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In this paper, we propose a novel method for autonomously seeking out sparsely distributed targets in an unknown underwater environment. Our Sparse Adaptive Search and Sample (SASS) algorithm mixes low-altitude observations of discrete targets with high-altitude observations of the surrounding substrates. By using prior information about the distribution of targets across substrate types in combination with belief modelling over these substrates in the environment, high-altitude observations provide information that allows SASS to quickly guide the robot to areas with high target densities. A maximally informative path is autonomously constructed online using Monte Carlo Tree Search with a novel acquisition function to guide the search to maximise observations of unique targets. We demonstrate our approach in a set of simulated trials using a novel generative species model. SASS consistently outperforms the canonical boustrophedon planner by up to 36% in seeking out unique targets in the first 75 - 90% of time it takes for a boustrophedon survey. Additionally, we verify the performance of SASS on two real world coral reef datasets. 

Abstract The ocean's twilight zone (TZ) is a vast, globe-spanning region of the ocean. Home to myriad fishes and invertebrates, mid-water fishes alone may constitute 10 times more biomass than all current ocean wild-caught fisheries combined. Life in the TZ supports ocean food webs and plays a critical role in carbon capture and sequestration. Yet the ecological roles that mesopelagic animals play in the ocean remain enigmatic. This knowledge gap has stymied efforts to determine the effects that extraction of mesopelagic biomass by industrial fisheries, or alterations due to climate shifts, may have on ecosystem services provided by the open ocean. We propose to develop a scalable, distributed observation network to provide sustained interrogation of the TZ in the northwest Atlantic. The network will leverage a “tool-chest” of emerging and enabling technologies including autonomous, unmanned surface and underwater vehicles and swarms of low-cost “smart” floats. Connectivity among in-water assets will allow rapid assimilation of data streams to inform adaptive sampling efforts. The TZ observation network will demonstrate a bold new step towards the goal of continuously observing vast regions of the deep ocean, significantly improving TZ biomass estimates and understanding of the TZ's role in supporting ocean food webs and sequestering carbon.