GAT-ABiGRU Based Prediction Model for AUV Trajectory
Source abstract
Abstract
Autonomous underwater vehicles (AUVs) are critical components of current maritime operations. However, because of the complicated marine environment, AUVs are at significant risk of being lost, and such losses significantly impact the continuity and safety of aquatic activities. This article suggests a methodology for forecasting the trajectory of lost autonomous underwater vehicles (AUVs) based on GAT-ABiGRU. Firstly, the time-series data of the AUV are transformed into a graph structure to represent the dependencies between data points. Secondly, a graph attention network is utilized to capture the spatial features of the trajectory data, while an attention-based bidirectional gated recurrent unit network learns the temporal features of the trajectory data; finally, the predicted drift trajectory is obtained. The findings show that the GAT-ABiGRU model outperforms previous trajectory prediction models, is highly accurate and robust in drift trajectory prediction, and presents a new method for forecasting the trajectory of wrecked AUVs.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Maritime Navigation and Safety · Underwater Vehicles and Communication Systems · Target Tracking and Data Fusion in Sensor Networks
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Information Science and Engineering, University of Jinan, Jinan 250022, China
Shandong Provincial Key Laboratory of Network-Based Intelligent Computing, University of Jinan, Jinan 250022, China
School of Data Intelligence, Yantai Institute of Science and Technology, Yantai 265699, China
School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China
Funding
National Natural Science Foundation of China
References (38)
Show source-provided references
1. On intelligent risk analysis and critical decision of underwater robotic vehicle10.1016/j.oceaneng.2017.06.020 ↗
2. Research status and prospect of multi-autonomous underwater vehicle cooperative search control
3. Terminal sliding mode-based tracking control with error transformation for underwater vehicles10.1002/rnc.5653 ↗
4. Fossen, S., and Fossen, T.I. (2018, January 20–22). Extended kalman filter design and motion prediction of ships using live automatic identification system (ais) data. Proceedings of the 2018 2nd European Conference on Electrical Engineering and Computer Science (EECS), Bern, Switzerland.10.1109/EECS.2018.00092 ↗
5. Cheng, J., Chen, Y., Zhang, Q., Gan, L., Liu, C., and Liu, M. (2022, January 23–27). Real-time trajectory planning for autonomous driving with gaussian process and incremental refinement. Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA.10.1109/ICRA46639.2022.9812405 ↗
6. RNN based MIMO channel prediction10.1016/j.sigpro.2009.07.013 ↗
7. Learning representations by back-propagating errors10.1038/323533a0 ↗
8. Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network10.1016/j.physd.2019.132306 ↗
9. Dey, R., and Salem, F.M. (2017, January 6–9). Gate-variants of gated recurrent unit (GRU) neural networks. Proceedings of the 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), Boston, MA, USA.10.1109/MWSCAS.2017.8053243 ↗
10. Long short-term memory10.1162/neco.1997.9.8.1735 ↗
11. Learning to forget: Continual prediction with LSTM10.1162/089976600300015015 ↗
12. Suo, Y., Chen, W., Claramunt, C., and Yang, S. (2020). A ship trajectory prediction framework based on a recurrent neural network. Sensors, 20.10.3390/s20185133 ↗
13. Deep learning methods for vessel trajectory prediction based on recurrent neural networks10.1109/TAES.2021.3096873 ↗
14. SABDM: A self-attention based bidirectional-RNN deep model for requirements classification10.1002/smr.2430 ↗
15. Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm10.1016/j.eswa.2023.120346 ↗
16. Vessel navigation behavior analysis and multiple-trajectory prediction model based on AIS data10.1155/2022/6622862 ↗
17. Dynamic-learning spatial-temporal Transformer network for vehicular trajectory prediction at urban intersections10.1016/j.trc.2023.104330 ↗
18. Spatio-temporal multi-graph transformer network for joint prediction of multiple vessel trajectories10.1016/j.engappai.2023.107625 ↗
19. A model for vessel trajectory prediction based on long short-term memory neural network10.1080/20464177.2019.1665258 ↗
20. Ding, W., Huang, J., Shang, G., Wang, X., Li, B., Li, Y., and Liu, H. (2022). Short-term Trajectory prediction based on Hyperparametric optimisation and a dual attention mechanism. Aerospace, 9.10.3390/aerospace9080464 ↗
21. Deep transfer learning based on Bi-LSTM and attention for remaining useful life prediction of rolling bearing10.1016/j.ress.2022.108914 ↗
22. A hybrid CNN-LSTM model for aircraft 4D trajectory prediction10.1109/ACCESS.2020.3010963 ↗
23. Cai, Z., Tan, C., Zhang, J., Zhu, L., and Feng, Y. (2024). DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction. Appl. Sci., 14.10.3390/app14052173 ↗
24. Short-term traffic speed forecasting based on graph attention temporal convolutional networks10.1016/j.neucom.2020.06.001 ↗
25. Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv.
26. Novel GCN Model Using Dense Connection and Attention Mechanism for Text Classification10.1007/s11063-024-11599-9 ↗
27. Influence maximization in social networks using transfer learning via graph-based LSTM10.1016/j.eswa.2022.118770 ↗
28. Wang, Y., Yao, X., Zhu, P., Li, W., Cao, M., and Hu, Q. (2024). Integrated Heterogeneous Graph Attention Network for Incomplete Multi-modal Clustering. Int. J. Comput. Vis., 1–20.10.1007/s11263-024-02066-y ↗
29. A graph-guided collaborative convolutional neural network for fault diagnosis of electromechanical systems10.1016/j.ymssp.2023.110609 ↗
30. Wu, Z., Pan, S., Long, G., Jiang, J., and Zhang, C. (2019). Graph wavenet for deep spatial-temporal graph modeling. arXiv.10.24963/ijcai.2019/264 ↗
31. Graph attention LSTM: A spatiotemporal approach for traffic flow forecasting10.1109/MITS.2020.2990165 ↗
32. STMAG: A spatial-temporal mixed attention graph-based convolution model for multi-data flow safety prediction10.1016/j.ins.2020.03.040 ↗
33. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv.10.3115/v1/D14-1179 ↗
34. Nguyen, D., and Fablet, R. (2021). TrAISformer-A generative transformer for AIS trajectory prediction. arXiv.
35. Busbridge, D., Sherburn, D., Cavallo, P., and Hammerla, N.Y. (2019). Relational graph attention networks. arXiv.
36. Cho, H., and Lee, H. (2019). Biomedical named entity recognition using deep neural networks with contextual information. BMC Bioinform., 20.10.1186/s12859-019-3321-4 ↗
37. Xian-yan, M., Rong-yi, C., Ya-hui, Z., and Zhenguo, Z. (2019, January 20–22). Multilingual short text classification based on LDA and BiLSTM-CNN neural network. Proceedings of the Web Information Systems and Applications: 16th International Conference, WISA 2019, Qingdao, China. Proceedings 16.10.1007/978-3-030-30952-7_32 ↗
38. Scholin, C. (2023). Autonomous Underwater Vehicle Monterey Bay Time Series—AUV Makai CTD. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 15 August 2023 [If Applicable, Indicate Subset Used], Springer.