Position Prediction for Space Teleoperation With SAO-CNN-BiGRU-Attention Algorithm

Abstract

Robot position is a crucial information flow for space teleoperation, and the existence of time delay makes it actual asynchronous in sending and reception, greatly affecting the telepresence. To address this issue, this letter investigates the position prediction for space teleoperation and proposes an Snow Ablation Optimization (SAO)-CNN-BiGRU-Attention based prediction algorithm. Through prediction, the spatiotemporal synchronization of position information is achieved, thereby improving the telepresence. Firstly, based on the bilateral active estimation delay control framework, the CNN-BiGRU-Attention model is first introduced into position prediction for space teleoperation, where CNN serves for capturing the spatial feature relationship of the past position information, while BiGRU perceives its dynamic changes and combines Attention mechanism to focus on key feature, ultimately ensuring the accuracy of the prediction model. However, hyperparameter selection for the CNN-BiGRU-Attention model directly affects its prediction efficiency, and the custom selection way of hyperparameter obviously cannot guarantee optimality. To solve this problem, the SAO algorithm is introduced into the hyperparameter selection, utilizing its unique dual population mechanism and flexible position update equation to autonomously identify the optimal model hyperparameter and ensure optimal prediction efficiency. Finally, the effectiveness of the SAO-CNN-BiGRU-Attention algorithm was verified through comparative simulation experiments.