Underwater DVL Optimization Network (UDON): A Learning-Based DVL Velocity Optimizing Method for Underwater Navigation
As the exploration of marine resources continues to deepen, the utilization of Autonomous Underwater Vehicles (AUVs) for conducting marine resource surveys and underwater environmental mapping has become a common practice. In order to successfully accomplish exploration missions, AUVs require high-p...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/9/1/56 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588619889508352 |
|---|---|
| author | Feihu Zhang Shaoping Zhao Lu Li Chun Cao |
| author_facet | Feihu Zhang Shaoping Zhao Lu Li Chun Cao |
| author_sort | Feihu Zhang |
| collection | DOAJ |
| description | As the exploration of marine resources continues to deepen, the utilization of Autonomous Underwater Vehicles (AUVs) for conducting marine resource surveys and underwater environmental mapping has become a common practice. In order to successfully accomplish exploration missions, AUVs require high-precision underwater navigation information as support. A Strapdown Inertial Navigation System (SINS) can provide AUVs with accurate attitude and heading information, while a Doppler Velocity Log (DVL) is capable of measuring the velocity vector of the AUVs. Therefore, the integrated SINS/DVL navigation system can furnish the necessary navigational information required by an AUV. In response to the issue of DVL being susceptible to external environmental interference, leading to reduced measurement accuracy, this paper proposes an end-to-end deep-learning approach to enhance the accuracy of DVL velocity vector measurements. The utilization of the raw measurement data from an Inertial Measurement Unit (IMU), which includes gyroscopes and accelerometers, to assist the DVL in velocity vector estimation and to refine it towards the Global Positioning System (GPS) velocity vector, compensates for the external environmental interference affecting the DVL, therefore enhancing the navigation accuracy. To evaluate the proposed method, we conducted lake experiments using SINS and DVL equipment, from which the collected data were organized into a dataset for training and assessing the model. The research results show that the DVL vector predicted by our model can achieve a maximum improvement of 69.26% in terms of root mean square error and a maximum improvement of 78.62% in terms of relative trajectory error. |
| format | Article |
| id | doaj-art-4deac8559a50404690404729b7087248 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-4deac8559a50404690404729b70872482025-01-24T13:29:48ZengMDPI AGDrones2504-446X2025-01-01915610.3390/drones9010056Underwater DVL Optimization Network (UDON): A Learning-Based DVL Velocity Optimizing Method for Underwater NavigationFeihu Zhang0Shaoping Zhao1Lu Li2Chun Cao3School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaTechnical Center Department, Norinco Group Testing and Research Institute, Xi’an 710000, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaAs the exploration of marine resources continues to deepen, the utilization of Autonomous Underwater Vehicles (AUVs) for conducting marine resource surveys and underwater environmental mapping has become a common practice. In order to successfully accomplish exploration missions, AUVs require high-precision underwater navigation information as support. A Strapdown Inertial Navigation System (SINS) can provide AUVs with accurate attitude and heading information, while a Doppler Velocity Log (DVL) is capable of measuring the velocity vector of the AUVs. Therefore, the integrated SINS/DVL navigation system can furnish the necessary navigational information required by an AUV. In response to the issue of DVL being susceptible to external environmental interference, leading to reduced measurement accuracy, this paper proposes an end-to-end deep-learning approach to enhance the accuracy of DVL velocity vector measurements. The utilization of the raw measurement data from an Inertial Measurement Unit (IMU), which includes gyroscopes and accelerometers, to assist the DVL in velocity vector estimation and to refine it towards the Global Positioning System (GPS) velocity vector, compensates for the external environmental interference affecting the DVL, therefore enhancing the navigation accuracy. To evaluate the proposed method, we conducted lake experiments using SINS and DVL equipment, from which the collected data were organized into a dataset for training and assessing the model. The research results show that the DVL vector predicted by our model can achieve a maximum improvement of 69.26% in terms of root mean square error and a maximum improvement of 78.62% in terms of relative trajectory error.https://www.mdpi.com/2504-446X/9/1/56AUVSINSDVLdeep learningunderwater navigation |
| spellingShingle | Feihu Zhang Shaoping Zhao Lu Li Chun Cao Underwater DVL Optimization Network (UDON): A Learning-Based DVL Velocity Optimizing Method for Underwater Navigation Drones AUV SINS DVL deep learning underwater navigation |
| title | Underwater DVL Optimization Network (UDON): A Learning-Based DVL Velocity Optimizing Method for Underwater Navigation |
| title_full | Underwater DVL Optimization Network (UDON): A Learning-Based DVL Velocity Optimizing Method for Underwater Navigation |
| title_fullStr | Underwater DVL Optimization Network (UDON): A Learning-Based DVL Velocity Optimizing Method for Underwater Navigation |
| title_full_unstemmed | Underwater DVL Optimization Network (UDON): A Learning-Based DVL Velocity Optimizing Method for Underwater Navigation |
| title_short | Underwater DVL Optimization Network (UDON): A Learning-Based DVL Velocity Optimizing Method for Underwater Navigation |
| title_sort | underwater dvl optimization network udon a learning based dvl velocity optimizing method for underwater navigation |
| topic | AUV SINS DVL deep learning underwater navigation |
| url | https://www.mdpi.com/2504-446X/9/1/56 |
| work_keys_str_mv | AT feihuzhang underwaterdvloptimizationnetworkudonalearningbaseddvlvelocityoptimizingmethodforunderwaternavigation AT shaopingzhao underwaterdvloptimizationnetworkudonalearningbaseddvlvelocityoptimizingmethodforunderwaternavigation AT luli underwaterdvloptimizationnetworkudonalearningbaseddvlvelocityoptimizingmethodforunderwaternavigation AT chuncao underwaterdvloptimizationnetworkudonalearningbaseddvlvelocityoptimizingmethodforunderwaternavigation |