A combined calibration method for workpiece positioning in robotic machining system and a hybrid optimization algorithm for improving the TCP calibration accuracy

Abstract This paper addresses the robot machining requirements for large aerospace structural components and provides a method for rapid workpiece positioning in robot machining systems that combines the ease of visual measurement-based positioning with the precision of contact-based positioning. In order to enhance the precision of the robot calibration system, this paper introduces a method that utilizes a ruby probe as a calibration tool to perform a sphere-to-sphere contact calibration of the Tool Center Point (TCP). A robot contact calibration model is established, converting the calibration problem into a non-linear least squares optimization problem. To address the challenges of multi-dimensional non-convex continuous optimization, the paper designs a combined LM-D algorithm that incorporates the Levenberg-Marquardt (L-M) algorithm and the DIRECT algorithm, engaging in mutual iterative processes to obtain the global optimum. This approach ensuring algorithm efficiency while maximizing the potential for a global optimum solution. In the algorithm, an iterative convergence termination criterion for robot TCP calibration is established, which is used to determine whether the algorithm converges globally. This criterion also contributes to improving the algorithm's efficiency. Experimental tests were conducted on typical industrial robots, and the results illustrate the algorithm's superior performance in terms of both global convergence and high iteration efficiency compared to traditional methods. This research offers a promising and efficient solution for robot TCP calibration in industrial.