A CNN-GRU Model-Based Trajectory Error Predicting and Compensating for a 6-DOF Parallel Robot

Abstract

The six-degree-of-freedom parallel robots is crucial for intelligent manufacturing, motion simulation, aerospace and other fields. Their trajectory performance level directly affects the reliable application of high-precision operation scenarios. However, dynamic trajectory errors under motion conditions remain a challenge. To address this, to improve the motion trajectory accuracy of parallel robots, a CNN-GRU model-based trajectory error prediction and compensation method is proposed. The novelty of this method lies in the hybrid deep learning architecture that combines CNN for spatial feature extraction and GRU for temporal dependency modeling. This method accurately predicts the trajectory error of parallel robots by constructing a deep learning model that integrates CNN and GRU, and compensates for the amplitude and bias of the trajectory error at the control command end, thereby improving the trajectory accuracy of parallel robots. The simulation and the 6-UPS parallel robot experiment verified the effectiveness of the proposed trajectory error prediction and compensation method. Key findings showed that the accuracy of the sinusoidal trajectory and circular trajectory of the parallel robot after error compensation was improved by about 90%.