A product-of-exponential-based robot calibration method with optimal measurement configurations

Abstract

This article presents a systematic and practical calibration method for an industrial robot to improve its absolute accuracy. The forward kinematics is established based on the global product-of-exponential formula considering some practical constraints. An enhanced partial pose measurement technique is used to construct the linearized error model with only position measurement. All the kinematic parameters are identified via the linear least-squared iteration. The end effector errors are compensated by an inverse Jacobian iteration algorithm in the robot joint space. To suppress the influences of the measurement error, an improved sequential forward floating search algorithm is proposed to select an optimal subset of configurations from a large pool of measured poses based on the D-Optimality. The proposed algorithm is verified via simulations. The calibration method is validated by experiments on an industrial robot, showing that the absolute accuracy of the robot is improved about 10 times under a statistics sense after the calibration.