Adaptive Navigation Algorithm with Deep Learning for Autonomous Underwater Vehicle
Precise navigation is essential for autonomous underwater vehicles (AUVs). The measurement deviation of the navigation sensors, especially the microelectromechanical systems (MEMS) sensors, is a crucial factor that affects the localization accuracy. Deep learning is a novel method to solve this prob...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/19/6406 |
| _version_ | 1797515766547873792 |
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| author | Hui Ma Xiaokai Mu Bo He |
| author_facet | Hui Ma Xiaokai Mu Bo He |
| author_sort | Hui Ma |
| collection | DOAJ |
| description | Precise navigation is essential for autonomous underwater vehicles (AUVs). The measurement deviation of the navigation sensors, especially the microelectromechanical systems (MEMS) sensors, is a crucial factor that affects the localization accuracy. Deep learning is a novel method to solve this problem. However, the calculation cycle and robustness of the deep learning method may be insufficient in practical application. This paper proposes an adaptive navigation algorithm with deep learning to address these questions and realize accurate navigation. Firstly, this algorithm uses deep learning to generate low-frequency position information to correct the error accumulation of the navigation system. Secondly, the χ2 rule is selected to judge if the Doppler velocity log (DVL) measurement fails, which could avoid interference from DVL outliers. Thirdly, the adaptive filter, based on the variational Bayesian (VB) method, is employed to estimate the navigation information simultaneous with the measurement covariance, improving navigation accuracy even more. The experimental results, based on AUV field data, show that the proposed algorithm could realize robust navigation performance and significantly improve position accuracy. |
| first_indexed | 2024-03-10T06:51:55Z |
| format | Article |
| id | doaj.art-375dca7ba28045b49cc63f6fcf6b4e9a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T06:51:55Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-375dca7ba28045b49cc63f6fcf6b4e9a2023-11-22T16:45:38ZengMDPI AGSensors1424-82202021-09-012119640610.3390/s21196406Adaptive Navigation Algorithm with Deep Learning for Autonomous Underwater VehicleHui Ma0Xiaokai Mu1Bo He2Shanghai Marine Electronic Equipment Research Institute, Shanghai 201108, ChinaCollege of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Information Science and Engineering, Ocean University of China, Qingdao 266000, ChinaPrecise navigation is essential for autonomous underwater vehicles (AUVs). The measurement deviation of the navigation sensors, especially the microelectromechanical systems (MEMS) sensors, is a crucial factor that affects the localization accuracy. Deep learning is a novel method to solve this problem. However, the calculation cycle and robustness of the deep learning method may be insufficient in practical application. This paper proposes an adaptive navigation algorithm with deep learning to address these questions and realize accurate navigation. Firstly, this algorithm uses deep learning to generate low-frequency position information to correct the error accumulation of the navigation system. Secondly, the χ2 rule is selected to judge if the Doppler velocity log (DVL) measurement fails, which could avoid interference from DVL outliers. Thirdly, the adaptive filter, based on the variational Bayesian (VB) method, is employed to estimate the navigation information simultaneous with the measurement covariance, improving navigation accuracy even more. The experimental results, based on AUV field data, show that the proposed algorithm could realize robust navigation performance and significantly improve position accuracy.https://www.mdpi.com/1424-8220/21/19/6406autonomous underwater vehiclenavigation algorithmdeep learningvariational Bayesian |
| spellingShingle | Hui Ma Xiaokai Mu Bo He Adaptive Navigation Algorithm with Deep Learning for Autonomous Underwater Vehicle Sensors autonomous underwater vehicle navigation algorithm deep learning variational Bayesian |
| title | Adaptive Navigation Algorithm with Deep Learning for Autonomous Underwater Vehicle |
| title_full | Adaptive Navigation Algorithm with Deep Learning for Autonomous Underwater Vehicle |
| title_fullStr | Adaptive Navigation Algorithm with Deep Learning for Autonomous Underwater Vehicle |
| title_full_unstemmed | Adaptive Navigation Algorithm with Deep Learning for Autonomous Underwater Vehicle |
| title_short | Adaptive Navigation Algorithm with Deep Learning for Autonomous Underwater Vehicle |
| title_sort | adaptive navigation algorithm with deep learning for autonomous underwater vehicle |
| topic | autonomous underwater vehicle navigation algorithm deep learning variational Bayesian |
| url | https://www.mdpi.com/1424-8220/21/19/6406 |
| work_keys_str_mv | AT huima adaptivenavigationalgorithmwithdeeplearningforautonomousunderwatervehicle AT xiaokaimu adaptivenavigationalgorithmwithdeeplearningforautonomousunderwatervehicle AT bohe adaptivenavigationalgorithmwithdeeplearningforautonomousunderwatervehicle |