Robotic Kinematic Calibration with Only Position Data and Consideration of Non-Geometric Errors Using POE-Based Model and Gaussian Mixture Models

Abstract

Kinematic calibration is crucial to improve the positioning accuracy of serial robots. This paper proposes a novel algorithm for robotic kinematic calibration based on an augmented product of exponentials (POE)-based kinematic model using Gaussian mixture models (GMMs) with only position data. In this algorithm, non-geometric errors that cannot be fitted by varying the parameters within the traditional robot model are also considered and compensated. This approach involving a three-stage calibration process which is used to identify the kinematic model parameters and to train the GMMs will be presented in this paper. Finally, this algorithm will be applied to two serial robots for simulation and experimental validation. The effectiveness of the proposed algorithm is verified from both results and significant improvement on error reduction from 26 % to 96% can be observed through the comparison with other existing approaches.