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.

中文翻译：

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机器人位姿误差不确定性的量化和可靠补偿值的校准

由于其固有的特性，机器人不可避免地会产生位姿误差，而准确的预测是误差补偿的关键，这有利于机器人在高精度场景下的应用。现有研究大多遵循点观点，补偿效果完全取决于点预测的准确性，导致预测结果过于自信。为了量化位姿误差不确定性并实现更准确的预测，提出了一种机器人位姿误差不确定性量化和可靠补偿值标定的方法。该方法设计了无分布联合预测模型来实现点和不确定区间的同时预测。在此基础上，创新性地提出了可靠的补偿值标定策略。该方法在空间运动、恒载荷和铣削加工等五个任务上进行了验证，显示出准确的联合预测能力和可靠的精度提升。此外，通过在线补偿实验，位姿误差降低了90%，促进了机器人在更高精度场景的应用。