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Many pelagic animals, such as krill, lanternfish, and cephalopods, migrate to deep water at dawn to avoid visual predators during daylight hours and move up toward the sea surface at dusk to search for food. This behavior is termed “diel vertical migration.” Migrating animals graze on phytoplankton or zooplankton and in turn serve as food for higher trophic levels, hence providing a key mechanism for carbon export via this migration. These animals are often observed as sound-scattering layers by echosounders, but the animals causing the acoustic scattering are difficult to identify using acoustics alone. In a spring 2019 experiment in Monterey Bay, we deployed autonomous underwater and surface vehicles over a seabed-mounted upward-looking echosounder to collect environmental DNA (eDNA) with the goal of identifying the vertically migrating animals. The echosounder was installed at 890-m depth on the Monterey Accelerated Research System (MARS) seabed cabled ocean observatory, providing real-time data of acoustic backscatter from the full water column. One long-range autonomous underwater vehicle (LRAUV) carrying a Third-Generation Environmental Sample Processor (3G-ESP) acquired water samples from a sequence of layers from near surface down to $\sim$ 290 m as directed by the distribution of animals observed by the echosounder. During the sampling of each layer, the LRAUV ran on a tight circular yo-yo trajectory directly above the echosounder, remaining in its beam by acoustically tracking a station-keeping Wave Glider on the sea surface marking the echosounder's latitude and longitude. The persistent and simultaneous acoustic observation and eDNA acquisition enables identification of animals at precise locations to better understand their vertical migration behaviors. We present the methods and the system performance in the experiment.

Phytoplankton communities residing in the open ocean, the largest habitat on Earth, play a key role in global primary production. Through their influence on nutrient supply to the euphotic zone, open-ocean eddies impact the magnitude of primary production and its spatial and temporal distributions. It is important to gain a deeper understanding of the microbial ecology of marine ecosystems under the influence of eddy physics with the aid of advanced technologies. In March and April 2018, we deployed autonomous underwater and surface vehicles in a cyclonic eddy in the North Pacific Subtropical Gyre to investigate the variability of the microbial community in the deep chlorophyll maximum (DCM) layer. One long-range autonomous underwater vehicle (LRAUV) carrying a third-generation Environmental Sample Processor (3G-ESP) autonomously tracked and sampled the DCM layer for four days without surfacing. The sampling LRAUV's vertical position in the DCM layer was maintained by locking onto the isotherm corresponding to the chlorophyll peak. The vehicle ran on tight circles while drifting with the eddy current. This mode of operation enabled a quasi-Lagrangian time series focused on sampling the temporal variation of the DCM population. A companion LRAUV surveyed a cylindrical volume around the sampling LRAUV to monitor spatial and temporal variation in contextual water column properties. The simultaneous sampling and mapping enabled observation of DCM microbial community in its natural frame of reference.

As the capabilities of autonomous underwater vehicles (AUVs) improve, the missions become longer, riskier, and more complex. For AUVs to succeed in complex missions, they must be reliable in the face of subsystem failure and environmental challenges. In practice, fault detection activities carried out by most AUVs employ a rule-based emergency abort system that is triggered by specific events. AUVs equipped with the ability to diagnose faults and reason about mitigation actions in real time could improve their survivability and increase the value of individual deployments by replanning their mission in response to failures. In this paper, we focus on AUV autonomy as it pertains to self-perception and health monitoring and argue that automatic classification of state-sensor data represents an important enabling capability. We apply an online Bayesian nonparametric topic modeling technique to state-sensor data in order to automatically characterize the performance patterns of an AUV, then demonstrate how in combination with operator-supplied semantic labels these patterns can be used for fault detection and diagnosis by means of nearest-neighbor classifier. The method is applied in post-processing to diagnose faults that led to the temporary loss of the Monterey Bay Aquarium Research Institute's Tethys long-range AUV in two separate deployments. Our results show that the method is able to accurately identify and characterize patterns that correspond to various states of the AUV, and classify faults with high probability of detection and no false detects.