Autonomous Underwater Vehicles for Ocean Research: Current Needs and State of the Art Technologies

Abstract

Research in the field of scientific Autonomous Underwater Vehicles (AUVs) has been underway for over three decades. Much of the original work was funded by the military, and the vehicles were intended for Naval Ordnance Systems Command oceanographic needs, although the payloads were for general scientific use. Over the last decade, several autonomous underwater vehicles have been purpose built for both the science community and industrial operations around the world. Because of the variety of mission requirements, AUV configurations and options available today are numerous.Beginning with a brief overview of the history of AUVs for science, the paper continues with discussion of the ocean applications that are suitable to AUVs. Highlighted are some points a potential user might consider when selecting an AUV for a particular science application. An overview of several AUVs available today has been included exploring the cross-section of capabilities offered. This leads into the emerging requirements for AUVs of the future. In conclusion, some insight is given on lessons learned by several members of the oceanographic AUV community and how these lessons might be applicable when considering AUVs as a solution to today's intriguing oceanographic science questions.

An Observing System Simulation Experiment for the Indian Ocean

“…However, notable exceptions are the upwelling regions off the coast of Somalia and Java/Sumatra; both of these regions are of significant climate interest (e.g., Saji et al 1999;Webster et al 1999;Vecchi and Harrison 2004;Vecchi et al 2004). Because of their proximity to land and the active fisheries present in those regions, expected losses to vandalism and snares with fishing equipment make mooring deployment in these regions unfeasible; the utility of developing observational technologies, such as autonomous gliders (e.g., Eriksen et al 2001;Sibenac et al 2004), to sample directly in these regions should be explored. However, the subsurface subseasonal variability along the coast of Java/Sumatra (e.g., 5°S, 100°E) exhibits strong coherence with variability along the equatorial waveguide, at the location of the proposed moorings (Fig.…”

Quantitative mapping and predictive modelling of Mn-nodules' distribution from hydroacoustic and optical AUV data linked by Random Forests machine learning

“…The data (Greinert, 2016) and dead reckoning (GEOMAR, 2016). Each system has its own limitations that contribute to the total navigation error (Sibenac et al, 2004;Chen et al, 2013) that generally results in positioning drifts over time. Consequently, this affects the position accuracy of the MBES and photo data.…”