# GAT-ABiGRU Based Prediction Model for AUV Trajectory
## 5 Conclusions and Future Prospects  
To improve the prediction accuracy of autonomous underwater vehicle (AUV) trajectories, we propose a novel GAT-ABiGRU lost trajectory prediction model for underwater robots. The core strength of the model lies in its ability to exploit the capabilities of graph neural networks (GAT) to capture spatial dependencies in trajectory data while incorporating the time-series analysis capabilities of a bidirectional gated recurrent unit (ABiGRU) to optimize the response to the effects of changes in the complex ocean environment on AUV trajectories. Compared to existing methods, the GAT-ABiGRU model has several significant advantages: First, it can consider both spatial and temporal characteristics of AUV trajectories, which is often neglected in traditional prediction models; second, through the graph attention mechanism, the model can automatically learn the weights between different nodes, thus capturing the key information more accurately; and finally, the time sequence analysis of the ABiGRU model optimizes the response to the impact of changes in the complex ocean environment on AUV trajectories, enabling the model to consider both forward and backward information, further improving the accuracy of the prediction. It is shown that the GAT-ABiGRU model shows a significant improvement in prediction accuracy and stability compared to existing prediction methods, especially in predicting the trajectories of lost AUVs. This is of great practical importance for the safety and efficiency of offshore  
Figure 14: (**a**) Comparison of latitude of the predicted and true trajectories of the ocean current tracks; (**b**) Comparison of longitude of predicted and true trajectories of the ocean current tracks. Figure 13: (**a**) Comparison of latitude of predicted and true ship trajectories; (**b**) Comparison of longitude of predicted and true ship trajectories. operations, as accurate trajectory prediction can help us better monitor and manage AUVs and reduce the risk of them being lost. In addition, the excellent ability of the GAT-ABiGRU model to handle complex data patterns and capture dynamic environmental changes further demonstrates its potential and value in the field of trajectory prediction. In summary, our GAT-ABiGRU model is innovative in theory and demonstrates significant application advantages in practice, providing new ideas and methods for research and application in the field of AUV trajectory prediction. Parameter configuration is critical in ensuring model performance in neural networks. Although we have selected a set of core hyperparameters based on our rich experience and practical applications, it should be noted that different parameter configurations can significantly impact model performance. Therefore, we must continuously focus on and explore different parameter configurations and improve long-term model performance by fine-tuning and optimizing the hyperparameters to adapt to new data and application scenarios. Future research will focus on the model's further optimization and practical application. We plan to improve further the model's ability to detect and adapt to environmental changes by expanding the size and type of dataset and integrating more ocean dynamics parameters. Meanwhile, improving the real-time computational capability will be a crucial direction for optimization to achieve fast response and real-time decision support for AUV trajectories. Conceptualization, M.Z. and J.Z. ; methodology, M.Z. ; software, M.Z. ; validation, M.Z., T.Z. and J.Z. ; formal analysis, Q.L. ; investigation, Q.L. and J.Y. ; resources, J.Z. ; data curation, M.Z., Q.L. and J.Y. ; writing--original draft preparation, M.Z. ; writing--review and editing, T.Z., M.Z. and E.S. ; visualization, M.Z. and J.Z. ; supervision, T.Z. and J.Z. ; project administration, J.Z. and T.Z. ; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by 2022-2025, the National Natural Science Foundation of China under Grant No.