A Data-Driven Front Tracking Algorithm for Autonomous Undersea Vehicles

LU Jieyang; WEN Yongpeng; GUO Qian; ZHU Xinke; JIAO Junsheng

doi:10.11993/j.issn.2096-3920.2024-0151

Volume 33 Issue 3

Jun 2025

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Article Navigation > Journal of Unmanned Undersea Systems > 2025 > 33(3): 518-526

LU Jieyang, WEN Yongpeng, GUO Qian, ZHU Xinke, JIAO Junsheng. A Data-Driven Front Tracking Algorithm for Autonomous Undersea Vehicles[J]. Journal of Unmanned Undersea Systems, 2025, 33(3): 518-526. doi: 10.11993/j.issn.2096-3920.2024-0151

Citation:

LU Jieyang, WEN Yongpeng, GUO Qian, ZHU Xinke, JIAO Junsheng. A Data-Driven Front Tracking Algorithm for Autonomous Undersea Vehicles[J]. Journal of Unmanned Undersea Systems, 2025, 33(3): 518-526. doi: 10.11993/j.issn.2096-3920.2024-0151

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A Data-Driven Front Tracking Algorithm for Autonomous Undersea Vehicles

doi: 10.11993/j.issn.2096-3920.2024-0151

1.
College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China
2.
Guodian Power Zhejiang Zhoushan Offshore Wind Power Development Co., Ltd, Zhoushan 316100, China
3.
Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China

Received Date: 2024-11-04
Accepted Date: 2025-01-15
Rev Recd Date: 2025-01-08

Available Online: 2025-05-26

Abstract

Abstract

To meet the requirement for adaptive observation of autonomous undersea vehicles(AUVs), a data-driven ocean front tracking algorithm was designed. This algorithm constructed a hybrid temperature field prediction model based on Gaussian process regression(GPR) and particle swarm optimization(PSO). Pre-collected data was utilized as prior information to train the model. The PSO algorithm was employed to iteratively optimize the hyperparameters within the kernel function, which were then substituted back into the GPR model to obtain predictions of the adjacent temperature field. By calculating the temperature gradient values between the AUV’s current position and the predicted region, the algorithm selected corresponding temperature gradient tracking strategies based on the AUV’s different positions within the front. This allowed the AUV to maintain motion along the gradient direction or track along isotherms, enabling rapid tracking of the ocean front by the AUV. To validate the effectiveness of the algorithm, simulation tests were conducted using real ocean front data. The results indicate that compared to other methods, this algorithm exhibits superior accuracy and speed in tracking ocean fronts, thereby satisfying the demand for efficient autonomous observation by AUV.
- autonomous undersea vehicle,
- front tracking,
- Gaussian process regression,
- particle swarm optimization algorithm

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References(15)

References

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