Towards autonomous underwater vehicles in the ocean survey: A mission management system (MMS). (1st November 2022)
- Record Type:
- Journal Article
- Title:
- Towards autonomous underwater vehicles in the ocean survey: A mission management system (MMS). (1st November 2022)
- Main Title:
- Towards autonomous underwater vehicles in the ocean survey: A mission management system (MMS)
- Authors:
- Yu, Fei
He, Bo
Liu, Jixin
Wang, Qi
Shen, Yue - Abstract:
- Abstract: This paper proposes a mission management system (MMS) to manage target recognition and path planning for the autonomous underwater vehicle (AUV) ocean survey. The system is autonomous and can trigger the corresponding mechanism according to the real-time ocean environment. The purpose is to survey only interest areas after online targets recognition, reducing wasted time optimizing mission-independent paths. The recognition part uses an improved end-to-end lightweight neural network algorithm, wide perception based on ShuffleNet (W-ShuffleNet), which enhances the image of the carried sensor information and performs online recognition, the accuracy of the simulation test is above 98%. The path planning part uses a real-time path planning (RTPP) method to explore the best strategy by evaluating the recognition results over a while to achieve the purpose of real-time adjustment of the AUV path, which effectively saves time and energy costs. This system is essential for improving overall mission performance and conducting effective ocean surveys under realistic conditions. We conducted sea trials with an AUV equipped with side-scan sonar, and the sea trial results proved the effectiveness of our proposed MMS. Highlights: A Mission Management System is proposed for the ocean survey. The W-ShuffleNet can achieve high-precision online target recognition. The RTPP optimizes the path in real time based on the recognition results. Superior results are obtained in the AUV seaAbstract: This paper proposes a mission management system (MMS) to manage target recognition and path planning for the autonomous underwater vehicle (AUV) ocean survey. The system is autonomous and can trigger the corresponding mechanism according to the real-time ocean environment. The purpose is to survey only interest areas after online targets recognition, reducing wasted time optimizing mission-independent paths. The recognition part uses an improved end-to-end lightweight neural network algorithm, wide perception based on ShuffleNet (W-ShuffleNet), which enhances the image of the carried sensor information and performs online recognition, the accuracy of the simulation test is above 98%. The path planning part uses a real-time path planning (RTPP) method to explore the best strategy by evaluating the recognition results over a while to achieve the purpose of real-time adjustment of the AUV path, which effectively saves time and energy costs. This system is essential for improving overall mission performance and conducting effective ocean surveys under realistic conditions. We conducted sea trials with an AUV equipped with side-scan sonar, and the sea trial results proved the effectiveness of our proposed MMS. Highlights: A Mission Management System is proposed for the ocean survey. The W-ShuffleNet can achieve high-precision online target recognition. The RTPP optimizes the path in real time based on the recognition results. Superior results are obtained in the AUV sea trial. … (more)
- Is Part Of:
- Ocean engineering. Volume 263(2022)
- Journal:
- Ocean engineering
- Issue:
- Volume 263(2022)
- Issue Display:
- Volume 263, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 263
- Issue:
- 2022
- Issue Sort Value:
- 2022-0263-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-11-01
- Subjects:
- Autonomous underwater vehicle -- Ocean survey -- Mission management system -- Side-scan sonar -- W-shuffleNet -- Real-time path planning
Ocean engineering -- Periodicals
Ocean engineering
Periodicals
620.4162 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00298018 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.oceaneng.2022.111955 ↗
- Languages:
- English
- ISSNs:
- 0029-8018
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 6231.280000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 24182.xml