Bubble Plume Tracking Using a Backseat Driver on an Autonomous Underwater Vehicle
Autonomous underwater vehicles (AUVs) have been applied in various scientific missions including oceanographic research, bathymetry studies, sea mine detection, and marine pollution tracking. We have designed and field-tested in the ocean a backseat driver autonomous system for a 5.5 m survey-class...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
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| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/7/10/635 |
| _version_ | 1827721188576591872 |
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| author | Jimin Hwang Neil Bose Gina Millar Craig Bulger Ginelle Nazareth |
| author_facet | Jimin Hwang Neil Bose Gina Millar Craig Bulger Ginelle Nazareth |
| author_sort | Jimin Hwang |
| collection | DOAJ |
| description | Autonomous underwater vehicles (AUVs) have been applied in various scientific missions including oceanographic research, bathymetry studies, sea mine detection, and marine pollution tracking. We have designed and field-tested in the ocean a backseat driver autonomous system for a 5.5 m survey-class Explorer AUV to detect and track a mixed-phase oil plume. While the first driver is responsible for controlling and safely operating the vehicle; the second driver processes real-time data surrounding the vehicle based on in situ sensor measurements and adaptively modifies the mission details. This adaptive sensing and tracking method uses the <i>Gaussian blur</i> and occupancy grid method. Using a large bubble plume as a proxy, our approach enables real-time adaptive modifications to the AUV’s mission details, and field tests show successful plume detection and tracking. Our results provide for remote detection of underwater oil plumes and enhanced autonomy with these large AUVs. |
| first_indexed | 2024-03-10T21:19:03Z |
| format | Article |
| id | doaj.art-e0f93231c332447eba91ed34f0148de6 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-10T21:19:03Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-e0f93231c332447eba91ed34f0148de62023-11-19T16:15:44ZengMDPI AGDrones2504-446X2023-10-0171063510.3390/drones7100635Bubble Plume Tracking Using a Backseat Driver on an Autonomous Underwater VehicleJimin Hwang0Neil Bose1Gina Millar2Craig Bulger3Ginelle Nazareth4Faculty of Engineering and Applied Science (FEAS), Memorial University, St. John’s, NL A1B 3X9, CanadaOffice of the President and Vice-Chancellor, Memorial University, St. John’s, NL A1B 3X9, CanadaAutonomous Ocean Systems Centre (AOSCENT), Memorial University, St. John’s, NL A1B 3X9, CanadaMarine Institute, Memorial University, St. John’s, NL A1B 3X9, CanadaFaculty of Engineering and Applied Science (FEAS), Memorial University, St. John’s, NL A1B 3X9, CanadaAutonomous underwater vehicles (AUVs) have been applied in various scientific missions including oceanographic research, bathymetry studies, sea mine detection, and marine pollution tracking. We have designed and field-tested in the ocean a backseat driver autonomous system for a 5.5 m survey-class Explorer AUV to detect and track a mixed-phase oil plume. While the first driver is responsible for controlling and safely operating the vehicle; the second driver processes real-time data surrounding the vehicle based on in situ sensor measurements and adaptively modifies the mission details. This adaptive sensing and tracking method uses the <i>Gaussian blur</i> and occupancy grid method. Using a large bubble plume as a proxy, our approach enables real-time adaptive modifications to the AUV’s mission details, and field tests show successful plume detection and tracking. Our results provide for remote detection of underwater oil plumes and enhanced autonomy with these large AUVs.https://www.mdpi.com/2504-446X/7/10/635autonomous underwater vehicleadaptive algorithmacoustic sensorsonar detectionmicro-size bubble plumeoil pollution |
| spellingShingle | Jimin Hwang Neil Bose Gina Millar Craig Bulger Ginelle Nazareth Bubble Plume Tracking Using a Backseat Driver on an Autonomous Underwater Vehicle Drones autonomous underwater vehicle adaptive algorithm acoustic sensor sonar detection micro-size bubble plume oil pollution |
| title | Bubble Plume Tracking Using a Backseat Driver on an Autonomous Underwater Vehicle |
| title_full | Bubble Plume Tracking Using a Backseat Driver on an Autonomous Underwater Vehicle |
| title_fullStr | Bubble Plume Tracking Using a Backseat Driver on an Autonomous Underwater Vehicle |
| title_full_unstemmed | Bubble Plume Tracking Using a Backseat Driver on an Autonomous Underwater Vehicle |
| title_short | Bubble Plume Tracking Using a Backseat Driver on an Autonomous Underwater Vehicle |
| title_sort | bubble plume tracking using a backseat driver on an autonomous underwater vehicle |
| topic | autonomous underwater vehicle adaptive algorithm acoustic sensor sonar detection micro-size bubble plume oil pollution |
| url | https://www.mdpi.com/2504-446X/7/10/635 |
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