Increasing the Absolute Position Accuracy of Industrial Robots by Means of a Deep Continual Evidential Regression Model

The use of industrial robots represents a key technology for increasing productivity and efficiency in manufacturing. However, their low absolute position accuracy still denies the broad substitution of machine tools by industrial robots. In this paper, a data-driven method for accuracy enhancement of industrial robots under consideration of kinematic, elastic, and thermal effects is presented. A continual learning algorithm is proposed, which allows to train the model in a process-parallel manner without suffering from catastrophic forgetting. Furthermore, the model is able to determine confidence intervals of the prediction values and thus supports further processing in safety-relevant applications. The effectiveness of the model can be demonstrated using a large data stream with about 3,000 real data points. As a result, it can be shown that the absolute position accuracy of the industrial robot can be improved by 96 % with the proposed method.