Gwyn Griffiths

Technology and applications of autonomous underwater vehicles

Autonomous underwater vehicles - robot submarines - are revolutionizing the way in which researchers and industry obtain data. Advances in technology have resulted in capable vehicles that have made new discoveries on how the oceans work and have dramatically reduced the cost to industry of surveying the seabed. Technology and Applications of Autonomous Underwater Vehicles describes the technologies that have made this happen and a host of applications from fish abundance studies to finding diamonds beneath the sea. This book will help the next generation of engineers and scientists in academia, industry, and government laboratories to continue in research, defense, and industry.

Measurements beneath an Antarctic ice shelf using an autonomous underwater vehicle

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.

Current measurements from autonomous underwater gliders

We consider the potential for making current measurements from gliders, and present data from a deployment in early 2007 of 1000 m Slocum electric gliders in the North West Mediterranean Sea. Three types of current measurement are considered. First, by comparing the difference between successive GPS positions, obtained when the glider surfaces, and dead-reckoned displacements when the glider is submerged, it is possible to estimate depth averaged horizontal currents and also surface drift. Second, our gliders were equipped with Conductivity Temperature Depth sensors, which provided data used to calculate geostrophic horizontal velocity. Third, from the measured rate of change of pressure it is possible to quantify the vertical water velocity as the difference between the measurement and the expected vertical motion. The latter two both require a model of the glider motion, which we outline. Horizontal currents of the order of 30 cm/s were measured in the westward flowing Northern Current off the south coast of France, with a width and transport comparable with previous observations using different technologies. The accuracy of the depth-averaged currents in magnitude and direction was limited by the accuracy of the measured heading of the glider. Measurements of vertical velocity were made during a time of active convection when the magnitude of the vertical motion was up to 10 cm/s. We estimate that the accuracy of the calculated velocity was of the order of 1 cm/s.

Vertical Water Velocities from Underwater Gliders

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 6 4m m s 21 .

Using 3DF GPS Heading for Improving Underway ADCP Data

Abstract Systematic error in the cross-track velocity measured under way from shipboard acoustic Doppler current profilers (ADCPs) can be attributed to error in measuring the ships heading with a gyrocompass. Drift- and direction-dependent errors in marine gyrocompasses may amount to 2°–3°, yet they can be difficult to observe. A new system for obtaining attitude information using differential carrier phase measurements on signals from Global Positioning System (GPS) navigation satellites can provide a heading accuracy of 0.05°. This paper proposes a method of using these GPS heading measurements as a reference, with the gyrocompass as an interpolation device, to reduce the cross-track velocity error from a shipboard ADCP. The practical application of the method is illustrated by a long north-south section dominated by latitude-induced gyrocompass error, and a small-scale survey where heading-dependent errors in the gyrocompass dominated.

Autonomous underwater vehicles (AUVs) and investigations of the ice-ocean interface in Antarctic and Arctic waters

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 500m depth limit. A single direct-drive d.c. motor and five-bladed propeller produce speeds of 1-2 ms−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).

On the Reliability of the Autosub Autonomous Underwater Vehicle

As autonomous underwater vehicles (AUVs) enter operational service an assessment of their reliability is timely. Using the Autosub AUV as an example, several design issues affecting reliability are discussed, followed by an analysis of recorded faults. Perhaps contrary to expectations, failures rarely involved the autonomous nature of the vehicle. Rather, faults were typical of those that occur with any complex item of marine electromechanical equipment. A statistical analysis showed that the failure rate decreased with distance travelled an indicator that an AUV underway, submerged, is at less risk of a fault developing than during other phases of a mission.

Risk analysis for autonomous underwater vehicle operations in extreme environments.

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 vehicles 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 Pine Island Glacier, Antarctica in January-March 2009.

A Markov Chain State Transition Approach to Establishing Critical Phases for AUV Reliability

The deployment of complex autonomous underwater platforms for marine science comprises sequential steps each of which is critical to mission success. Here we present a state transition approach, in the form of a Markov chain, which models step sequence from prelaunch to operation to recovery. The aim is to identify states and state transitions presenting high risk to the vehicle and hence to the mission, based on evidence and judgment. Developing a Markov chain consists of two separate tasks. The first defines the structure that encodes event sequence. The second assigns probabilities to each possible transition. Our model comprises 11 discrete states, and includes distance-dependent underway survival statistics. Integration of the Markov model with underway survival statistics allows us to quantify success likelihood during each state and state transition, and consequently the likelihood of achieving desired mission goals. To illustrate this generic process, the fault history of the Autosub3 autonomous underwater vehicle (AUV) provides the information for different operation phases. In our proposed method, faults are discriminated according to the mission phase in which they took place.

Towards environmental monitoring with the Autosub autonomous underwater vehicle

In this paper we describe some of the desirable characteristics of an autonomous underwater vehicle capable of undertaking environmental surveys in the ocean. Several of these characteristics are incorporated in the 7 m long Autosub-1 vehicle which has completed over 120 missions to date in UK and US waters. We review some of the key technological innovations used within Autosub-1 and describe some results from a 110 km survey off the coast of Florida in December 1997. While the survey demonstrated many of the advantages of using an AUV for environmental monitoring the paper concludes with a discussion of technical and procedural areas that still require attention before the use of AUVs can be considered routine.