A Novel Deep Learning-Based Spatio-Temporal Model for Prediction of Pose Residual Errors in Optical Processing Hybrid Robot

Abstract

The accuracy of high-precision optical processing robots is influenced by various factors, including static error factors and dynamic error factors. These factors pose significant challenges to the deterministic processing of precision optics. This article proposes a pose residual prediction model for optical processing hybrid robots based on deep spatio-temporal graph convolutional neural networks. In this study, we establish a geometric error model for hybrid robots and calibrate the geometric error parameters using an extended Kalman filter to obtain the pose residuals component. To address the complex spatio-temporal interactions between multiple sensor variables in joint space during robot motion, we introduce the non-Euclidean spatio-temporal graph convolutional neural network. This model effectively extracts advanced spatio-temporal interaction features based on a spatio-temporal attention mechanism. Finally, the performance of the proposed method in pose residual prediction was validated through real experiments, and the results demonstrated its advantages over other state-of-the-art methods.