A Data-Driven Front Tracking Algorithm for Autonomous Undersea Vehicles

• Published in: 水下无人系统学报 Vol. 33; no. 3; pp. 518 - 526
• Main Authors: Jieyang LU, Yongpeng WEN, Qian GUO, Xinke ZHU, Junsheng JIAO
• Format: Journal Article
• Language: Chinese
• Published: Science Press (China) 01.06.2025
• Subjects: autonomous undersea vehicle; front tracking; gaussian process regression; particle swarm optimization algorithm
• ISSN: 2096-3920
• DOI: 10.11993/j.issn.2096-3920.2024-0151

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,