We present a novel system which blends multiple distinct sensing modalities in audio-visual surveys to assist marine biologists in collecting datasets for understanding the ecological relationship of fish and other organisms with their habitats on and around coral reefs. Our system, designed for the CUREE AUV, uses four hydrophones to determine the bearing to biological sound sources through beamforming. These observations are merged in a Bayesian Occupancy Grid to produce a 2D map of the acoustic activity of a coral reef. Simultaneously, the AUV uses unsupervised topic modeling to identify different benthic habitats. Combining these maps allows us to determine the level of acoustic activity within each habitat. We demonstrated the system in field trials on reefs in the U.S. Virgin Islands, where it was able to autonomously discover the favored habitats of snapping shrimp (genus Alpheus).

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.

The current approach to exploring and monitoring complex underwater ecosystems, such as coral reefs, is to conduct surveys using diver-held or static cameras, or deploying sensor buoys. These approaches often fail to capture the full variation and complexity of interactions between different reef organisms and their habitat. The CUREE platform presented in this paper provides a unique set of capabilities in the form of robot behaviors and perception algorithms to enable scientists to explore different aspects of an ecosystem. Examples of these capabilities include low-altitude visual surveys, soundscape surveys, habitat characterization, and animal following. We demonstrate these capabilities by describing two field deployments on coral reefs in the US Virgin Islands. In the first deployment, we show that CUREE can identify the preferred habitat type of snapping shrimp in a reef through a combination of a visual survey, habitat characterization, and a soundscape survey. In the second deployment, we demonstrate CUREE's ability to follow arbitrary animals by separately following a barracuda and stingray for several minutes each in midwater and benthic environments, respectively.

In this paper, we present an approach that enables long-term monitoring of biological activity on coral reefs by extending mission time and adaptively focusing sensing resources on high-value periods. Coral reefs are one of the most biodiverse ecosystems on the planet; yet they are also among the most imperiled: facing bleaching, ecological community collapses due to global climate change, and degradation from human activities. Our proposed method improves the ability of scientists to monitor biological activity and abundance using passive acoustic sensors. We accomplish this by extracting periodicities from the observed abundance, and using them to predict future abundance. This predictive model is then used with a Monte Carlo Tree Search planning algorithm to schedule sampling at periods of high biological activity, and power down the sensor during periods of low activity. In simulated experiments using long-term acoustic datasets collected in the US Virgin Islands, our adaptive Online Sensor Scheduling algorithm is able to double the lifetime of a sensor while simultaneously increasing the average observed acoustic activity by 21%.

This paper presents a framework that uses language instructions to define the constraints and objectives for robots gathering information about their environment. Designing autonomous robotic sampling missions requires deep knowledge of both autonomy systems and scientific domain expertise. Language commands provide an intuitive interface for operators to give complex instructions to robots. The key insight we leverage is using topological constraints to define routing directions from the language instruction such as ‘route to the left of the island.’ This work introduces three main contributions: a framework to map language instructions to constraints and rewards for robot planners, a topology constrained information gathering algorithm, and an automatic semantic feature detection algorithm for upwelling fronts. Our work improves on existing methods by not requiring training data with language instruction to planner constraint pairs, allowing new robotic domains such as marine robotics to use our method. This paper provides results demonstrating our framework producing correct constraints for 84.6% of instructions, from a systematically generated corpus of over 1.1 million instructions We also demonstrate the framework producing robot plans from language instructions for real-world scientific sampling missions with the Slocum underwater glider.

In this paper, we present a novel algorithm for constructing a maximally informative path for a robot in an information gathering task. We use a Self-Organizing Map (SOM) framework to discover important topological features in the information function. Using these features, we identify a set of distinct classes of trajectories, each of which has improved convexity compared with the original function. We then leverage a Stochastic Gradient Ascent (SGA) optimization algorithm within each of these classes to optimize promising representative paths. The increased convexity leads to an improved chance of SGA finding the globally optimal path across all homotopy classes. We demonstrate our approach in three different simulated experiments. First, we show that our SOM is able to correctly learn the topological features of a gyre environment with a well-defined topology. Then, in the second set of experiments, we compare the effectiveness of our algorithm in an information gathering task across the gyre world, a set of randomly generated worlds, and a set of worlds drawn from real-world ocean model data. In these experiments our algorithm performs competitively or better than a state-of-the-art Branch and Bound while requiring significantly less computation time. Lastly, the final set of experiments show that our method scales better than the comparison methods across different planning mission sizes in real-world environments.

In this work, we present a novel method for constructing a topological map of biological hotspots in an aquatic environment using a Fast Marching-based Voronoi segmentation. Using this topological map, we develop a closed form solution to the scheduling problem for any single path through the graph. Searching over the space of all paths allows us to compute a maximally informative path that traverses a subset of the hotspots, given some budget. Using a greedy-coverage algorithm we can then compute an informative path. We evaluate our method in a set of simulated trials, both with randomly generated environments and a real-world environment. In these trials, we show that our method produces a topological graph which more accurately captures features in the environment than standard thresholding techniques. Additionally, We show that our method can improve the performance of a greedy-coverage algorithm in the informative path planning problem by guiding it to different informative areas to help it escape from local maxima.

In this paper, we present a method to improve the navigation of tethered underwater vehicles by computing optimal paths that prevent their tethers from becoming entangled in obstacles. To accomplish this, we define the Non-Entangling Travelling Salesperson Problem (NE-TSP) as an extension of the Travelling Salesperson Problem with a non-entangling constraint. We compute the optimal solution to the NE-TSP by constructing a Mixed Integer Programming model, leveraging homotopy augmented graphs to plan an optimal trajectory through a set of inspection points, while maintaining a non-entangling guarantee. To avoid the computational expense of computing an optimal solution to the NE-TSP, we also introduce several methods to compute near-optimal solutions. In a set of simulated trials, our method was able to plan optimal non-entangling paths through a variety of environments. These results were then validated in a set of pool and field trials using a Seabotix vLBV300 underwater vehicle. The paths generated by our method were then compared to human-generated paths.