作者：
Wilcox, GalenMurray, RyanMIT
WHOI Joint Program in Oceanography/Applied Ocean Science & Engineering Woods Hole MA United States MIT
WHOI Joint Program in Oceanography/Applied Ocean Science & Engineering CambridgeMA United States North Carolina State University
RaleighNC United States
We represent the outermost shear interface of a eddy by a circular vortex sheet in two dimensions, and provide a new proof of linear instability via the Birkhoff-Rott equation. Like planar vortex sheets, circular shee...
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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 ...
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.
Successful applications of complex vision-based behaviours underwater have lagged behind progress in terrestrial and aerial domains. This is largely due to the degraded image quality resulting from the physical phenom...
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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 ...
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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 or...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
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).
Successful applications of complex vision-based behaviours underwater have lagged behind progress in terrestrial and aerial domains. This is largely due to the degraded image quality resulting from the physical phenom...
Successful applications of complex vision-based behaviours underwater have lagged behind progress in terrestrial and aerial domains. This is largely due to the degraded image quality resulting from the physical phenomena involved in underwater image formation. Spectrally-selective light attenuation drains some colors from underwater images while backscattering adds others, making it challenging to perform vision-based tasks underwater. State-of-the-art methods for underwater color correction optimize the parameters of image formation models to restore the full spectrum of color to underwater imagery. However, these methods have high computational complexity that is unfavourable for realtime use by autonomous underwater vehicles (AUVs), as a result of having been primarily designed for offline color correction. Here, we present DeepSeeColor, a novel algorithm that combines a state-of-the-art underwater image formation model with the computational efficiency of deep learning frameworks. In our experiments, we show that DeepSeeColor offers comparable performance to the popular “Sea-Thru” algorithm [1] while being able to rapidly process images at up to 60Hz, thus making it suitable for use onboard AUVs as a preprocessing step to enable more robust vision-based behaviours.
Microplastics (MPs) have been found in a diverse range of organisms across trophic levels. While a majority of the information on organismal exposure to plastics in the environment comes from gastrointestinal (GI) dat...
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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 observ...
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ISBN:
(数字)9798350377705
ISBN:
(纸本)9798350377712
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.
Highlights• ML algorithms can classify water and sea ice characteristics in the coastal Arctic.• Spring freshet runoff over sea ice is optically distinct from surface melt ponds.• Time-series analyses of satellite ima...
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Since 2011, Sargassum, a free-floating macroalgae, has been causing significant ecological and economic damage to coastal regions in the Northern Equatorial Atlantic due to its voluminous blooms. To better understand ...
Since 2011, Sargassum, a free-floating macroalgae, has been causing significant ecological and economic damage to coastal regions in the Northern Equatorial Atlantic due to its voluminous blooms. To better understand the fate and effects of Sargassum, approaches are needed to track its transport. Here we developed a low-cost drifter, designed to entangle with Sargassum, to aid in its tracking providing in situ movement data to ground-truth models and supplementing gaps in satellite imaging. The results of 27 drifter designs and five days of field trials are presented. The successful entanglement and tracking with the Sargassum demonstrated here can guide future studies to further our understanding of the movement of Sargassum in the great Atlantic Sargassum belt (GASB).
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