Quantification of uncertainty in robot pose errors and calibration of reliable compensation values

Due to their inherent characteristics, robots inevitably suffer from pose errors, and accurate prediction is the key to error compensation, which facilitates the application of robots in high-precision scenarios. Existing studies almost follow the points-view, and the compensation effect depends entirely on the accuracy of the point prediction, which leads to overconfident prediction results. In order to quantify the pose errors uncertainty and achieve more accurate prediction, a method to quantify of uncertainty in robot pose errors and calibration of reliable compensation values is proposed in this paper. In the proposed method, a distribution-free joint prediction model is designed to realize the simultaneous prediction of points and uncertainty intervals. Based on this, the reliable compensation value calibration strategy is innovatively proposed. The proposed method is verified on five tasks including spatial motions, constant load and milling processing, showing accurate joint prediction capability and reliable accuracy improvement. In addition, through online compensation experiments, the pose errors are reduced by 90 %, which promotes the application of robots in higher-precision scenarios.