Predicting risk in missions under sea ice with Autonomous Underwater Vehicles

Abstract

Autonomous Underwater Vehicles (AUVs) have a
future as effective platforms for multi-disciplinary science
research and monitoring in the polar oceans. However, operation
under ice may involve significant risk to the vehicle. A risk
assessment and management process that balances the risk
appetite of the responsible owner with the reliability of the
vehicle and the probability of loss has been proposed. A critical
step in the process of assessing risk is based on expert judgment
of the fault history of the vehicle, and what affect faults or
incidents have on the probability of loss. However, this subjective
expert judgment is sensitive to the nature of sea ice cover. In
contrast to the simple, yet high risk, case of operation under an
ice shelf, sea ice offers a complex risk environment. Furthermore,
the risk is modified by the characteristics of the support vessel,
especially its ice-breaking capability. We explore how the
ASPeCt sea ice characterization protocol and probability
distributions of ice thickness and concentration can be used
within a rigorous process to quantify risk given a range of sea ice
conditions and with ships of differing ice capabilities. A solution
founded on a Bayesian Belief Network approach is proposed,
where the results of the expert judgment elicitation is taken as a
reference. The design of the network topology captures the causal
effects of the environment separately on the vehicle and on the
ship, and combines these to produce the output. Complementary
expert knowledge is included within the conditional probability
tables of the Bayesian Belief Network. Using expert judgment on
the fault history of the Autosub3 vehicle and sea ice data
gathered in the Arctic and Antarctic by its predecessor,
Autosub2, examples are provided of how risk is modified by the
sea ice environment.

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Identifiers

ORCID for Mario Brito: orcid.org/0000-0002-1779-4535

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