A matrix-solving hand-eye calibration method considering robot kinematic errors

Abstract

Hand-eye calibration is one of the practical solutions for enhancing the robotic machining accuracy of complex freeform surfaces. The hand-eye parameters identification, however, is inevitably affected by the robot kinematic errors. In this paper, a matrix-solving hand-eye calibration method is proposed to overcome the challenge by taking into consideration the position constraint based robot kinematics identification. In the calibration framework, the calibration of hand-eye parameters is performed for solving the rotation matrix and translation vector, and the corresponding calibration strategies are proposed based on the axial translation under robot end-effector coordinate frame and the robot relocalization, respectively. Then the hand-eye parameters are corrected in the form of matrix compensation by using the differential matrix of the robot pose errors. A uniform sampling method for robot pose screening is further designed to automatically acquire spherical point clouds by the laser scanner when the robot poses change. Based on these steps, the combined identification model of robot kinematic parameter errors and hand-eye parameters is developed according to the position constraint that the position of the criterion sphere in the robot base coordinate frame is invariant. By virtue of the least square method, the kinematic parameter errors of the robot are calculated to compensate the hand-eye calibration accuracy. The calibration experiments are investigated by robotic scanning of the criterion sphere and two typical complex workpiece of car bodywork and blade, and the results are particularly compared with the uncompensated method and the state-of-the-art methods, showing the effectiveness and applicability of the proposed method.