibration is established to determine global convergence, further enhancing the algorithm’s efficiency. Experimental validation was performed on industrial robots, demonstrating the proposed algorithm’s superior performance in global convergence and iteration efficiency compared to traditional methods. This research provides an effective and practical solution for TCP calibration in industrial robotic applications.