Error compensation for industrial robots combining kinematics characterized by conformal geometric algebra and fused observability-index-based measurement strategy

Abstract The geometric errors of industrial robots are key factors affecting positioning accuracy. A new compensation method for industrial robots is proposed based on the kinematics characterizing with conformal geometric algebra (CGA) and measurement strategy with double ball bar (DBB) path point optimization. Firstly, a kinematic error model for the industrial robot is established using CGA, the starting point of the CGA model is modified to simplify the modeling process and reduce computational complexity. Secondly, fused observability index is proposed and the relationship between the number of sampling points on the DBB path and the effectiveness of error parameter is obtained. Thirdly, the adaptive golden spiral optimization algorithm for error parameter identification is proposed, achieving efficient and stable identification of error parameters. Finally, a case study is carried out on a six-degree-of-freedom industrial robot. The validity of measurement strategy and error parameter identification algorithm are confirmed by comparing the residuals and uncertainties of predicted point positions in space with different methods. The spatial compensation results show that, after compensation, the average error and root mean square error of measurement paths are reduced by 36.76% and 33.96%, respectively.