Bibliographic Details
| Title: |
Research on LSTM-Based Maneuvering Motion Prediction for USVs |
| Authors: |
Rong Guo, Yunsheng Mao, Zuquan Xiang, Le Hao, Dingkun Wu, Lifei Song |
| Source: |
Journal of Marine Science and Engineering, Vol 12, Iss 9, p 1661 (2024) |
| Publisher Information: |
MDPI AG, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Naval architecture. Shipbuilding. Marine engineering
LCC:Oceanography |
| Subject Terms: |
unmanned surface vehicle, maneuvering motion model, black-box model, machine learning, long short-term memory network, Naval architecture. Shipbuilding. Marine engineering, VM1-989, Oceanography, GC1-1581 |
| More Details: |
Maneuvering motion prediction is central to the control and operation of ships, and the application of machine learning algorithms in this field is increasingly prevalent. However, challenges such as extensive training time, complex parameter tuning processes, and heavy reliance on mathematical models pose substantial obstacles to their application. To address these challenges, this paper proposes an LSTM-based modeling algorithm. First, a maneuvering motion model based on a real USV model was constructed, and typical operating conditions were simulated to obtain data. The Ornstein–Uhlenbeck process and the Hidden Markov Model were applied to the simulation data to generate noise and random data loss, respectively, thereby constructing a sample set that reflects real experiment characteristics. The sample data were then pre-processed for training, employing the MaxAbsScaler strategy for data normalization, Kalman filtering and RRF for data smoothing and noise reduction, and Lagrange interpolation for data resampling to enhance the robustness of the training data. Subsequently, based on the USV maneuvering motion model, an LSTM-based black-box motion prediction model was established. An in-depth comparative analysis and discussion of the model’s network structure and parameters were conducted, followed by the training of the ship maneuvering motion model using the optimized LSTM model. Generalization tests were then performed on a generalization set under Zigzag and turning conditions to validate the accuracy and generalization performance of the prediction model. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2077-1312 |
| Relation: |
https://www.mdpi.com/2077-1312/12/9/1661; https://doaj.org/toc/2077-1312 |
| DOI: |
10.3390/jmse12091661 |
| Access URL: |
https://doaj.org/article/73050a5aa26343b083e136619f022dd5 |
| Accession Number: |
edsdoj.73050a5aa26343b083e136619f022dd5 |
| Database: |
Directory of Open Access Journals |
