Deep-diving autonomous underwater vehicle provides insights into scattering layer dynamics

Kelly J. Benoit-Bird

doi:10.1121/1.4876983

Deep diving with Clio

Science Robotics ◽

10.1126/scirobotics.abf1499 ◽

2020 ◽

Vol 5 (48) ◽

pp. eabf1499

Author(s):

Jnaneshwar Das ◽

Elizabeth Trembath-Reichert

Keyword(s):

Autonomous Underwater Vehicle ◽

Underwater Vehicle ◽

Ocean Basin ◽

Deep Diving ◽

Basin Scale

An autonomous underwater vehicle, named Clio, can sample ocean basin–scale biogeochemistry at depths up to 6000 meters.

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

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-12-00005.1 ◽

2012 ◽

Vol 29 (11) ◽

pp. 1689-1703 ◽

Cited By ~ 21

Author(s):

Mario Brito ◽

Gwyn Griffiths ◽

James Ferguson ◽

David Hopkin ◽

Richard Mills ◽

...

Keyword(s):

Risk Assessment ◽

Autonomous Underwater Vehicle ◽

Risk Mitigation ◽

Operational Risk ◽

Maximum Error ◽

Underwater Vehicle ◽

Behavioral Approach ◽

Mitigation Measures ◽

The Arctic ◽

Deep Diving

Abstract The 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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AUV DeepC: Technology Platform for the Atlas Elektronik AUV Family

23rd International Conference on Offshore Mechanics and Arctic Engineering, Volume 3 ◽

10.1115/omae2004-51323 ◽

2004 ◽

Author(s):

Willi Hornfeld

Keyword(s):

Modular Design ◽

Autonomous Underwater Vehicle ◽

Underwater Vehicle ◽

Maximum Speed ◽

Operational Experience ◽

Unmanned Underwater Vehicles ◽

Technological Basis ◽

Deep Diving ◽

Vehicle System ◽

High Efficient

ATLAS ELEKTRONIK has a long tradition in the development and manufacture of naval systems and marine technologies like mine-countermeasure boats, integrated sonar, command and control systems and unmanned underwater vehicles (UUVs). One of the latest UUV-product development is the autonomous underwater vehicle, called DeepC with the main features: • Mission depth till 4000 m (Crush: 6000 m); • Weight in air 2.4 to ; • Cruise speed 4 kts; • Maximum speed 6 kts; • Mission time up to 60 hours; • Operating range up to 400 km; • Payloads 250 kg. The development was started on Jan. 1st 2001 and will be finalized end of 2004 with the deep water tests. DeepC (www.deepc-auv.de) is a high efficient deep diving autonomous underwater vehicle system with long endurance for a lot of applications. Many actions that would normally require high cost ROV systems or which are not possible (e.g. under ice investigations) can now be accomplished using DeepC, at less cost and manpower. The main performance parameters of the DeepC system are in general: • Autonomous mission execution; • Low weight; • High manoeuvrability; • Hover capability; • Modular design; • Containerised Payload; • Deployment from vessels of opportunity; • Obstacle avoidance; • In mission re-planning capability; • Precise navigation; • Advanced diagnosis and fault recovery system. With these performance takes the DeepC system an international top place on the AUV sector and has in the framework of the ATLAS ELEKTRONIK AUV family philosophy a key position: it is the technological basis and highlight of the AUV family, contains the equipment with the most fastidious technologies and is now in the final phase of the development. The second member of the AUV family is the SeaOtter Mk 1, a vehicle system based on an ATLAS MARIDAN development. This AUV is extensively tested, qualified and successfully introduced at the market in its basic version. With the help of the DeepC, technology upgrades are planned which will lead to a substantial performance increase. The AUV of the third generation represents the SeaOtter Mk 2, whose development was up-to-date started and which bundles both, the DeepC technology and the operational experience of the SeaOtter Mk 1.

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