A Hybrid Neural Network Approach for Increasing the Absolute Accuracy of Industrial Robots
Christian Landgraf, Kilian Ernst, Gesine Schleth, Marc Fabritius, Marco F. Huber
Abstract
The comparatively poor positioning accuracy of industrial robots limits or even prevents their use in many industrial applications. In contrast to other fields of robotic research, robot accuracy improvement has not been significantly boosted by machine learning-based methods yet. For this reason, we carried out four comprehensive series of measurements using a high-precision laser tracker together with a widely used 6-axis articulated robot. The data will be made publicly available to serve as benchmark data for different techniques. Along with the dataset, this paper introduces a hybrid neural network-based approach to compensate both geometric and non-geometric error sources and increase robot positioning accuracy. We compare our method to previous works and demonstrate advanced results.