We surveyed Antarctic krill (Euphausia superba) under sea ice using the autonomous underwater vehicle Autosub-2. Krill were concentrated within a band under ice between 1 and 13 kilometers south of the ice edge. Within this band, krill densities were fivefold greater than that of open water. The under-ice environment has long been considered an important habitat for krill, but sampling difficulties have previously prevented direct observations under ice over the scale necessary for robust krill density estimation. Autosub-2 enabled us to make continuous high-resolution measurements of krill density under ice reaching 27 kilometers beyond the ice edge.

The cavities beneath Antarctic ice shelves are among the least studied regions of the World Ocean, yet they are sites of globally important water mass transformations. Here we report results from a mission beneath Fimbul Ice Shelf of an autonomous underwater vehicle. The data reveal a spatially complex oceanographic environment, an ice base with widely varying roughness, and a cavity periodically exposed to water with a temperature significantly above the surface freezing point. The results of this, the briefest of glimpses of conditions in this extraordinary environment, are already reforming our view of the topographic and oceanographic conditions beneath ice shelves, holding out great promises for future missions from similar platforms.

The underwater glider is set to become an important platform for oceanographers to gather data within oceans. Gliders are usually equipped with a conductivity-temperature-depth (CTD) sensor, but a wide range of other sensors have been fitted to gliders.In the present work, the authors aim at measuring the vertical water velocity. The vertical water velocity is obtained by subtracting the vertical glider velocity relative to the water from the vertical glider velocity relative to the water surface. The latter is obtained from the pressure sensor. For the former, a quasi-static model of planar glider flight is developed. The model requires three calibration parameters, the (parasite) drag coefficient, glider volume (at atmospheric pressure), and hull compressibility, which are found by minimizing a cost function based on the variance of the calculated vertical water velocity.Vertical water velocities have been calculated from data gathered in the northwestern Mediterranean during the Gulf of Lions experiment, winter 2008. Although no direct comparison could be made with water velocities from an independent measurement technique, the authors show that, for two different heat loss regimes ('0 and '400 W m 22 ), the calculated vertical velocity scales are comparable with those expected for internal waves and active open ocean convection, respectively. High noise levels resulting from the pressure sensor require the water velocity time series to be low-pass filtered with a cutoff period of 80 s. The absolute accuracy of the vertical water velocity is estimated at 64 mm s 21.

The aim of this community white paper is to make recommendations for a glider component of a global ocean observing system. We first recommend the adoption of an Argo-like data system for gliders. Then, we argue that combining glider deployments with the other components (ships, moorings, floats and satellites) will considerably enhance our capacity for observing the ocean by filling gaps left by the other observing systems. Gliders could be deployed to sample most of the western and eastern boundary circulations and the regional seas (around 20 basins in the world) which are not well covered by the present global ocean observing system and in the vicinity of fixed point time series stations. These plans already involve people scattered around the world in Australia, Canada, Cyprus, France, Germany, Italy, Norway, Spain, UK, and the USA, and will certainly expand to many other countries. A rough estimate of resources required is about 13M$/Euro for ~20+ gliders permanently at sea during five years in the world ocean, based on present scientific infrastructures.

Autonomous Underwater Vehicles (AUVs) are effective platforms for science research and 9 monitoring, and for military and commercial data-gathering purposes. However, there is an inevitable risk of 10 loss during any mission. Quantifying the risk of loss is complex, due to the combination of vehicle reliability 11 and environmental factors, and cannot be determined through analytical means alone. An alternative 12 approach -formal expert judgment -is a time-consuming process; consequently a method is needed to 13 broaden the applicability of judgments beyond the narrow confines of an elicitation for a defined 14 environment. We propose and explore a solution founded on a Bayesian Belief Network (BBN), where the 15 results of the expert judgment elicitation are taken as the initial prior probability of loss due to failure. The 16 network topology captures the causal effects of the environment separately on the vehicle and on the 17 support platform, and combines these to produce an updated probability of loss due to failure. An extended 18 version of the Kaplan Meier estimator is then used to update the mission risk profile with travelled distance. 19Sensitivity analysis of the BBN is presented and a case study of Autosub3 AUV deployment in the Amundsen 20Sea is discussed in detail. 22Keywords: Bayesian networks, survival statistics, expert judgment elicitation, autonomous vehicles. Autonomous Underwater Vehicles (AUVs) have a future as effective platforms for science research and 2 monitoring, and for military and commercial data-gathering purposes. Increasingly they are being used in 3 environments that are not benign [1,2]. Environments such as under sea ice [3], under shelf ice [4], or along 4 rocky coasts [5] intuitively give rise to a higher risk of loss should the vehicle malfunction. The risk of loss is 5 real; for example, Australian and British AUVs have been lost under ice sheets [6] and one team maintained a 6 lightweight tether to an AUV when operating under sea ice. The problem of predicting risk of loss is not only 7 one of predicting the reliability of the vehicle as a whole, its sub-systems and its components, but also of how 8 the operating environment, together with reliability, sets the probability of losing the vehicle. It is not obvious 9 that an approach based on separate statistical analyses of vehicle reliability and the affects of the 10 environment on probability of loss is either feasible or meaningful. Such an approach, when reduced to 11 summary statistics such as mean time to failure, would ignore the interaction between individual faults or 12 incidents and the environment, which we postulate to be at the centre of this problem. 13One alternative would be to assess the probability of loss in various environments directly, by counting the 14 frequency of occurrence. This frequentist approach is certainly appropriate for assessing the reliability of 15 identical engineered systems, where probability of failure is derived from a long-run frequency of occurrence, 16usually fr...

Autonomous underwater vehicles (AUVs) are used increasingly to explore hazardous marine environments. Risk assessment for such complex systems is based on subjective judgment and expert knowledge as much as on hard statistics. Here we describe the use of a risk management process tailored to AUV operations, the implementation of which requires the elicitation of expert judgment. We conducted a formal judgment elicitation process where eight world experts in AUV design and operation were asked to assign a probability of AUV loss given the emergence of each fault or incident from the vehicle's life history of 63 faults and incidents.After discussing methods of aggregation and analysis, we show how the aggregated risk estimates obtained from the expert judgments were used to create a risk model. To estimate AUV survival with mission distance, we adopted a statistical survival function based on the nonparametric Kaplan-Meier estimator. We present theoretical formulations for the estimator, its variance and confidence limits. We also present a numerical example where the approach is applied to estimate the probability that the Autosub3 AUV would survive a set of missions under

Limitations of access have long restricted exploration and investigation of the cavities beneath ice shelves to a small number of drillholes. Studies of sea-ice underwater morphology are limited largely to scientific utilization of submarines. Remotely operated vehicles, tethered to a mother ship by umbilical cable, have been deployed to investigate tidewater-glacier and ice-shelf margins, but their range is often restricted. The development of free-flying autonomous underwater vehicles (AUVs) with ranges of tens to hundreds of kilometres enables extensive missions to take place beneath sea ice and floating ice shelves. Autosub2 is a 3600 kg, 6.7 m long AUV, with a 1600 m operating depth and range of 400 km, based on the earlier Autosub1 which had a 500 m depth limit. A single direct-drive d.c. motor and five-bladed propeller produce speeds of 1–2 m s−1. Rear-mounted rudder and stern-plane control yaw, pitch and depth. The vehicle has three sections. The front and rear sections are free-flooding, built around aluminium extrusion space-frames covered with glass-fibre reinforced plastic panels. The central section has a set of carbon-fibre reinforced plastic pressure vessels. Four tubes contain batteries powering the vehicle. The other three house vehicle-control systems and sensors. The rear section houses subsystems for navigation, control actuation and propulsion and scientific sensors (e.g. digital camera, upward-looking 300 kHz acoustic Doppler current profiler, 200 kHz multibeam receiver). The front section contains forward-looking collision sensor, emergency abort, the homing systems, Argos satellite data and location transmitters and flashing lights for relocation as well as science sensors (e.g. twin conductivity–temperature–depth instruments, multibeam transmitter, sub-bottom profiler, AquaLab water sampler). Payload restrictions mean that a subset of scientific instruments is actually in place on any given dive. The scientific instruments carried on Autosub are described and examples of observational data collected from each sensor in Arctic or Antarctic waters are given (e.g. of roughness at the underside of floating ice shelves and sea ice).

The range of physiological adaptations possessed by marine animals allowing them to successfully operate in the marine environment is a plentiful source of inspiration for the designers of Autonomous Underwater Vehicles. This chapter compares the total energetic cost of straight line swimming for both marine animals and AUVs, using cost of transport (COT) as a comparative metric. COT is a normalised measure of the energetic cost of transporting the animal's or vehicle's mass over a unit distance. It includes non propulsion power requirements as well as considering the energy lost by actuators and mechanical couplings and energy lost in the wake. Comparisons presented in this chapter show that marine animals typically have higher optimum COT than engineered systems of equivalent size. However parallels may be drawn, for example, to increase range both marine animals and AUVs appear to favour reducing non-propulsion power costs and travelling slowly to ensure operating at the minimum COT.