UNCLOS under ice survey - An historic AUV deployment in the Canadian high arctic

Crees, Tristan; Kaminski, Chris; Ferguson, J.; Laframboise, Jean Marc; Forrest, Alexander L.; Williams, Jacob; MacNeil, Erin; Hopkin, David; Pederson, R.

doi:10.1109/oceans.2010.5664438

Cited by 27 publications

(15 citation statements)

References 5 publications

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“…The results herein provide a solution in the vertical degree of freedom and serve as a foundation for future work in horizontal ADCP-aided navigation (Medagoda et al (2015)). This method could also enable new polar AUV missions where the presence of ice precludes obtaining GPS measurements and there are increased demands on navigation and long duration autonomy (Crees et al (2010); Nicholls et al (2006)). These future applications pose new research challenges.…”

Section: Resultsmentioning

confidence: 99%

Mid-water current aided localization for autonomous underwater vehicles

et al. 2016

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Survey-class Autonomous Underwater Vehicles (AUVs) typically rely on Doppler Velocity Logs (DVL) for precision localization near the seafloor. In cases where the seafloor depth is greater than the DVL bottom-lock range, localizing between the surface and the seafloor presents a localization problem since both GPS and DVL observations are unavailable in the midwater column. This work proposes a solution to this problem that exploits the fact that current profile layers of the water column are near constant over short time scales (in the scale of minutes). Using observations of these currents obtained with the Acoustic Doppler Current Profiler (ADCP) mode of the DVL during descent, along with data from other sensors, the method discussed herein constrains position error. The method is validated using field data from the Sirius AUV coupled with view-based Simultaneous Localization and Mapping (SLAM) and on descents up to 3km deep with the Sentry AUV.

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Section: Resultsmentioning

confidence: 99%

Mid-water current aided localization for autonomous underwater vehicles

et al. 2016

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“…It is not absolutely the case that only small vehicles can be launched from ice holes, e.g., a large Explorer-class AUV was launched from an ice hole for an UNCLOS seabed mapping mission in 2010 (Crees et al 2010), but economics and practical considerations are strongly supportive of using a hole for a local mission but, where possible, a ship for a long-range mission.…”

Section: Discussionmentioning

confidence: 99%

The use of autonomous underwater vehicles to map the variability of under-ice topography

Wadhams

2012

Ocean Dynamics

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We review the development of autonomous underwater vehicle (AUV) use under sea ice to map the threedimensional (3-D) structure of the ice underside. The author, after extensive experience in under-ice profiling from submarines using single-beam sonar, carried out the first underice sidescan sonar profiling from an AUV in 2002 in the Greenland Sea. This was followed in August 2004 by the first full multibeam sonar experiment, using Kongsberg EM2000 sonar aboard the Autosub-II vehicle off NE Greenland. Two experiments using a small Gavia vehicle deployed through holes in the ice followed in 2007 and 2008, in the Beaufort Sea and off Ellesmere Island. Examples of the 3-D imagery are shown, and the two approaches of using a large vehicle deployed from a ship and a small through-ice vehicle are compared and found to be complementary. The imagery has shown that although first-year (FY) ridges have the familiar shape of a triangular prism made of small ice blocks, multi-year (MY) ridges are found to be broken up by lead formation into a chain of individual large ice blocks rather than a coherent linear feature. New work and future plans are described.

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“…Two Explorer class AUVs were delivered to NRCan for the 2010 Arctic campaign, both of which had been built by International Submarine Engineering (ISE; Crees et al 2010). Only vehicle 5's data are used in this paper, vehicle 6 had too few runs for the statistics to be meaningful.…”

Section: Motivating Example: Explorer Auv Fault Historymentioning

confidence: 99%

“…The motivation came from the decision by the Canadian Government, acting through Natural Resources Canada (NRCan), to use AUVs as part of its data-gathering program in the Arctic in support of its United Nations Convention on the Law of the Sea (UNCLOS) submission (Crees et al 2010). Article 76 allows countries to extend their jurisdiction beyond 200 nmi from the baselines if it is demonstrated that the seabed and subsoil of the submarine areas are ''a natural prolongation of its land territory to the outer edge of the continental margin'' (United Nations Oceans and Law of the Sea 2011).…”

Section: Introductionmentioning

confidence: 99%

A Behavioral Probabilistic Risk Assessment Framework for Managing Autonomous Underwater Vehicle Deployments

Brito

Griffiths

Ferguson

et al. 2012

Journal of Atmospheric and Oceanic Technology

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. 2012 A Behavioral probabilistic risk assessment framework for managing autonomous underwater vehicle deployments. Journal of Atmospheric and Oceanic Technology, 29 (11). 1689-1703. 10 ABSTRACTThe deployment of a deep-diving long-range autonomous underwater vehicle (AUV) is a complex operation that requires the use of a risk-informed decision-making process. Operational risk assessment is heavily dependent on expert subjective judgment. Expert judgments can be elicited either mathematically or behaviorally. During mathematical elicitation experts are kept separate and provide their assessment individually. These are then mathematically combined to create a judgment that represents the group view. The limitation with this approach is that experts do not have the opportunity to discuss different views and thus remove bias from their assessment. In this paper, a Bayesian behavioral approach to estimate and manage AUV operational risk is proposed. At an initial workshop, behavioral aggregation, that is, reaching agreement on the distributions of risks for faults or incidents, is followed by an agreed upon initial estimate of the likelihood of success of the proposed risk mitigation methods. Postexpedition, a second workshop assesses the new data and compares observed to predicted risk, thus updating the prior estimate using Bayes' rule. This feedback further educates the experts and assesses the actual effectiveness of the mitigation measures. Applying this approach to an AUV campaign in ice-covered waters in the Arctic showed that the maximum error between the predicted and the actual risk was 9% and that the experts' assessments of the effectiveness of risk mitigation led to a maximum of 24% in risk reduction.

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UNCLOS under ice survey - An historic AUV deployment in the Canadian high arctic

Cited by 27 publications

References 5 publications

Mid-water current aided localization for autonomous underwater vehicles

Mid-water current aided localization for autonomous underwater vehicles

The use of autonomous underwater vehicles to map the variability of under-ice topography

A Behavioral Probabilistic Risk Assessment Framework for Managing Autonomous Underwater Vehicle Deployments

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