Polar AUV Challenges and Applications: A Review
Abstract
1. Introduction
1.1. Concerns in Polar Ice Studies
1.2. Progress of Under-Ice Observation
2. History of Polar AUV Deployments
2.1. Sporadic Developments in Early Years (1970s–2000s)
2.2. Continued Series AUV Deployments (2000s–2020s)
2.3. Other Deployments in Recent Years
2.4. Discussion of AUV Deployments in Polar Regions
3. Key Challenges in Technology for Polar AUV Deployments
3.1. Navigation
- Acoustic positioning: This method involves the use of acoustic beacons placed on the ice, buoys, or the seafloor. By combining long-range acoustic bearing systems with short-range localization systems, it is possible to achieve both expansive coverage and precise positioning. Long baseline (LBL) and ultra-short baseline (USBL) systems are typically used, leveraging the propagation of sound waves in water to triangulate the AUV’s position with high accuracy.
- Conventional dead reckoning: The commonly used inertial navigation systems (INS), Doppler velocity logs (DVL), pressure sensor, and electrical compass are usually integrated to track the movement of the AUV from a known starting point. Data from these different sources are often fused using advanced filtering algorithms, such as Kalman filters, to enhance positioning accuracy. This method relies on the continuous accumulation of positional data, corrected for drift and error over time, to maintain an accurate track of the AUV’s trajectory.
- Terrain, geomagnetic, and gravity field-assisted navigation: These methods are particularly valuable in environments where traditional GPS and acoustic systems are limited or unavailable. Pre-existing bathymetric, geomagnetic, and gravity maps provide reference data that can be used for navigation. For instance, terrain-aided navigation (TAN) uses detailed seafloor topography to cross-reference the AUV’s position, while geomagnetic and gravity field data offer additional layers of spatial information. These techniques are effective in areas with distinct geological features, although their integration remains an active area of research and development.
- Underwater GPS technology: Emerging technologies, such as underwater GPS, are being developed to provide more precise underwater navigation. These systems use sound waves in a manner analogous to traditional GPS, enabling accurate positioning even in the absence of direct satellite signals. Underwater GPS technology represents a significant advancement, promising to enhance the reliability and precision of AUV navigation in polar regions [106].
3.2. Communication
- Acoustic communication: Acoustic modems are the primary method for under-ice AUV communication, utilizing sound waves to transmit data through water. However, acoustic signals can be attenuated by ice cover, limiting both range and data transfer rates. Advances in signal processing and underwater acoustic technologies are continuously improving the reliability and efficiency of acoustic communication systems for polar AUV operations.
- Surface communication: When AUVs surface in open water areas, they can utilize satellite or Radio Frequency (RF) communication systems, such as Iridium, for data transfer and receiving commands. This method circumvents the limitations imposed by ice cover on acoustic communication. However, operational constraints may still arise in regions where satellite coverage is limited or compromised by polar conditions.
- Buoy relay systems: Deploying buoys equipped with integrated acoustic modems (for underwater communication) and satellite links (for surface communication) serves as a bridge between submerged AUVs and base stations. These buoy relay systems enable seamless communication transitions between underwater and surface environments, extending operational range and enhancing data transfer capabilities in polar regions. Strategic placement of buoys optimizes communication reliability and facilitates continuous monitoring and control of AUV missions.
- Data muling: In scenarios where real-time communication is impractical, AUVs can store collected data onboard for physical retrieval upon mission completion. This approach ensures data integrity and security, particularly in remote and inaccessible polar regions where communication disruptions are common. Advances in data storage technologies and onboard processing capabilities further support efficient data muling strategies for extended mission durations.
- Inter-vehicle communication: In collaborative missions involving multiple AUVs or other vehicles, inter-vehicle communication plays a crucial role in data sharing and mission coordination. AUVs can exchange real-time data, coordinate maneuvers, and optimize survey coverage through collaborative communication protocols. Enhanced networking capabilities and protocols tailored for polar environments enable synchronized operations and adaptive decision-making among autonomous vehicles.
3.3. Path Planning and Obstacle Avoidance
- Dynamic under-ice terrain: Polar regions exhibit constantly changing ice conditions, including icebergs, ridges, and variable ice thickness. AUVs necessitate adaptive path planning strategies to navigate these unpredictable environments safely and efficiently.
- Advanced sensing and mapping: Leveraging advanced sensing technologies such as sonar and camera, AUVs can generate real-time maps of their surroundings. These detailed maps are critical for identifying potential obstacles and planning optimal navigation paths to avoid hazards.
- Three-dimensional path planning algorithms: Effective path planning algorithms must process environmental data in three dimensions, incorporating depth constraints to navigate around or beneath ice formations. These algorithms optimize route efficiency while ensuring safe passage through intricate under-ice terrains.
- Simulations and predictive models: Prior to deployment, simulations and predictive models can be used to simulate ice movements and underwater topography. These tools provide valuable insights for planning missions, anticipating environmental challenges, and refining path planning strategies to enhance operational success.
- Autonomy in decision-making: Due to limited communication with surface operators, AUVs rely on high levels of autonomy to make real-time decisions for obstacle avoidance and path adjustments. Autonomous systems continuously analyze sensor data, enabling swift responses to dynamic environmental changes without human intervention. Incorporating machine learning and artificial intelligence (AI) enhances AUV capabilities in obstacle detection and path planning. AI algorithms learn from past missions, improving decision-making processes and adapting strategies based on accumulated experience and environmental conditions.
- Safety protocols: Implementing robust safety protocols is essential for handling emergency situations. Features such as automatic return-to-home capabilities and protocols for hovering in place upon encountering unexpected obstacles ensure mission safety and data integrity.
3.4. Energy
- Battery technology: Polar AUVs primarily rely on advanced battery systems to power their operations. Lithium-based batteries are preferred for their high energy density and reliability, particularly in cold temperatures. Ongoing research focuses on enhancing battery efficiency and cold tolerance, aiming to extend operational durations and improve reliability under polar conditions. Fuel cells present a promising alternative power source for polar AUVs, offering advantages such as extended endurance, cold tolerance, and reduced environmental impact. However, challenges remain in fuel storage, cold start capability, and integration complexity.
- Energy-efficient design: The design of AUVs plays a crucial role in minimizing energy consumption. This involves optimizing hydrodynamic efficiency to reduce drag, employing energy-efficient propulsion systems, and carefully managing power requirements for onboard sensors and communication systems. Efficient design practices ensure optimal energy utilization throughout the mission lifecycle.
- Operational strategy: Mission planning must meticulously consider energy constraints to maximize operational efficiency. This includes optimizing travel routes to minimize energy consumption, strategically managing the operational periods of energy-intensive instruments, and balancing exploration depth, speed, and data collection priorities to optimize energy use without compromising mission objectives.
- Renewable energy sources: Exploring renewable energy sources is essential for extending mission durations and reducing reliance on traditional battery power. Integration of solar panels for surface charging and environmental energy harvesting technologies offers promising avenues to supplement onboard power systems, particularly during extended missions in sunlit polar regions.
- Autonomous recharging: Developing autonomous recharging capabilities is critical for prolonged AUV operations. Solutions such as docking stations on ice shelves or buoys equipped with renewable energy sources can facilitate autonomous recharging, thereby extending mission endurance and operational flexibility without the need for manual intervention.
- Energy storage and backup systems: Ensuring adequate energy storage capacity and reliable backup systems is imperative to maintain uninterrupted AUV operation. Robust energy storage solutions and contingency plans for unexpected energy drains or emergencies are essential safeguards in the unpredictable polar environment.
3.5. Launch and Recovery
- Preparation and planning: Thorough preparation is essential, given the unpredictable nature of polar weather and dynamic ice conditions. Rigorous planning involves comprehensive analysis of ice dynamics, continuous monitoring of weather forecasts, and the development of robust contingency plans to mitigate risks during AUV missions.
- Utilizing icebreaker support: Icebreakers play a crucial role in navigating through thick ice to access designated launch sites. These vessels not only provide key logistical support but also serve as stable platforms for deploying AUVs in challenging polar environments, ensuring safe and efficient operations.
- Deployment through ice: Launching an AUV often necessitates creating openings, or leads, in the ice to facilitate entry into the water. Methods such as ice melting, cutting, or utilizing the icebreaker’s capabilities are employed to establish suitable access points for deploying the vehicle.
- Recovery operations: Retrieving an AUV from ice-covered waters presents significant challenges. Effective recovery strategies involve guiding the AUV back to a predetermined open water location or newly created lead in the ice. Techniques such as acoustic homing systems and the use of remotely operated vehicles (ROVs) are employed to ensure precise and secure recovery operations.
- Adaptation to variable ice conditions: Polar ice conditions are inherently dynamic, requiring adaptive responses to rapidly changing environments. Both AUV operators and support teams must be equipped to swiftly adjust to shifting ice formations, which can impact the timing and execution of deployment and recovery procedures.
3.6. Risk Analysis
- Environmental assessment: Regular assessment of ice conditions, weather patterns, and water characteristics is essential to mitigate risks associated with the unpredictable nature of polar environments. Continuous monitoring enables proactive adjustments to operational plans based on real-time data.
- Robust design and testing: AUVs must be carefully designed to withstand extreme cold, high pressures, and potential interactions with ice formations. Rigorous testing under simulated polar conditions ensures the reliability and durability of AUV systems before deployment in the field.
- Emergency protocols: Developing comprehensive emergency procedures is critical for handling scenarios such as AUV entrapment under ice, loss of communication, or equipment failures. Regular drills and rehearsals help maintain readiness and ensure swift and effective responses in crisis situations.
- Data and power backup systems: Integration of redundant systems for data storage and power supply is vital to maintain operational integrity in the event of system failures. Backup systems minimize disruptions and enhance the AUV’s resilience during missions.
- Real-time monitoring: Continuous monitoring of AUV operational parameters and environmental conditions allows for timely decision-making and proactive adjustments to mission strategies. Real-time data analysis facilitates early detection of potential issues, enabling swift corrective actions.
- Team training and preparedness: Ensuring that expedition teams are well trained in AUV operations, emergency response protocols, and familiar with the specific challenges of polar regions is crucial. Competency in handling AUV operations under challenging conditions enhances overall mission safety and effectiveness.
- Risk analysis: Conducting thorough risk analysis throughout all phases of the mission—from planning to execution and post-mission assessment—helps identify, assess, and mitigate potential risks. This proactive approach ensures continuous improvement in risk management strategies and enhances overall mission safety.
4. Capabilities and Applications
4.1. Under-Ice Mapping and Measurement
4.2. Water Sampling
4.3. Ecological Investigation
4.4. Seafloor Mapping
4.5. Surveillance Networking
5. Discussion and Future Outlook
- Enhanced technological capabilities: Continuous advancements in AUV technology are expected to yield more robust, efficient, and versatile vehicles. Innovations in battery life, propulsion systems, and miniaturization will enable longer, more complex missions, extending the operational range and capabilities of AUVs in polar environments. These improvements will facilitate more comprehensive and sustained data collection efforts, allowing for extended deployments and reducing the need for frequent retrieval and maintenance. Additionally, the development of modular AUV designs will enable the customization of vehicles for specific missions, enhancing their adaptability and performance across various research and commercial applications [78,80].
- Improved navigation and communication: Innovations in under-ice navigation and communication systems are crucial for operating in the challenging polar environment. Enhanced navigation technologies, such as advanced INS and USBL systems, will provide more accurate motion control. Concurrently, developments in communication technology, especially the acoustic approach, will ensure reliable data transmission between the AUV and the control center, thus improving mission success rates [98]. The integration of real-time data processing and transmission capabilities will enable scientists to monitor and adjust AUV missions dynamically, enhancing the precision and effectiveness of data collection.
- Versatile data collection: Future AUVs will be equipped with a wide array of sensors and instruments designed for comprehensive data gathering in remote polar regions. These capabilities will include under-ice surveys, oceanographic measurements, biological sampling, chemical analysis, seafloor mapping, acoustic surveys, and visual observations. The integration of multi-modal sensors will enhance the ability to monitor and study the polar environment comprehensively. For instance, advanced imaging systems combined with environmental DNA (eDNA) sampling technologies will provide detailed insights into the biodiversity and health of polar ecosystems [195].
- Autonomy and AI integration: The integration of artificial intelligence (AI) and machine learning technologies will significantly enhance the autonomy of AUVs. These advancements will enable AUVs to make independent decisions during missions, adapting to dynamic environments and optimizing data collection processes. Enhanced autonomy will not only improve operational efficiency but also ensure higher-quality data collection, reducing the need for human intervention [190]. AI-driven algorithms will allow AUVs to identify and respond to anomalies or changes in the environment, ensuring the collection of relevant and high-priority data.
- Increased accessibility and operational safety: As AUV technology becomes more user-friendly and cost-effective, it will become accessible to a broader range of users, including academic institutions, research organizations, and commercial enterprises. Improved safety features and user interfaces will facilitate safe operations in hazardous polar environments, minimizing the risks associated with under-ice missions [175]. The development of standardized training programs and operational protocols will further enhance the safe and effective use of AUVs, ensuring that even less experienced operators can conduct successful missions.
- Collaborative and networked operations: The future will see an increase in the use of AUV swarms or coordinated missions involving multiple AUVs. These networked operations will provide broader coverage and more diverse datasets, enhancing the overall understanding of the polar environment. Collaborative missions will leverage the strengths of individual AUVs, allowing for more efficient and comprehensive data collection. Swarm intelligence and distributed computing techniques will enable AUVs to coordinate their activities autonomously, optimizing their collective performance and resilience in dynamic environments [196].
- Increased focus on climate change research: Polar AUVs will play a critical role in climate change research as the effects of global warming become more pronounced. These vehicles will be instrumental in monitoring ice melt, sea level rise, and changes in marine ecosystems. The data collected by AUVs will provide valuable insights into the impacts of climate change on polar regions, informing mitigation strategies and policy decisions [197]. Long-term monitoring programs will enable scientists to track temporal changes in the polar environment, enhancing our understanding of climate dynamics and their global implications.
- Broader scientific and commercial applications: Beyond environmental research, polar AUVs are likely to find applications in resource exploration, environmental monitoring, and mitigation, as well as support for commercial and military shipping in newly accessible polar routes. The versatility and advanced capabilities of future AUVs will drive their adoption across various sectors, contributing to the sustainable management and utilization of polar resources. For example, AUVs equipped with geophysical survey instruments will facilitate the exploration of mineral and hydrocarbon resources, while environmental monitoring missions will ensure the responsible development and protection of these regions [198].
- Global collaboration and policy development: The strategic importance of polar regions is expected to rise, leading to increased international collaboration and policy-making regarding the deployment and use of AUVs in these areas. Collaborative efforts will involve partnerships with Indigenous communities, governments, and international organizations, promoting the sustainable management of polar environments and ensuring equitable access to polar research opportunities [199]. The establishment of international agreements and regulatory frameworks will be essential to harmonize AUV operations, safeguard environmental integrity, and address geopolitical considerations in the polar regions.
6. Summary and Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
No. | Time | Vehicle | Location | Institute/Program/Sponsor | Duration and Range | Outcomes | Reference |
---|---|---|---|---|---|---|---|
1 | 1972 | UARS | Beaufort Sea, Arctic | APL, ARPA-ONR | The AUV ran in excess of 17 miles for more than 4 h. | Provided the most complete, directly correlated measurements of underwater ice topography ever made. | [29] |
2 | 1992 | ACTV | Beaufort Sea, Arctic | APL, Lead Experiment | 20 runs were made around 4 leads for almost 4.5 h. | Obtained the first measurements of temperature, salinity, and turbulence under and around leads. | [30] |
3 | 1994 | ACTV | Eastern Weddell Sea, Antarctic | APL, Antarctic Zone Flux Experiment | Runs of 1–2 km tracks at different depths. | Measured the temperature and salinity of the upper ocean in response to a series of storms. | [32] |
4 | 1998 | ACTV, AMTV | Beaufort Sea, Arctic | APL and WHOI, Surface Heat Balance of the Arctic Ocean program | 44 runs adding up to 70 km of run track were gathered. | Collected temperature and salinity profiles to estimate heat and salt fluxes under varying surface conditions. | [32] |
5 | 1994 | Odyssey II | Beaufort Sea, Arctic | AUV Lab, MIT, Arctic Sea Ice Mechanics research program, MIT Sea Grant and ONR | The vehicle performed a series of “out-and-back” missions, and generated preliminary maps. | Measured the topography of the ice canopy to study transient events in the ice. | [33] |
6 | 1996 | Theseus | Canadian Arctic | ISE and DREA, Canadian Department of National Defence | The vehicle completed a 320 km under-ice transit, establishing an AUV endurance record of over 60 h—all under ice. | Laid an optical fiber cable stretching up to 220 km in the ice-covered Arctic Ocean. | [36] |
7 | 2001 | ALTEX (Dorado) | Fram Strait, Arctic | MBARI, Atlantic Layer Tracking Experiment, NSF and ONR | Three days of under-ice operations resulted in the collection of plentiful multi-kilometer long sections of ice draft. | Gathered data on the warm Atlantic Layer water mass flowing into the Arctic Ocean via the Fram Strait. | [40] |
8 | 2001 | Autosub 2 | Northern Weddell Sea, Antarctic | British Antarctic Survey and NOC, Autosub Under Ice program, NERC | There were more than 20 missions in total that collected over 690 km of data, 485 km being beneath sea ice (including 210 km for a krill survey). | Measured Antarctic sea ice thickness, surveyed beneath different types of icebergs, and assessed the abundance of Antarctic krill. | [9,43] |
9 | 2002 | Maridan MARTIN 150 | Off the coast of East Greenland, Arctic | University of Cambridge, EU CONVECTION program | A total track length of 4.6 km from two runs was reported. | Captured the first 2D imagery of multi-year ice using a sidescan sonar, together with the CTD and ADCP data. | [45] |
10 | 2004 | Autosub 2 | Off NE Greenland, Arctic | University of Cambridge, Autosub Under Ice program, NERC | 458 km of high-quality multibeam sonar images and oceanographic data were collected. | Obtained the first successful swath sonar images under sea ice, and collect systematic measurements of the water and the seabed beneath the ice. | [41] |
11 | 2005 | Autosub 2 | Under the Fimbul Ice Shelf, Antarctic | British Antarctic Survey and NOC, Autosub Under Ice program, NERC | The vehicle ran a simple in and out mission that took it some 25 km into the cavity under the ice shelf. | Revealed the topographic and oceanographic conditions beneath ice shelves. | [4] |
12 | 2009 | Autosub 3 | Pine Island Glacier, Antarctic | British Antarctic Survey and NOC, Autosub Under Ice program, NERC | The AUV undertook six missions and covered 510 km in total under the PIG. | The data indicated the glacier used to ground on a seafloor ridge, but its retreat has led to warm water entering and quickly melting the upstream ice. | [48] |
13 | 2014 | Autosub 3 | Pine Island Glacier, Antarctic | British Antarctic Survey and NOC, Ice Sheet Stability program | The AUV covered 460 km of track beneath the PIG Ice Shelf. | Provided observations of temperature, salinity, velocity, turbulent kinetic energy dissipation rate, and thermal variance dissipation rate under the ice shelf, giving confidence in previous estimates of basal melting. | [49] |
14 | 2018 | ALR | Filchner–Ronne Ice Shelf, Antarctic | British Antarctic Survey and NOC, Ice Sheet Stability program | The ALR navigated under the ice shelf for over three days, covering more than 25 km in regions where the ice was over 500 m thick. | Made direct measurements of the hydrology as well as the ice shelf and seabed morphology. | [50,51] |
15 | 2022 | ALR | Thwaites Glacier and Dotson Ice Shelves, Antarctic | Science agencies of the UK and USA, TARSAN and Ocean Alliance of NOC | The ALR AUV travelled more than 40 km under the shelf. | Measured currents, turbulence, and other water properties like temperature and salinity to investigate the factors driving ice loss from the glacier. | [56] |
16 | 2019 | Ran (Hugin) | Thwaites Glacier, Antarctic | Science agencies of the UK and US, NERC and NSF Office of Polar Programs as part of the ITGC | The AUV undertook short excursions within 10 km under the ice shelf, collecting around 13 km2 of new geophysical data over a 19 h mission across an isolated seafloor promontory. | Produced the most detailed seafloor maps ever made of the region and gathered data on ocean conditions and currents. | [53,54] |
17 | 2022, 2024 | Ran (Hugin) | Thwaites Glacier and Dotson Ice Shelves, Antarctic | Science agencies of the UK and US, TARSAN and Ocean Alliance of NOC | The vehicle was tasked with 20-hour missions at two key sites. Some explorations were under the 200–500 m thick ice. | Integrated seafloor mapping with mid-water column profiling and sampling into mission programs. | [56,57] |
18 | 2007 | SeaBED Jaguar and Puma | Gakkel Ridge in the Arctic Ocean | WHOI, Arctic Gakkel Vents Expedition, NSF Office of Polar Programs and NASA ASTEP program | The two AUVs made nine deep dives during the expedition. The longest mission lasted over 30 h and dived up to 4000 m water depth. | Marked the first instance of AUVs with deployment and recovery through ice into the deep ocean (over 3500 m) for scientific research. | [58] |
19 | 2010 | SeaBED | Weddell and Bellingshausen Seas, Antarctica | British Antarctic Survey, UK-led ICEBell voyage, UK National Environmental Research Council | The SeaBED AUV specializes in single floe-scale sea ice measurements up to 500 m × 500 m. The missions resulted in ten floe-scale sea ice draft maps collected in three different coastal regions around Antarctica. | Enabled the first-ever coincident high-resolution 3D mapping of both upper and lower surfaces of Antarctic sea ice, revealing extensive deformation and a mean sea ice draft significantly greater than typically observed in drilling data. | [20,60,61] |
20 | 2012 | East Antarctica | University of Tasmania, Australian-led SIPEX II, Antarctic Climate and Ecosystems Cooperative Research Center | ||||
21 | 2009, 2010, 2011, 2012, 2013 | PAUL (Bluefin) | At the edge of a large ice tongue in the Fram Strait, Arctic | AWI, HGF-Research Program PACES and Helmholtz Alliance ROBEX | The AUV traversed two cross-front sections of 9 km between 0 and 50 m water depth at a horizontal station spacing of 800–1000 m. | Captured detailed vertical profiles of physical and biogeochemical properties at a moving ice edge. | [64] |
22 | 2010 | ISE Explorer | Canada’s high Arctic | ISE, NRCan | The AUV operated for 10 days under the ice, conducting approximately 1000 km of under-ice survey over the course of three missions. | Conducted under-ice bathymetric surveys. | [11,65] |
23 | 2019 | nupiri muka | Sørsdal Ice Shelf in East Antarctica | University of Tasmania, Antarctic Gateway Partnership, Australian Research Council | Nine missions were conducted along the calving front, with two missions beneath the ice shelf. | Measured temperature, salinity, and water currents and revealed the presence of cold, salty water under the ice shelf, and a deep seafloor trough at the shelf’s entrance. | [68] |
24 | 2020 | nupiri muka | Thwaites Glacier in West Antarctica | University of Tasmania, Antarctic Gateway Partnership, Australian Research Council | Six missions were completed including a significant 60 km round trip along the seabed beneath a sea ice barrier | Mapped the influx of warm water and collected 46 trace metal-free water samples. | [69] |
25 | 2007 | Gavia | Beaufort Sea, Arctic | University of Cambridge, SEDNA project, NSF Office of Polar Programs | The vehicle was tethered by a 400 m Kevlar line during the missions. A series of sonar swathes (over 200 m long, 80 m width) were collected. | The first 3D digital terrain mapping of the underside of sea ice was conducted by an ice-launched AUV. The interferometric sonar imagery revealed morphological distinctions between first-year and multi-year ice undersides. | [72] |
26 | 2008 | Gavia | Lincoln Sea, Arctic | University of Cambridge, DAMOCLES project, European Union 6th Framework Program | 24 tethered missions were completed within an area of 500 m × 500 m. | Mapped the ice draft in the local area with the Geoswath unit, measured the water profiles with the CTD module, and investigated the horizontal variability in light transmission under sea ice with a hyper-spectral radiometer. | [70,71] |
27 | 2011 | Gavia | Lancaster Sound and Baffin Bay, Arctic | University of British Columbia, Canadian ArcticNet program, Canadian Ice Service | The AUV mapped a roughly 700 m × 500 m area of the underside of PII-B. | The AUV’s mapping of the underside of PII-B, together with a surface vessel’s sidewall survey, resulted in a 3D terrain map of the ice island’s submerged section. | [73] |
28 | 2010 | REMUS-100 | Ny-Alesund, Svalbard, Norway, Arctic | University Centre on Svalbard, Norwegian Research Council-funded projects | AUV missions surveyed a transect of 1.5 km at different depths during day and night. | Detected bioluminescence among zooplankton during the polar night using a bathyphotometer. | [74] |
29 | 2010 | REMUS-100 | Offshore of Barrow, Alaska, Arctic | WHOI, grant from Ocean and Climate Change Institute, Richard B. Sellars Foundation | Both tethered test missions and untethered survey missions were conducted, including a survey in a “mow the lawn” pattern centered on the ice floe, featuring three 400 m track lengths along floe lines at a depth of 6 m. | Acquired cross-shore hydrographic profiles, detailing variations in temperature, salinity, and velocity at different depths. | [75] |
30 | 2014 | REMUS-100 | Ny-Alesund, Svalbard, Norway, Arctic | NTNU, Centre for Research-based Innovation SAMCoT, Centre of Excellence AMOS, KMB Arctic DP, Research Council of Norway | During the cross-fjord survey, the vehicle traveled over 16 h and more than 88 km. | Used for seafloor mapping and collection of oceanographic parameters. | [76] |
31 | 2020, 2022 | Icefin | Thwaites Glacier, Antarctic | Georgia Institute of Technology, MELT project, International Thwaites Glacier Collaboration | The vehicle conducted a 15 km round-trip mission. | Marked the first vehicle to explore the grounding line of Thwaites Glacier, gathering crucial environmental data, along with sonar and optical imagery. | [78] |
32 | 2016 | RAIV | Chukchi Sea, Arctic | JAMSTEC, Arctic Challenge for Sustainability | --- | Succeeded in autonomous navigation under ice in the Arctic Ocean for the first time in Japan, measured salinity and temperature of sea water, and captured images under sea ice. | [81,82] |
33 | 2021 | COMAI | Chukchi Sea, Arctic | JAMSTEC, Arctic Challenge for Sustainability II (ArCS II) Project | 4 test items were conducted during 8 dives. | The test results helped to fix problems and to improve the performance of the drone, which was planned to be used for under-ice surveys in 2022. | [83] |
34 | 2022 | COMAI | Chukchi Sea, Arctic | JAMSTEC, Arctic Challenge for Sustainability II (ArCS II) Project | 4 test items were conducted. The total cruising distance was more than 200 m along the ice edge. | Measured the vertical profiles of temperature and salinity around the ice and mapped the underwater ice thickness. | [84] |
35 | 2023 | MONACA | Off the coast of Langhovde in Lütso Holm Bay, Antarctic | University of Tokyo, JSPS KAKENHI | In total, 20 dives were conducted, with 6 sub-ice surveys, 2 mid-ocean explorations, 5 submarine topographic surveys, and 1 observation of the ice shelf edge of Langhovde Glacier. | Deployed the first Japanese AUV in the Antarctic, obtained bathymetry, seawater temperature, and salinity measurements. | [85,86] |
36 | 2008, 2010, 2014 | Polar ARV | Long-term ice station of the 6th CHINARE at 81°N, Arctic | SIA, CAS, Chinese National 863 Program fund | Polar ARV operated for 7 days, covering a total distance of 9 km beneath the ice. | Measured spectral irradiance, ice draft, temperature, and conductivity, and recorded images and videos beneath the ice. | [87] |
37 | 2019, 2020 | TS-1000 | Ross Sea at 75°S, Antarctic | SIA, CAS, Strategic Priority Research Program of CAS | The AUV conducted 17 profile survey missions and traveled a total of 68 km. | Collected extensive hydrological data including measurements of ocean currents, temperature, salinity, turbidity, dissolved oxygen, and chlorophyll. | [88] |
38 | 2021 | TS-4500 | High latitudes of the Arctic | SIA, CAS | --- | Marked China’s first use of an AUV for near-seabed exploration in the Arctic collecting data about the floating ice, the waters, and the seabed. | [90] |
39 | 2022 | Seafloor Mapping (Dorado) | Canadian Beaufort Sea, Inuvialuit Settlement Region, Arctic | MBARI | Several sinkhole-like valleys as large as the size of a city with six-story buildings were recorded by the two AUVs. | Gathered seafloor mapping information using a swath multibeam sonar, two sidescan sonars, and a sub-bottom profiler, all rated for depths up to 6000 m. | [92] |
40 | 2023 | XH1000 | Chukchi Sea, Arctic | Harbin Engineering University | The vehicle mapped an area of 7000 square meters beneath the Arctic ice. | Collected detailed data on ice tomography and water properties. | [93] |
References
- Singh, H.; Maksym, T.; Wilkinson, J.; Williams, G. Inexpensive, small AUVs for studying ice-covered polar environments. Sci. Robot. 2017, 2, eaan4809. [Google Scholar] [CrossRef]
- Broecker, W.S. The great ocean conveyor. Oceanography 1991, 4, 79–89. [Google Scholar] [CrossRef]
- Wadhams, P.; Krogh, B. Operational history and development plans for the use of AUVs and UAVs to map sea ice topography. Polar Sci. 2019, 21, 195–203. [Google Scholar] [CrossRef]
- Nicholls, K.W.; Abrahamsen, E.P.; Buck, J.J.H.; Dodd, P.A.; Goldblatt, C.; Griffiths, G.; Heywood, K.J.; Hughes, N.E.; Kaletzky, A.; Lane-Serff, G.F.; et al. Measurements beneath an Antarctic ice shelf using an autonomous underwater vehicle. Geophys. Res. Lett. 2006, 33, L08612. [Google Scholar] [CrossRef]
- Orsi, A.H.; Smethie, W.M.; Bullister, J.L. On the total input of Antarctic waters to the deep ocean: A preliminary estimate from chlorofluorocarbon measurements. J. Geophys. Res. 2002, 107, 3122. [Google Scholar] [CrossRef]
- Payne, A.J.; Vieli, A.; Shepherd, A.P.; Wingham, D.J.; Rignot, E. Recent dramatic thinning of largest West Antarctic ice stream triggered by oceans. Geophys. Res. Lett. 2004, 31, L23401. [Google Scholar] [CrossRef]
- Quetin, L.B.; Ross, R.M.; Frazer, T.K.; Haberman, K.L. Factors affecting distribution and abundance of zooplankton, with an emphasis on Antarctic krill, Euphausia Superba. Antarct. Res. Ser. 1996, 70, 357. [Google Scholar] [CrossRef]
- Loeb, V.; Siegel, V.; Holm-Hansen, O.; Hewitt, R.; Fraser, W.; Trivelpiece, W.; Trivepliece, S. Effects of sea-ice extent and krill or salp dominance on the Antarctic food web. Nature 1997, 387, 897–900. [Google Scholar] [CrossRef]
- Brierley, A.S.; Millard, N.W.; McPhail, S.D.; Stevenson, P.; Pebody, M.; Perrett, J.; Squires, M.; Griffiths, G. Antarctic krill under sea ice: Elevated abundance in a narrow band just south of ice edge. Science 2002, 295, 1890–1892. [Google Scholar] [CrossRef]
- Wilkinson, J.P.; Wadhams, P.; Hughes, N.E. Modelling the spread of oil under fast sea ice using three-dimensional multibeam sonar data. Geophys. Res. Lett. 2007, 34, L22506. [Google Scholar] [CrossRef]
- Kaminski, C.; Crees, T.; Ferguson, J.; Forrest, A.; Williams, J.; Hopkin, D.; Heard, G. 12 days under ice—An historic AUV deployment in the Canadian High Arctic. In Proceedings of the 2010 IEEE/OES Autonomous Underwater Vehicles, Monterey, CA, USA, 1–3 September 2010; pp. 1–11. [Google Scholar] [CrossRef]
- Wadhams, P. The use of autonomous underwater vehicles to map the variability of under-ice topography. Ocean Dyn. 2012, 62, 439–447. [Google Scholar] [CrossRef]
- Sheng, E.L. The Polar Silk Road and the Belt and Road Initiative: Integration and Optimization. In Arctic Opportunities and Challenges; Palgrave Macmillan: Singapore, 2022. [Google Scholar] [CrossRef]
- Qanittaq. The Clean Arctic Shipping Initiative. 2023. Available online: https://www.qanittaq.ca (accessed on 20 February 2024).
- Duarte, P.; Sundfjord, A.; Meyer, A.; Hudson, S.R.; Spreen, G.; Smedsrud, L.H. Warm Atlantic water explains observed sea ice melt rates north of Svalbard. J. Geophys. Res. Ocean. 2020, 125, e2019JC015662. [Google Scholar] [CrossRef]
- Fraser, A.D.; Wongpan, P.; Langhorne, P.J.; Klekociuk, A.R.; Kusahara, K.; Lannuzel, D.; Massom, R.A.; Meiners, K.M.; Swadling, K.M.; Atwater, D.P.; et al. Antarctic landfast sea ice: A review of its physics, biogeochemistry and ecology. Rev. Geophys. 2023, 61, e2022RG000770. [Google Scholar] [CrossRef]
- Lyon, W.K. Ocean and sea-ice research in the Arctic Ocean via submarine. N. Y. Acad. Sci. 1961, 2, 662–674. [Google Scholar] [CrossRef]
- Wadhams, P. The underside of Arctic sea ice imaged by sidescan sonar. Nature 1988, 333, 161–164. [Google Scholar] [CrossRef]
- Wadhams, P. Arctic sea ice changes under global warming. In Proceedings of the SNAME 8th International Conference on Performance of Ships and Structures in Ice, Banff, AB, Canada, 20–23 July 2008; pp. 1–8. [Google Scholar] [CrossRef]
- Williams, G.D.; Maksym, T.; Wilkinson, J.; Kunz, C.; Trujillo, E.; Steer, A.; Kimball, P.; Massom, R.; Meiners, K.; Leonard, K.; et al. Beyond point measurements: 3-D characterization of sea ice floes. Eos Trans. Am. Geophys. Union 2013, 94, 69–70. [Google Scholar] [CrossRef]
- Gherardi, M.; Lagomarsino, M.C. Characterizing the size and shape of sea ice floes. Sci. Rep. 2015, 5, 10226. [Google Scholar] [CrossRef] [PubMed]
- Jeffrey, M.; Maksym, T.; Weissling, B.; Singh, H. Estimating early-winter Antarctic sea ice thickness from deformed ice morphology. Cryosphere 2019, 13, 2915–2934. [Google Scholar] [CrossRef]
- Dowdeswell, J.A.; Evans, J.; Mugford, R.; Griffiths, G.; McPhail, S.; Millard, N.; Stevenson, P.; Brandon, M.A.; Banks, C.; Heywood, K.J.; et al. Autonomous underwater vehicles (AUVs) and investigations of the ice–ocean interface in Antarctic and Arctic waters. J. Glaciol. 2008, 54, 661–672. [Google Scholar] [CrossRef]
- Fan, S.; Zhang, X.; Zeng, G.; Cheng, X. Underwater ice adaptive mapping and reconstruction using autonomous underwater vehicles. Front. Mar. Sci. 2023, 10, 1124752. [Google Scholar] [CrossRef]
- Griffiths, G. Fifty years and counting: Applications of AUVs in the polar regions. In Proceedings of the 2020 IEEE/OES Autonomous Underwater Vehicles Symposium (AUV), St. Johns, NL, Canada, 30 September–2 October 2020; pp. 1–6. [Google Scholar] [CrossRef]
- Francois, R.E.; Nodland, W.K. Unmanned Arctic Research Submersible (UARS) System Development and Test Report; Technical Report APL-UW 7219, 88; University of Washington, Applied Physics Laboratory: Seattle, WA, USA, 1972. [Google Scholar]
- Murphy, S. Arctic Technology Development at the University of Washington; Technical Report DTIC_AD0748228; University of Washington, Applied Physics Laboratory: Seattle, WA, USA, 1972. [Google Scholar]
- Francois, R.E. High Resolution Observations of Under-Ice Morphology; Technical Report APL-UW 7712, 30; University of Washington, Applied Physics Laboratory: Seattle, WA, USA, 1977. [Google Scholar]
- Francois, R.E. The Unmanned Arctic Research Submersible System, Mar. Technol. Soc. J. 2006, 40, 75–77. [Google Scholar] [CrossRef]
- Morison, J.H.; McPhee, M.G. Lead convection measured with an autonomous underwater vehicle. J. Geophys. Res. Ocean. 1998, 103, 3257–3281. [Google Scholar] [CrossRef]
- McPhee, M.G.; Ackley, S.F.; Guest, P.; Huber, B.A.; Martinson, D.G.; Morison, J.H.; Muench, R.D.; Padman, L.; Stanton, T.P. The Antarctic zone flux experiment. Bull. Am. Meteorol. Soc. 1996, 77, 1221–1232. [Google Scholar] [CrossRef]
- Hayes, D.; Morison, J. Determining turbulent vertical velocity, and fluxes of heat and salt with an autonomous underwater vehicle. J. Atmos. Ocean. Technol. 2002, 19, 759–779. [Google Scholar] [CrossRef]
- Bellingham, J.G.; Goudey, C.A.; Consi, T.R.; Bales, J.W.; Atwood, D.K.; Leonard, J.J.; Chryssostomidis, C. A second generation survey AUV. In Proceedings of the IEEE Symposium on Autonomous Underwater Vehicle Technology, Cambridge, MA, USA, 19–20 July 1994; pp. 148–155. [Google Scholar]
- Odyssey Class History. A History of the Odyssey-Class of Autonomous Underwater Vehicles. MIT Sea Grant. 2024. Available online: https://seagrant.mit.edu/auv-odyssey-class (accessed on 20 February 2024).
- Butler, B. Into the Labyrinth: The Making of a Modern-Day Theseus. 2018. Available online: https://www.brucebutler.ca/into-the-labyrinth.html (accessed on 20 February 2024).
- Ferguson, J.; Pope, A.; Butler, B.; Verrall, R.I. Theseus AUV: Two record breaking missions. Sea Technol. 1999, 40, 65–70. [Google Scholar]
- Theseus AUV. International Submarine Engineering Ltd. 2024. Available online: https://ise.bc.ca/product/theseus-auv (accessed on 20 February 2024).
- Bellingham, J.G.K.; Streitlien, K.; Overland, J.; Rajan, S.; Stein, P.; Stannard, I.; Kirkwood, W.; Yoerger, D. An Arctic Basin observational capability using AUVs. Oceanography 2000, 13, 64–70. [Google Scholar] [CrossRef]
- MBARI News. Underwater Robot Tested Beneath the Arctic Ice Sheet. Monterey Bay Aquarium Research Institute. 2002. Available online: https://www.mbari.org/news/underwater-robot-tested-beneath-the-arctic-ice-sheet (accessed on 20 February 2024).
- Tervalon, N.S.; Henthorn, R. Ice profiling sonar for an AUV: Experience in the Arctic. In Proceedings of the 2002 MTS/IEEE OCEANS, Biloxi, MI, USA, 29–31 October 2002; pp. 305–310. [Google Scholar] [CrossRef]
- Wadhams, P.; Wilkinson, J.P.; McPhail, S.D. A new view of the underside of Arctic sea ice. Geophys. Res. Lett. 2006, 33, L04501. [Google Scholar] [CrossRef]
- The Story of Autosub. National Oceanography Center 2022. Available online: https://noc.ac.uk/technology/technology-archive/story-autosub (accessed on 20 February 2024).
- Brandon, M.A.; Brierley, A.S.; Fernandes, P.G.; Armstrong, F.; Millard, N.W.; McPhail, S.D.; Stevenson, P.; Pebody, M.; Perrett, J.; Squires, M.; et al. Measurements of the sea ice Thickness Distribution and Icebergs Using the Autonomous Underwater Vehicle Autosub 2 in Antarctica. In Proceedings of the American Geophysical Union, San Francisco, CA, USA, 10–14 December 2001; pp. 1–6. [Google Scholar]
- McPhail, S.D.; Furlong, M.E.; Pebody, M.; Perrett, J.R.; White, D. Exploring beneath the PIG Ice Shelf with the Autosub3 AUV. In Proceedings of the OCEANS 2009-EUROPE, Bremen, Germany, 11–14 May 2009; pp. 1–8. [Google Scholar] [CrossRef]
- Wadhams, P.; Wilkinson, J.P.; Kaletzk, A. Sidescan sonar imagery of the winter marginal ice zone obtained from an AUV. J. Atmos. Ocean. Technol. 2004, 21, 1462–1470. [Google Scholar] [CrossRef]
- Strutt, J.E. Report of the Inquiry into the Loss of Autosub2 under the Fimbulisen. Research and Consultancy Report; National Oceanography Centre: Southampton, UK, 2006; p. 39. Available online: https://eprints.soton.ac.uk/41098 (accessed on 20 February 2024).
- Brito, M.; Griffiths, G.; Ferguson, J.; Hopkin, D.; Mills, R.; Pederson, R.; MacNeil, E. A behavioral probabilistic risk assessment framework for managing autonomous underwater vehicle deployments. J. Atmos. Ocean. Technol. 2012, 29, 1689–1703. [Google Scholar] [CrossRef]
- Jenkins, A.; Dutrieux, P.; Jacobs, S.S.; McPhail, S.D.; Perrett, J.R.; Webb, A.T.; White, D. Observations beneath Pine Island Glacier in West Antarctica and implications for its retreat. Nat. Geosci. 2010, 3, 468–472. [Google Scholar] [CrossRef]
- Kimura, S.; Jenkins, A.; Dutrieux, P.; Forryan, A.; Naveira Garabato, A.C.; Firing, Y. Ocean mixing beneath Pine Island Glacier ice shelf, West Antarctica. J. Geophys. Res. Ocean. 2016, 121, 8496–8510. [Google Scholar] [CrossRef]
- McPhail, S.; Templeton, R.; Pebody, M.; Roper, D.; Morrison, R. Autosub Long Range AUV missions under the Filchner and Ronne Ice Shelves in the Weddell Sea, Antarctica—An Engineering Perspective. In Proceedings of the OCEANS 2019, Marseille, France, 17–20 June 2019; pp. 1–8. [Google Scholar] [CrossRef]
- Davis, P.E.D.; Jenkins, A.; Nicholls, K.W.; Dutrieux, P.; Schröder, M.; Janout, M.A.; Hellmer, H.; Templeton, R.; McPhail, S. Observations of modified warm deep water beneath Ronne Ice Shelf, Antarctica, from an Autonomous Underwater Vehicle. J. Geophys. Res. Ocean. 2022, 127, e2022JC019103. [Google Scholar] [CrossRef]
- TARSAN. Thwaites-Amundsen Regional Survey and Network Integrating Atmosphere-Ice-Ocean Processes (TARSAN) Project. The International Thwaites Glacier Collaboration. 2018. Available online: https://thwaitesglacier.org/projects/tarsan (accessed on 20 February 2024).
- Wåhlin, A.K.; Graham, A.G.C.; Hogan, K.A.; Queste, B.Y.; Boehme, L.; Larter, R.D.; Pettit, E.; Wellner, J.; Heywood, K.J. Pathways and modification of warm water flowing beneath Thwaites Ice Shelf, West Antarctica. Sci. Adv. 2021, 7, eabd7254. [Google Scholar] [CrossRef] [PubMed]
- Graham, A.G.C.; Wåhlin, A.K.; Hogan, K.A.; Nitsche, F. Rapid retreat of Thwaites Glacier in the pre-satellite era. Nat. Geosci. 2022, 15, 706–713. [Google Scholar] [CrossRef]
- Wåhlin, A.K.; Bastien, Y.Q.; Graham, A.G.C.; Hogan, K.A.; Boehme, L.; Heywood, K.J.; Larter, R.D.; Pettit, E.; Wellner, J.S. Warm Water Flow and Mixing Beneath Thwaites Glacier Ice Shelf, West Antarctica. 2020. Available online: https://presentations.copernicus.org/EGU2020/EGU2020-19934_presentation.pdf (accessed on 20 February 2024).
- NOC News. Boaty McBoatface Returns from Thwaites Glacier. National Oceanography Center. 2022. Available online: https://noc.ac.uk/news/boaty-mcboatface-returns-thwaites-glacier (accessed on 20 February 2024).
- ITGC News. Final Mission for the Underwater Robot ‘Ran’ under Thwaites Glacier. The International Thwaites Glacier Collaboration. 2024. Available online: https://thwaitesglacier.org/news/final-mission-auv-under-thwaites-glacier (accessed on 20 February 2024).
- Kunz, C.; Murphy, C.; Singh, H.; Pontbriand, C.; Sohn, R.A.; Singh, S.; Sato, T.; Roman, C.; Nakamura, K.; Jakuba, M. Toward extraplanetary under-ice exploration: Robotic steps in the Arctic. J. Field Robot. 2009, 26, 411–429. [Google Scholar] [CrossRef]
- Williams, G.; Turner, D.; Maksym, T.; Singh, H. Near-coincident mapping of sea ice from above and below with UAS and AUV. In Proceedings of the 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV), Porto, Portugal, 6–9 November 2018; pp. 1–6. [Google Scholar] [CrossRef]
- Williams, G.; Maksym, T.; Wilkinson, J.; Kunz, C.; Singh, H. Thick and deformed Antarctic sea ice mapped with autonomous underwater vehicles. Nat. Geosci. 2015, 8, 61–67. [Google Scholar] [CrossRef]
- Williams, G.D.; Junz, C.; Kimball, P.; Frost, R.; Alexander, P. 3-D Mapping of Sea Ice Draft with an Autonomous Underwater Vehicle; Ver. 1; Australian Antarctic Data Centre: Kingston, TAS, Australia, 2017. [Google Scholar] [CrossRef]
- SeaBED. Hanumant Singh Lab. Woods Hole Oceanographic Institution. 2024. Available online: https://web.whoi.edu/singh/auvasf/seabed (accessed on 20 February 2024).
- Wulff, T.; Lehmenhecker, S.; Bauerfeind, E.; Hoge, U.; Shurn, K.; Klages, M. Biogeochemical research with an Autonomous Underwater Vehicle: Payload structure and arctic operations. In Proceedings of the OCEANS, Bergen, Norway, 10–14 June 2013; pp. 1–10. [Google Scholar] [CrossRef]
- Wulff, T.; Bauerfeind, E.; Appen, W.v. Physical and ecological processes at a moving ice edge in the Fram Strait as observed with an AUV. Deep. Sea Res. Part I Oceanogr. Res. Pap. 2016, 115, 253–264. [Google Scholar] [CrossRef]
- Crees, T.; Kaminski, C.; Ferguson, J.; Laframboise, J.M.; Forrest, A.; Williams, J.; MacNeil, E.; Hopkin, D.; Pederson, R. UNCLOS under ice survey—An historic AUV deployment in the Canadian high arctic. In Proceedings of the OCEANS, Seattle, WA, USA, 20–23 September 2010; pp. 1–8. [Google Scholar]
- AGP. Antarctic Gateway Partnership. Institute for Marine and Antarctic Studies, University of Tasmania. 2020. Available online: https://www.imas.utas.edu.au/antarctic-gateway-partnership/home (accessed on 20 February 2024).
- King, P.; Williams, G.; Coleman, R.; Zürcher, K.; Bowden-Floyd, I.; Ronan, A.; Kaminski, C.; Laframboise, J.M.; McPhail, S.; Wilkinson, J.; et al. Deploying an AUV beneath the Sørsdal Ice Shelf: Recommendations from an expert-panel workshop. In Proceedings of the 2018 IEEE/OES Autonomous Underwater Vehicle Workshop, Porto, Portugal, 6–9 November 2018; pp. 1–6. [Google Scholar] [CrossRef]
- Gwyther, D.E.; Spain, E.; King, P.; Guihen, D.; Williams, G.D.; Evans, E.; Cook, S.; Richter, O.; Galton-Fenzi, B.K.; Coleman, R. Cold ocean cavity and weak basal melting of the Sørsdal ice shelf revealed by surveys using autonomous platforms. J. Geophys. Res. Ocean. 2020, 125, e2019JC015882. [Google Scholar] [CrossRef]
- King, P.; Ziürcher, K.; Bowden-Floyd, I. A risk-averse approach to mission planning: Nupiri muka at the Thwaites Glacier. In Proceedings of the 2020 IEEE/OES Autonomous Underwater Vehicles Symposium, St. Johns, NL, Canada, 30 September–2 October 2020; pp. 1–5. [Google Scholar] [CrossRef]
- Doble, M.; Wadhams, P.; Forrest, A.; Laval, B. Experiences from two-years’ through ice AUV deployments in the high Arctic. In Proceedings of the 2008 IEEE/OES Autonomous Underwater Vehicles, Woods Hole, MA, USA, 13–14 October 2008; pp. 1–7. [Google Scholar]
- Doble, M.J.; Forrest, A.L.; Wadhams, P.; Laval, B.E. Through-ice AUV deployment: Operational and technical experience from two seasons of arctic fieldwork. Cold Reg. Sci. Technol. 2009, 56, 90–97. [Google Scholar] [CrossRef]
- Wadhams, P.; Doble, M.J. Digital terrain mapping of the underside of sea ice from a small AUV. Geophys. Res. Lett. 2008, 35, L01501. [Google Scholar] [CrossRef]
- Forrest, A.L.; Hamilton, A.K.; Schmidt, V.E.; Laval, B.E.; Mueller, D.; Crawford, A.J.; Brucker, S.; Hamilton, T. Digital terrain mapping of Petermann Ice Island fragments in the Canadian high arctic. In Proceedings of the 21st IAHR International Symposium on Ice 2012, Dalian, China, 11–15 June 2012; pp. 710–721. [Google Scholar]
- Berge, J.; Båtnes, A.S.; Johnsen, G.; Blackwell, S.M.; Moline, M.A. Bioluminescence in the high Arctic during the polar night. Mar. Biol. 2011, 159, 231–237. [Google Scholar] [CrossRef]
- Plueddemann, A.J.; Kukulya, A.L.; Stokey, R.; Freitag, L. Autonomous underwater vehicle operations beneath coastal sea ice. IEEE/ASME Trans. Mechatron. 2012, 17, 54–64. [Google Scholar] [CrossRef]
- Norgren, P.; Skjetne, R. Using autonomous underwater vehicles as sensor platforms for ice-monitoring. Model. Ident. Control. 2014, 35, 263–277. [Google Scholar] [CrossRef]
- Meister, M.; Dichek, D.; Spears, A.; Hurwitz, B.; Ramey, C.; Lawrence, J.; Philleo, K.; Lutz, J.; Lawrence, J.; Schmidt, B.E. Icefin: Redesign and 2017 Antarctic field deployment. In Proceedings of the OCEANS 2018 MTS/IEEE, Charleston, SC, USA, 22–25 October 2018; pp. 1–5. [Google Scholar] [CrossRef]
- Schmidt, B.; Nicholls, K.; Davis, P.; Smith, J.; Riverman, K.; Holland, D.; Dichek, D.; Mullen, A.; Lawrence, J.; Washam, P.; et al. The Grounding Zone of Thwaites Glacier Explored by Icefin. In Proceedings of the EGU General Assembly 2020, Online, 4–8 May 2020; pp. 4–8. [Google Scholar] [CrossRef]
- Washam, P.; Lawrence, J.D.; Stevens, C.L.; Hulbe, C.; Horgan, H.J.; Robinson, N.J.; Stewart, C.L.; Spears, A.; Quartini, E.; Hurwitz, B.; et al. Direct observations of melting, freezing, and ocean circulation in an ice shelf basal crevasse. Sci. Adv. 2023, 9, eadi7638. [Google Scholar] [CrossRef]
- Lawrence, J.D.; Hulbe, C.; Schmidt, B.E. Robotic exploration of sub-ice shelf melting and freezing processes. Nat. Geosci. 2023, 16, 198–199. [Google Scholar] [CrossRef]
- Noguchi, T. Succeeded in Photographing the Arctic Ocean under Sea Ice Using a Small AUV Prototype. Japan Agency for Marine-Earth Science and Technology. 2016. Available online: https://www.jamstec.go.jp/j/about/press_release/20161124 (accessed on 20 February 2024).
- ArCS. R/V Miral Cruise Report MR16-06 (MR16-Nishino). Japan Agency for Marine-Earth Science and Technology. 2016. Available online: https://www.jamstec.go.jp/iace/e/report/pdf/2016.MR16-06.pdf (accessed on 20 February 2024).
- ArCS II. R/V Miral Cruise Report MR21-05C. Japan Agency for Marine-Earth Science and Technology. 2021. Available online: https://www.godac.jamstec.go.jp/cr_catalog/external/metadata/MR21-05C_all/file/MR21-05C_all.pdf (accessed on 20 February 2024).
- ArCS II. R/V Miral Cruise Report MR22-06C (MR22-Itoh). Japan Agency for Marine-Earth Science and Technology. 2022. Available online: https://www.godac.jamstec.go.jp/cr_catalog/external/metadata/MR22-06C_all/file/MR22-06C_all.pdf (accessed on 20 February 2024).
- Yamagata, H.; Kochii, S.; Yoshida, H.; Nogi, Y.; Maki, T. Development of AUV MONACA—Hover-capable platform for detailed observation under ice. J. Robot. Mechatron. 2021, 33, 1223–1233. [Google Scholar] [CrossRef]
- Yamaguchi, S. AUV MONACA Observation in Ljuzo Holm Bay. National Institute of Polar Research. 2023. Available online: https://nipr-blog.nipr.ac.jp/jare/20230211auv-monaca.html (accessed on 20 February 2024). (In Japanese).
- Zeng, J.B.; Li, S.; Li, Y.P.; Wang, X.H.; Chen, Q.; Lei, R.B.; Li, T. The observation of sea-ice in the sixth Chinese National Arctic Expedition using polar-ARV. In Proceedings of the MTS/IEEE OCEANS, Washington, DC, USA, 19–22 October 2015; pp. 19–22. [Google Scholar] [CrossRef]
- Zeng, J.B.; Li, S.; Liu, Y. Application of unmanned underwater vehicles in polar research. Adv. Polar Sci. 2021, 32, 173–184. [Google Scholar]
- Liu, T.; Jiang, Z.; Li, S.; Gu, H. Explorer1000: A long endurance AUV with variable ballast systems. In Proceedings of the MTS/IEEE OCEANS, Kobe, Japan, 28–31 May 2018; pp. 1–6. [Google Scholar] [CrossRef]
- Wu, Y.H. China’s Self-Developed AUV Shows Impressive Capabilities in Latest Arctic Scientific Expedition. China Story, 2021. Available online: https://www.chinastory.cn/PCywdbk/v2/detail/20211102/1012700000042741635842036151133533_1.html (accessed on 18 August 2024).
- Tao, L. Record of Chinese ninth Arctic scientific expedition (3): Successful deployment of “Haiyi” underwater glider. Encyclopedia 2019, 2019, 28–29. [Google Scholar]
- Lundsten, E.; Paul, C.K. Arctic Expedition Fall 2022. Monterey Bay Aquarium Research Institute. 2022. Available online: https://www.mbari.org/expedition/arctic-expedition-fall-2022 (accessed on 20 February 2024).
- Huaxia. China-Made Robotic Vehicle Explores Underside of Arctic Ice. XinhuaNet 2023. Available online: https://english.news.cn/20231008/bd459e0284384cbeb25a722c0bdf6a9e/c.html (accessed on 20 February 2024).
- Polar AUV Guide. Society for Underwater Technology. 2022. Available online: http://wsprdaemon.org/polar/chronology.html (accessed on 20 February 2024).
- Spreen, G.; Melsheimer, C.; Gerken, M. Sea Ice Concentration Data on January 1 in Arctic and Antarctic, 2024. Institute of Environmental Physics, University of Bremen, Germany. 2024. Available online: https://data.seaice.uni-bremen.de/databrowser/ (accessed on 20 February 2024).
- Paull, L.; Saeedi, S.; Seto, M.; Li, H. AUV navigation and localization: A review. IEEE J. Ocean. Eng. 2014, 39, 131–149. [Google Scholar] [CrossRef]
- Barker, L.D.L.; Jakuba, M.V.; Bowen, A.D.; German, C.R.; Maksym, T.; Mayer, L.; Boetius, A.; Dutrieux, P.; Whitcomb, L.L. Scientific challenges and present capabilities in underwater robotic vehicle design and navigation for oceanographic exploration under-ice. Remote Sens. 2020, 12, 2588. [Google Scholar] [CrossRef]
- Barker, L.D.L.; Whitcomb, L.L. Performance analysis of ice-relative upward-looking doppler navigation of underwater vehicles beneath moving sea ice. J. Mar. Sci. Eng. 2021, 9, 174. [Google Scholar] [CrossRef]
- Norgren, P.; Mo-Bjørkelund, T.; Gade, K.; Hegrenæs, Ø.; Ludvigsen, M. Intelligent buoys for aiding AUV navigation under the ice. In Proceedings of the 2020 IEEE/OES Autonomous Underwater Vehicles Symposium, St. Johns, NL, Canada, 30 September–2 October 2020; pp. 1–7. [Google Scholar] [CrossRef]
- Nicholls, K.W.; Abrahamsen, E.P.; Heywood, K.J.; Stansfifield, K.; Østerhus, S. High-latitude oceanography using the Autosub autonomous underwater vehicle. Limnol. Oceanogr. 2008, 53, 2309–2320. [Google Scholar] [CrossRef]
- Kepper, J.H.; Claus, B.C.; Kinsey, J.C. A Navigation Solution Using a MEMS IMU, Model-based dead-reckoning, and one-way-travel-time acoustic range measurements for autonomous underwater vehicles. IEEE J. Ocean. Eng. 2019, 44, 664–682. [Google Scholar] [CrossRef]
- Liang, Z.; Tian, M.; Liao, Z.; Wang, C.; Li, J. Adaptive robust Kalman filter for AUV polar integrated navigation. In Proceedings of the 2022 International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM), Xiamen, China, 5–7 August 2022; pp. 99–105. [Google Scholar] [CrossRef]
- McEwen, R.; Thomas, H.; Weber, D.; Psota, F. Performance of an AUV navigation system at Arctic latitudes. IEEE J. Ocean. Eng. 2005, 30, 443–454. [Google Scholar] [CrossRef]
- Salavasidis, G.; Munafò, A.; McPhail, S.D.; Harris, C.A.; Fenucci, D.; Pebody, M.; Rogers, E.; Phillips, A.B. Terrain-aided navigation with coarse maps: Toward an Arctic crossing with an AUV. IEEE J. Ocean. Eng. 2021, 46, 1192–1212. [Google Scholar] [CrossRef]
- King, P.; Vardy, A.; Forrest, A.L. Teach-and-repeat path following for an autonomous underwater vehicle. J. Field Robot. 2018, 35, 748–763. [Google Scholar] [CrossRef]
- Underwater GPS. Discovery of Sound in the Sea. University of Rhode Island and Inner Space Center. 2024. Available online: https://dosits.org/galleries/technology-gallery/navigation-technology/underwater-gps (accessed on 20 February 2024).
- O’Hara, C.A.; Collis, J.M. Underwater acoustic propagation in Arctic environments. J. Acoust. Soc. Am. 2011, 130, 2529. [Google Scholar] [CrossRef]
- Lewis, R.S.; Drogou, M.; King, P.; Mann, G.; Bose, N.; Worby, A. An acoustic signal propagation experiment beneath sea ice. Ocean Eng. 2012, 43, 56–63. [Google Scholar] [CrossRef]
- Alexander, P.; Duncan, A.; Bose, N.; Williams, G. Modelling acoustic propagation beneath Antarctic sea ice using measured environmental parameters. Deep. Sea Res. Part II Top. Stud. Oceanogr. 2016, 131, 84–95. [Google Scholar] [CrossRef]
- Stojanovic, M. Recent advances in high-speed underwater acoustic communications. IEEE J. Ocean. Eng. 1996, 21, 125–136. [Google Scholar] [CrossRef]
- Johnson, M.; Herold, D.; Catipovic, J. The design and performance of a compact underwater acoustic network node. In Proceedings of the MTS/IEEE OCEANS, Brest, France, 13–16 September 1994; pp. 467–471. [Google Scholar] [CrossRef]
- Freitag, L.; Koski, P.; Morozov, A.; Singh, S.; Partan, J. Acoustic communications and navigation under Arctic ice. In Proceedings of the MTS/IEEE OCEANS, Hampton Roads, VA, USA, 14–19 October 2012; pp. 1–8. [Google Scholar] [CrossRef]
- Schneider, T.; Schmidt, H.; Randeni, S. Self-adapting under-ice integrated communications and navigation network. In Proceedings of the Fifth Underwater Communications and Networking Conference (UComms), Lerici, Italy, 31 August–2 September 2021; pp. 1–5. [Google Scholar] [CrossRef]
- Randeni, S.; Schneider, T.; Bhatt, E.C.; Víquez, O.A.; Schmidt, H. A high-resolution AUV navigation framework with integrated communication and tracking for under-ice deployments. J. Field Robot. 2023, 40, 346–367. [Google Scholar] [CrossRef]
- Li, D.; Wang, P.; Du, L. Path planning technologies for autonomous underwater vehicles: A review. IEEE Access 2021, 7, 9745–9768. [Google Scholar] [CrossRef]
- Cheng, C.X.; Sha, Q.X.; He, B.; Li, G.L. Path planning and obstacle avoidance for AUV: A review. Ocean Eng. 2021, 235, 109355. [Google Scholar] [CrossRef]
- Carroll, K.P.; McClaran, S.R.; Nelson, E.L.; Barnett, D.M.; Friesen, D.K.; William, G.N. AUV Path planning: An A* approach to path planning with consideration of variable vehicle speeds and multiple, overlapping, time-dependent exclusion zones. In Proceedings of the 1992 Symposium on Autonomous Underwater Vehicle Technology, Washington, DC, USA, 2–3 June 1992; pp. 79–84. [Google Scholar]
- Szczerba, R.J.; Galkowski, P.; Glicktein, I.S.; Ternullo, N. Robust algorithm for real-time route planning. IEEE Trans. Aerosp. Electron. Syst. 2000, 36, 869–878. [Google Scholar] [CrossRef]
- Alvarez, A.; Caiti, A. A genetic algorithm for autonomous underwater vehicle route planning in ocean environments with complex space-time variability. In Proceedings of the IFAC Control Applications in Marine System, Glasgow, Scotland, UK, 18–20 July 2001; Volume 34, pp. 237–242. [Google Scholar]
- Alvarez, A.; Caiti, A.; Onken, R. Evolutionary path planning for autonomous underwater vehicles in a variable ocean. IEEE J. Ocean. Eng. 2004, 29, 418–429. [Google Scholar] [CrossRef]
- Li, J.J.; Zhang, R.B.; Yang, Y. Research on route obstacle avoidance task planning based on differential evolution algorithm for AUV. In Proceedings of the International Conference in Swarm Intelligence, Hefei, China, 17–20 October 2014; pp. 106–113. [Google Scholar]
- Zhang, C.B.; Gong, Y.J.; Li, J.J.; Lin, Y. Automatic path planning for autonomous underwater vehicles based on an adaptive differential evolution. In Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, Vancouver, BC, Canada, 12–16 July 2014; pp. 89–96. [Google Scholar]
- Yang, G.; Zhang, R.B. Path planning of AUV in turbulent ocean environments using adapted inertia-weight PSO. In Proceedings of the Fifth International Conference on Natural Computation, Tianjian, China, 14–16 August 2009; Volume 3, pp. 299–302. [Google Scholar]
- Tang, X.Y.; Yu, F.; Chen, R.J. Path planning of underwater vehicle based on particle swarm optimization. In Proceedings of the 2010 International Conference on Intelligent Control and Information Processing, Dalian, China, 13–15 August 2010; pp. 123–126. [Google Scholar]
- Wang, H.J.; Wei, X. Research on global path planning based on ant colony optimization for AUV. J. Mar. Sci. Appl. 2009, 8, 58–64. [Google Scholar] [CrossRef]
- Zhang, G.L.; Jia, H.M. Global path planning of AUV based on improved ant colony optimization algorithm. In Proceedings of the 2012 IEEE International Conference on Automation and Logistics, Zhengzhou, China, 15–17 August 2012; pp. 606–610. [Google Scholar]
- Tan, C.S.; Sutton, R.; Chudley, J. Quasi-random, manoeuvre-based motion planning algorithm for autonomous underwater vehicles. In Proceedings of the 16th IFAC World Congress, Prague, Czech Republic, 3–8 July 2005; Volume 38, pp. 103–108. [Google Scholar]
- Hernández, E.; Carreras, M.; Antich, J.; Ridao, P.; Ortiz, A. A topologically guided path planner for an AUV using homotopy classes. In Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China, 9–13 May 2011; pp. 2337–2343. [Google Scholar]
- Ding, F.G.; Jiao, P.; Bian, X.Q.; Wang, H.J. AUV Local path planning based on virtual potential field. In Proceedings of the IEEE International Conference Mechatronics and Automation, Niagara Falls, ON, Canada, 29 July–1 August 2005; Volume 4, pp. 1711–1716. [Google Scholar]
- Saravanakumar, S.; Asokan, T. Multipoint potential field method for path planning of autonomous underwater vehicles in 3D space. Intell. Serv. Robot. 2013, 6, 211–224. [Google Scholar] [CrossRef]
- Khanmohammadi, S.; Alizadeh, G.; Poormahmood, M. Design of a fuzzy controller for underwater vehicles to avoid moving obstacles. In Proceedings of the 2007 IEEE International Fuzzy Systems Conference, London, UK, 23–26 July 2007; pp. 1–6. [Google Scholar]
- Xu, H.L.; Feng, X.S. An AUV fuzzy obstacle avoidance method under event feedback supervision. In Proceedings of the MTS/IEEE OCEANS, Biloxi, MS, USA, 26–29 October 2009; pp. 1–6. [Google Scholar]
- Duan, Q.J.; Zhang, M.; Zhang, Q. Local path planning method for AUV based on fuzzy-neural network. Ship Eng. 2001, 1, 54–58. [Google Scholar]
- Guerrero-González, A.; García-Córdova, F.; Gilabert, J. A biologically inspired neural network for navigation with obstacle avoidance in autonomous underwater and surface vehicles. In Proceedings of the MTS/IEEE OCEANS, Santander, Spain, 6–9 June 2011; pp. 1–8. [Google Scholar]
- Kawano, H.; Ura, T. Fast reinforcement learning algorithm for motion planning of nonholonomic autonomous underwater vehicle in disturbance. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Lausanne, Switzerland, 30 September–4 October 2002; Volume 1, pp. 903–908. [Google Scholar]
- Kawano, H.; Ura, T. Motion planning algorithm for nonholonomic autonomous underwater vehicle in disturbance using reinforcement learning and teaching method. In Proceedings of the 2002 IEEE International Conference on Robotics and Automation, Washington, DC, USA, 11–15 May 2002; Volume 4, pp. 4032–4038. [Google Scholar]
- Cao, X.; Sun, C.Y.; Yan, M.Z. Target search control of AUV in underwater environment with deep reinforcement learning. IEEE Access 2019, 7, 96549–96559. [Google Scholar] [CrossRef]
- Wu, H.; Song, S.J.; Hsu, Y.C.; You, K.Y.; Wu, C. End-to-end sensorimotor control problems of AUVs with deep reinforcement learning. In Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 3–8 November 2019; pp. 5869–5874. [Google Scholar]
- Yang, G.; Zhang, R.B.; Xu, D.; Zhang, Z.Y. Local planning of AUV based on Fuzzy-Q learning in strong sea flow field. In Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization, Sanya, China, 24–26 April 2009; Volume 1, pp. 994–998. [Google Scholar]
- Yan, Z.P.; Li, J.Y.; Wu, Y.; Yang, Z.W. A novel path planning for AUV based on objects’ motion parameters prediction. IEEE Access 2018, 6, 69304–69320. [Google Scholar] [CrossRef]
- Lim, H.S.; Chin, C.K.; Chai, S.; Bose, N. Online AUV path replanning using quantum-behaved particle swarm optimization with selective differential evolution. CMES Comput. Model. Eng. Sci. 2020, 125, 33–50. [Google Scholar]
- Pebody, M. Autonomous underwater vehicle collision avoidance for under-ice exploration. Proc. IMechE Part M J. Eng. Marit. Environ. 2008, 222, 53–66. [Google Scholar] [CrossRef]
- Millar, G. An obstacle avoidance system for autonomous underwater vehicles: A reflexive vector field approach utilizing obstacle localization. In Proceedings of the 2014 IEEE/OES Autonomous Underwater Vehicles (AUV), Oxford, MS, USA, 6–9 October 2014; pp. 1–4. [Google Scholar] [CrossRef]
- Hayashi, E.; Kimura, H.; Tam, C.; Ferguson, J.; Laframboise, J.M.; Miller, G.; Kaminski, C.; Johnson, A. Customizing an autonomous underwater vehicle and developing a launch and recovery system. In Proceedings of the 2013 IEEE International Underwater Technology Symposium (UT), Tokyo, Japan, 5–8 March 2013; pp. 1–7. [Google Scholar] [CrossRef]
- McFarlane, J.R.; Mackay, L. Autonomous underwater vehicle operations in the Arctic. In Proceedings of the MTS/IEEE OCEANS, Washington, DC, USA, 19–22 October 2015; pp. 1–6. [Google Scholar] [CrossRef]
- Chen, X.; Neil, B.; Mario, B.; Faisal, K.; Gina, M.; Craig, B.; Zou, T. Risk-based path planning for autonomous underwater vehicles in an oil spill environment. Ocean Eng. 2022, 266, 113077. [Google Scholar] [CrossRef]
- Bandhauer, T.M.; Garimella, S.; Fuller, T.F. A critical review of thermal issues in lithium-ion batteries. J. Electrochem. Soc. 2011, 158, R1. [Google Scholar] [CrossRef]
- Bradley, A.M.; Feezor, M.D.; Singh, H.; Yates Sorrell, F. Power systems for autonomous underwater vehicles. IEEE J. Ocean. Eng. 2001, 26, 526–538. [Google Scholar] [CrossRef]
- Phillips, A.B.; Pebody, M.; Marlow, R.; Fanelli, F.; Fenucci, D.; Roper, D. Autosub 2000 under ice: Design of a new work class AUV for under ice exploration. In Proceedings of the 2020 IEEE/OES Autonomous Underwater Vehicles Symposium, St. Johns, NL, Canada, 30 September–2 October 2020; pp. 1–8. [Google Scholar] [CrossRef]
- Wilson, R.A.; Bales, J.W. Development and experience of a practical pressure-tolerant lithium battery for underwater use. In Proceedings of the MTS/IEEE OCEANS 2006, Boston, MA, USA, 18–21 September 2006; pp. 1–5. [Google Scholar]
- McPhail, S. Autosub 6000: A deep diving long range AUV. J. Bionic Eng. 2009, 6, 55–62. [Google Scholar] [CrossRef]
- Roper, D.; Harris, C.A.; Salavasidis, G.; Pebody, M.; Templeton, R.; Prampart, T.; Kingsland, M.; Morrison, R.; Furlong, M.; Phillips, A.B.; et al. Autosub Long Range 6000: A multiple-month endurance AUV for deep-ocean monitoring and survey. IEEE J. Ocean. Eng. 2021, 46, 1179–1191. [Google Scholar] [CrossRef]
- Roper, D.T.; Phillips, A.; Harris, C.; Salavasidis, G.; Pebody, M.; Templeton, R.; Sithalakshmi, S.V.; Amma, S.; Smart, M.; Mcphail, S. Autosub long range 1500: An ultra-endurance AUV with 6000 km range. In Proceedings of the MTS/IEEE OCEANS, Aberdeen, UK, 19–22 June 2017; pp. 1–5. [Google Scholar] [CrossRef]
- Mendez, A.; Leo, T.J.; Herreros, M. Current state of technology of fuel cell power systems for autonomous underwater vehicles. Energies 2014, 7, 4676–4693. [Google Scholar] [CrossRef]
- Mendez, A.; Leo, T.J.; Herreros, M. Fuel cell power systems for autonomous underwater vehicles: State of the art. In Proceedings of the 1st International e-Conference on Energies, Online, 14–31 March 2014; pp. 1–19. [Google Scholar] [CrossRef]
- Weydahl, H.; Gilljam, M.; Lian, T.; Johannessen, T.C.; Holm, S.I.; Hasvold, J.Ø. Fuel cell systems for long-endurance autonomous underwater vehicles—Challenges and benefits. Int. J. Hydrogen Energy 2020, 45, 5543–5553. [Google Scholar] [CrossRef]
- Hasvold, Ø. A magnesium-seawater power source for autonomous underwater vehicles. In Proceedings of the 18th International Power Sources Symposium, Leatherhead, UK, 19–21 April 1993; pp. 243–255. [Google Scholar]
- Hasvold, Ø.; Johansen, K.H. The alkaline aluminium hydrogen peroxide semi-fuel cell for the HUGIN 3000 autonomous underwater vehicle. In Proceedings of the 2002 Workshop on Autonomous Underwater Vehicles, San Antonio, TX, USA, 21 June 2002; pp. 89–94. [Google Scholar]
- Hasvold, Ø.; Johannessen, T.C.; Forseth, J.S.; Lian, T. Exposure of lithium batteries to external hydrostatic pressure. In Proceedings of the 42nd Power Sources Conference, Philadelphia, PA, USA, 12–15 June 2006; pp. 75–78. [Google Scholar]
- HUGIN AUV. Autonomous and Uncrewed Solutions. Kongsberg. 2024. Available online: https://www.kongsberg.com/discovery/autonomous-and-uncrewed-solutions/auv/ (accessed on 18 August 2024).
- ARCS AUV. History. International Submarine Engineering. 2016. Available online: https://ise.bc.ca/history (accessed on 20 February 2024).
- Gilljam, M.; Weydahl, H.; Lian, T.; Johannessen, T.C.; Holm, S.I.; Hasvold, J.Ø. 24 hour test of a fuel cell system for an autonomous underwater vehicle. ECS Trans. 2016, 71, 145–154. Available online: https://iopscience.iop.org/article/10.1149/07101.0145ecst (accessed on 20 February 2024). [CrossRef]
- Borgogna, G.; Lamberti, T.; Massardo, A.F. Innovative power system for autonomous underwater vehicle. In Proceedings of the MTS/IEEE OCEANS, Genova, Italy, 18–21 May 2015; pp. 1–8. [Google Scholar] [CrossRef]
- Chiche, A.; Lindbergh, G.; Stenius, I.; Lagergren, C. A strategy for sizing and optimizing the energy system on long-range AUVs. IEEE J. Ocean. Eng. 2021, 46, 1132–1143. [Google Scholar] [CrossRef]
- Wang, X.M.; Shang, J.Z.; Luo, Z.R.; Tang, L.; Zhang, X.P.; Li, J. Reviews of power systems and environmental energy conversion for unmanned underwater vehicles. Renew. Sustain. Energy Rev. 2011, 16, 1958–1970. [Google Scholar] [CrossRef]
- Ferguson, J. Adapting AUVs for use in under-ice scientific missions. In Proceedings of the MTS/IEEE OCEANS 2008, Quebec City, QC, Canada, 15–18 September 2008; pp. 1–5. [Google Scholar]
- Ferguson, J. The Theseus autonomous underwater vehicle. Two successful missions. In Proceedings of the 1998 International Symposium on Underwater Technology, Tokyo, Japan, 17 April 1998; pp. 109–114. [Google Scholar]
- Kukulya, A.; Plueddemann, A.; Austin, T.; Stokey, R.; Purcell, M.; Allen, B.; Littlefield, R.; Freitag, L.; Koski, P.; Gallimore, E.; et al. Under-ice operations with a REMUS-100 AUV in the Arctic. In Proceedings of the 2010 IEEE/OES Autonomous Underwater Vehicles, Monterey, CA, USA, 1–3 September 2010; pp. 1–8. [Google Scholar] [CrossRef]
- Norgren, P.; Lubbad, R.; Skjetne, R. Unmanned underwater vehicles in Arctic operation. In Proceedings of the 22nd IAHR International Symposium on Ice, Singapore, 11–15 August 2014; pp. 89–101. [Google Scholar]
- King, P.; Lewis, R.; Mouland, D.; Walker, D. CATCHY an AUV ice dock. In Proceedings of the MTS/IEEE OCEANS 2009, Biloxi, MS, USA, 26–29 October 2009; pp. 1–6. [Google Scholar]
- Lewis, R.; Bose, N.; Lewis, S.; King, P.; Walker, D.; Devillers, R.; Ridgley, N.; Husain, T.; Munroe, J.; Vardy, A. Merlin—A decade of large AUV experience at Memorial University of Newfoundland. In Proceedings of the IEEE/OES Autonomous Underwater Vehicles, Tokyo, Japan, 6–9 November 2016; pp. 222–229. [Google Scholar] [CrossRef]
- Bellingham, J.G.; Cokelet, E.D.; Kirkwood, W.J. Observation of warm water transport and mixing in the Arctic basin with the ALTEX AUV. In Proceedings of the 2008 IEEE/OES Autonomous Underwater Vehicles, Woods Hole, MA, USA, 13–14 October 2008; pp. 1–5. [Google Scholar]
- Spain, E.; Gwyther, D.; King, P. Submarine ventures under Sorsdal glacier. Aust. Antarct. Mag. 2019, 36, 18–19. [Google Scholar]
- Loh, T.Y.; Brito, M.P.; Bose, N.; Xu, J.; Tenekedjiev, K. Human error in autonomous underwater vehicle deployment: A system dynamics approach. Risk Anal. 2020, 40, 1258–1278. [Google Scholar] [CrossRef]
- Chen, X.; Neil, B.; Mario, B.; Faisal, K.; Bo, T.; Zou, T. A review of risk analysis research for the operations of autonomous underwater vehicles. Reliab. Eng. Syst. Saf. 2021, 216, 108011. [Google Scholar] [CrossRef]
- Rausand, M.; Haugen, M. Risk Assessment: Theory, Methods, and Applications, 2nd ed.; Wiley: Hoboken, NJ, USA, 2020. [Google Scholar]
- Griffiths, G.; Collins, K.J. Towards a risk management process for autonomous underwater vehicles. In Proceedings of the Masterclass in AUV Technology for Polar Science: Collaborative Autosub Science in Extreme Environments, National Oceanography Centre, Southampton, London, UK, 28–30 March 2006; pp. 103–118. [Google Scholar]
- Griffiths, G.; Brito, M. Predicting risk in missions under sea ice with autonomous underwater vehicles. In Proceedings of the 2008 IEEE/OES Autonomous Underwater Vehicles, Woods Hole, MA, USA, 13–14 October 2008; pp. 1–7. [Google Scholar] [CrossRef]
- Brito, M.P.; Griffiths, G.; Challenor, P. Risk analysis for autonomous underwater vehicle operations in extreme environments. Risk Anal. 2010, 30, 1771–1788. [Google Scholar] [CrossRef]
- Brito, M.P.; Griffiths, G. A Markov chain state transition approach to establishing critical phases for AUV reliability. IEEE J. Ocean. Eng. 2011, 36, 139–149. [Google Scholar] [CrossRef]
- Brito, M.P.; Griffiths, G. A Bayesian approach for predicting risk of autonomous underwater vehicle loss during their missions. Reliab. Eng. Syst. Saf. 2016, 146, 55–67. [Google Scholar] [CrossRef]
- Loh, T.Y.; Brito, M.P.; Bose, N.; Xu, J.; Tenekedjiev, K. Fuzzy system dynamics risk analysis (FuSDRA) of autonomous underwater vehicle operations in the Antarctic. Risk Anal. 2020, 40, 818–841. [Google Scholar] [CrossRef] [PubMed]
- Bremnes, J.E.; Thieme, C.A.; Sørensen, A.J.; Utne, I.B.; Norgren, P. A Bayesian approach to supervisory risk control of AUVs applied to under-ice operations. Mar. Technol. Soc. J. 2020, 54, 16–39. [Google Scholar] [CrossRef]
- Utne, I.B.; Rokseth, B.; Sørensen, A.J.; Vinnem, J.E. Towards supervisory risk control of autonomous ships. Reliab. Eng. Syst. Safe. 2020, 196, 1–15. [Google Scholar] [CrossRef]
- ISO 31000; Risk Management: Principles and Guidelines. ISO: Geneva, Switzerland, 2018. Available online: https://www.iso.org/obp/ui/#iso:std:iso:31000:ed-1:v1:en (accessed on 20 February 2024).
- Bremnes, J.E.; Norgren, P.; Sørensen, A.J.; Thieme, C.A.; Utne, I.B. Intelligent risk-based under-ice altitude control for autonomous underwater vehicles. In Proceedings of the MTS/IEEE OCEANS, Seattle, WA, USA, 27–31 October 2019; pp. 1–8. [Google Scholar] [CrossRef]
- Yang, R.C.; Utne, I.B. Towards an online risk model for autonomous marine systems (AMS). Ocean Eng. 2022, 251, 111100. [Google Scholar] [CrossRef]
- Yang, R.C.; Bremnes, J.E.; Utne, I.B. Online risk modeling of autonomous marine systems: Case study of autonomous operations under sea ice. Ocean Eng. 2023, 281, 114765. [Google Scholar] [CrossRef]
- Dodd, P.A.; Price, M.R.; Heywood, K.J.; Miles, P. Collection of water samples from an autonomous underwater vehicle for tracer analysis. J. Atmos. Ocean. Technol. 2006, 23, 1759–1767. [Google Scholar] [CrossRef]
- Hwang, J.; Bose, N.; Millar, G.; Bulger, C.; Nazareth, G.; Chen, X. Adaptive AUV mission control system tested in the waters of Baffin Bay. Drones 2024, 8, 45. [Google Scholar] [CrossRef]
- Wynn, R.B.; Huvenne, V.A.I.; Bas, T.P.L.; Murton, B.J.; Connelly, D.P.; Bett, B.J.; Ruhl, H.A.; Morris, K.J.; Peakall, J.; Parsons, D.R.; et al. Autonomous Underwater Vehicles (AUVs): Their past, present and future contributions to the advancement of marine geoscience. Mar. Geol. 2014, 352, 451–468. [Google Scholar] [CrossRef]
- Graham, A.G.C.; Dutrieux, P.; Vaughan, D.G.; Nitsche, F.O.; Gyllencreutz, R.; Greenwood, S.L.; Larter, R.D.; Jenkins, A. Seabed corrugations beneath an Antarctic ice shelf revealed by Autonomous Underwater Vehicle survey: Origin and implications for the history of Pine Island Glacier. J. Geophys. Res. 2013, 118, 1356–1366. [Google Scholar] [CrossRef]
- Dowdeswell, J.A.; Evans, J.; Cofaigh, C.Ó. Submarine landforms and shallow acoustic stratigraphy of a 400 km-long fjord–shelf–slope transect, Kangerlussuaq margin, East Greenland. Quat. Sci. Rev. 2010, 29, 3359–3369. [Google Scholar] [CrossRef]
- Trevithick, J. The navy is building a network of drone submarines and sensor buoys in the Arctic. The War Zone, 2020. Available online: https://www.thedrive.com/the-war-zone/36821/the-navy-is-building-a-network-of-drone-submarines-and-sensor-buoys-in-the-arctic (accessed on 20 February 2024).
- Govindarajan, A.F.; McCartin, L.; Adams, A.; Allan, E.; Belani, A.; Francolini, R.; Fujii, J.; Gomez-Ibañez, D.; Kukulya, A.; Marin, F.; et al. Improved biodiversity detection using a large-volume environmental DNA sampler with in situ filtration and implications for marine eDNA sampling strategies. Deep-Sea Res. Part I Oceanogr. Res. Pap. 2022, 189, 103871. [Google Scholar] [CrossRef]
- Li, X.; Yu, S. Comparison of biological swarm intelligence algorithms for AUVs for three-dimensional path planning in ocean currents’ conditions. J. Mar. Sci. Technol. 2023, 28, 832–843. [Google Scholar] [CrossRef]
- Batchelor, C.L.; Montelli, A.; Ottesen, D.; Evans, J.; Dowdeswell, E.K.; Christie, F.D.W.; Dowdeswell, J.A. New insights into the formation of submarine glacial landforms from high-resolution Autonomous Underwater Vehicle data. Geomorphology 2020, 370, 107396. [Google Scholar] [CrossRef]
- Okada, K. Breakthrough technologies for mineral exploration. Miner. Econ. 2022, 35, 429–454. [Google Scholar] [CrossRef]
- Tran, T.C.; Ban, N.C.; Bhattacharyya, J. A review of successes, challenges, and lessons from Indigenous protected and conserved areas. Biol. Conserv. 2020, 241, 108271. [Google Scholar] [CrossRef]
Year | Arctic | Antarctic | Total |
---|---|---|---|
1972 | 1 | 0 | 1 |
1992 | 1 | 0 | 1 |
1993 | 1 | 0 | 1 |
1994 | 2 | 1 | 3 |
1995 | 2 | 0 | 2 |
1996 | 2 | 0 | 2 |
1998 | 1 | 0 | 1 |
2001 | 1 | 1 | 2 |
2002 | 1 | 0 | 1 |
2003 | 0 | 1 | 1 |
2004 | 2 | 0 | 2 |
2005 | 0 | 2 | 2 |
2007 | 2 | 0 | 2 |
2008 | 3 | 1 | 4 |
2009 | 1 | 2 | 3 |
2010 | 6 | 2 | 8 |
2011 | 3 | 0 | 3 |
2012 | 2 | 2 | 4 |
2013 | 3 | 0 | 3 |
2014 | 1 | 3 | 4 |
2015 | 0 | 1 | 1 |
2016 | 5 | 0 | 5 |
2017 | 1 | 2 | 3 |
2018 | 1 | 1 | 2 |
2019 | 1 | 3 | 4 |
2020 | 0 | 2 | 2 |
2021 | 2 | 0 | 2 |
2022 | 2 | 1 | 3 |
2023 | 1 | 1 | 2 |
2024 | 0 | 1 | 1 |
In total | 75 |
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Fan, S.; Bose, N.; Liang, Z. Polar AUV Challenges and Applications: A Review. Drones 2024, 8, 413. https://doi.org/10.3390/drones8080413
Fan S, Bose N, Liang Z. Polar AUV Challenges and Applications: A Review. Drones. 2024; 8(8):413. https://doi.org/10.3390/drones8080413
Chicago/Turabian StyleFan, Shuangshuang, Neil Bose, and Zeming Liang. 2024. "Polar AUV Challenges and Applications: A Review" Drones 8, no. 8: 413. https://doi.org/10.3390/drones8080413
APA StyleFan, S., Bose, N., & Liang, Z. (2024). Polar AUV Challenges and Applications: A Review. Drones, 8(8), 413. https://doi.org/10.3390/drones8080413