收藏切换
Identification modeling based on deep convolutional neural network for AUV 6-DOF motion
收藏切换
PDF
Chenyu LI1, Bin MEI1, Xiang'en BAI2, Jie ZHANG1, Heng WANG1
Navigation of China | 2025, 48(4) : 36 - 46
Less
收藏切换
Navigation of China | 2025, 48(4): 36-46
Marine Traffic Safety
Identification modeling based on deep convolutional neural network for AUV 6-DOF motion
Full
Chenyu LI1, Bin MEI1, Xiang'en BAI2, Jie ZHANG1, Heng WANG1
Affiliations
  • 1.Navigation College, Dalian Maritime University, Dalian 116026, China
  • 2.Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China
Published: 2025-12-25 doi: 10.3969/j.issn.1000-4653.2025.04.005
Outline
收藏切换

To address the challenges in dynamic modeling of Autonomous Underwater Vehicle (AUV), this paper proposes a black-box identification method for nonlinear systems based on deep convolutional neural networks, taking into account the nonlinear characteristics of the AUV's six-degree-of-freedom (6-DOF) motion. First, the frequency corresponding to the maximum amplitude of the rudder signal is extracted and used as a threshold for Variational Mode Decomposition (VMD) denoising. This reduces noise in the experimental data of the AUV model and resolves the issue of difficult parameter tuning in VMD decomposition. Then, a black-box model for the nonlinear system is constructed using Bidirectional Long Short-Term Memory (BiLSTM) and Attention mechanisms, with the Adam optimization algorithm employed to solve the AUV 6-DOF motion model. Finally, the AUV model data are used for model training and predictive validation, and the results are compared with modeling methods such as CNN-LSTM, CNN-BiLSTM, and CNN-LSTM-Attention to analyze the velocity, Euler angles, and trajectory of AUV motion. Experimental results show that, compared to the CNN-LSTM model, the proposed method improves the Root Mean Square Error (RMSE), the coefficient of determination (R2), and the Symmetric Mean Absolute Percentage Error (SMAPE) by 79.29%, 3.84%, and 74.41%, respectively, validating the feasibility and effectiveness of the proposed dynamic modeling approach. This method provides an alternative strategy for precise obstacle avoidance and autonomous navigation of underwater vehicles.

Adaptive VMD  /  BiLSTM  /  Attention mechanism  /  AUV Model experiments
Chenyu LI, Bin MEI, Xiang'en BAI, Jie ZHANG, Heng WANG. Identification modeling based on deep convolutional neural network for AUV 6-DOF motion[J]. Navigation of China, 2025 , 48 (4) : 36 -46 . DOI: 10.3969/j.issn.1000-4653.2025.04.005
Year 2025 volume 48 Issue 4
PDF
89
26
Cite this Article
BibTeX
Article Info
doi: 10.3969/j.issn.1000-4653.2025.04.005
  • Receive Date:2024-10-01
  • Online Date:2026-03-17
  • Published:2025-12-25
Article Data
Affiliations
History
  • Received:2024-10-01
Funding
Affiliations
    1.Navigation College, Dalian Maritime University, Dalian 116026, China
    2.Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China
References
Share
https://castjournals.cast.org.cn/joweb/zghh/EN/10.3969/j.issn.1000-4653.2025.04.005
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT