Popsitioning accuracy is an important index for evaluating the capacity of industrial robots. As a mechanism with multi-degree of freedom, the uncertainties of industrial robots are diverse and analyzing the positioning accuracy reliability is time consuming. To improve computation efficiency, a new active learning method based on Kriging model is proposed for hybrid reliability analysis of positioning accuracy with random and interval variables. In this study, the updated samples were selected through U learning function in the vicinity of limit-state function. A new stopping criterion based on expected risk function was exploited to judge whether the accuracy of Kriging model is enough. Two numerical examples and one engineering example were provided to verify the efficiency and accuracy of the proposed method. The results indicate that the proposed method is accurate and efficient.

定位精度是评估工业机器人能力的重要指标。作为具有多自由度的机制，工业机器人的不确定性是多种多样的，并且分析定位精度的可靠性非常耗时。为了提高计算效率，提出了一种基于克里格模型的主动学习新方法，用于随机和区间变量的定位精度混合可靠性分析。在这项研究中，更新的样本是通过U选择的极限状态功能附近的学习功能。利用基于期望风险函数的新的停止准则来判断Kriging模型的准确性是否足够。通过两个数值算例和一个工程算例，验证了所提方法的有效性和准确性。结果表明，该方法准确有效。