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CUREE: A Curious Underwater Robot for Ecosystem Exploration

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arXiv 2023年

作者： Girdhar, Yogesh McGuire, Nathan Cai, Levi Jamieson, Stewart McCammon, Seth Claus, Brian Soucie, John San E. Todd, Jessica E. Mooney, T. Aran United States MIT-WHOI Joint Program in Oceanography Applied Ocean Science and Engineering United States

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. © 2023, CC BY-NC-ND.

关键词： Ecosystems

Multi-Robot Distributed Semantic Mapping in Unfamiliar Environments through Online Matching of Learned Representations

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Multi-Robot Distributed Semantic Mapping in Unfamiliar Envir...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Stewart Jamieson Kaveh Fathian Kasra Khosoussi Jonathan P. How Yogesh Girdhar MIT-WHOI Joint Program in Oceanography/Applied Ocean Science and Engineering Massachusetts Institute of Technology (MIT) Woods Hole Oceanographic Institution (WHOI)

We present a solution to multi-robot distributed semantic mapping of novel and unfamiliar environments. Most state-of-the-art semantic mapping systems are based on supervised learning algorithms that cannot classify novel observations online. While unsupervised learning algorithms can invent labels for novel observations, approaches to detect when multiple robots have independently developed their own labels for the same new class are prone to erroneous or inconsistent matches. These issues worsen as the number of robots in the system increases and prevent fusing the local maps produced by each robot into a consistent global map, which is crucial for cooperative planning and joint mission summarization. Our proposed solution overcomes these obstacles by having each robot learn an unsupervised semantic scene model online and use a multiway matching algorithm to identify consistent sets of matches between learned semantic labels belonging to different robots. Compared to the state of the art, the proposed solution produces 20-60% higher quality global maps that do not degrade even as many more local maps are fused.

关键词： Conferences Semantics Supervised learning Buildings Bandwidth Path planning Classification algorithms

Multi-robot distributed semantic mapping in unfamiliar environments through online matching of learned representations

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arXiv 2021年

作者： Jamieson, Stewart Fathian, Kaveh Khosoussi, Kasra How, Jonathan P. Girdhar, Yogesh The MIT-WHOI Joint Program in Oceanography/Applied Ocean Science and Engineering United States United States United States

关键词： Industrial robots

A modern approach to the Kelvin-Helmholtz instability on circular vortex sheets

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arXiv 2024年

作者： Wilcox, Galen Murray, Ryan MIT 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 sheets are found to be susceptible to a violent shortwave instability known as the Kelvin-Helmholtz instability, with some modifications due to vortex sheet geometry. This result is in agreement with the classical derivation of [35, 49], but our modern approach provides greater clarity. We go on to show that the linear evolution problem can develop a singularity from analytic initial data in a time proportional to the square of the vortex sheet radius. Numerical evidence is presented that suggests this linear instability captures the wave-breaking mechanism observed in nonlinear point vortex simulations. Based on these results, we hypothesize that the Kelvin-Helmholtz instability can contribute to the development of secondary instability for eddies in two-dimensional turbulent flow. Copyright © 2024, The Authors. All rights reserved.

关键词： Turbulent flow

DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles via Deep Learning Methods

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DeepSeeColor: Realtime Adaptive Color Correction for Autonom...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Stewart Jamieson Jonathan P. How Yogesh Girdhar MIT-WHOI Joint Program in Oceanography/Applied Ocean Science and Engineering Department of Aeronautics and Astronautics Massachusetts Institute of Technology (MIT) Applied Ocean Physics and Engineering Department Woods Hole Oceanographic Institution (WHOI)

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.

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Wireless Data Harvesting Using the AUV Sentry and whoi Optical Modem

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Wireless Data Harvesting Using the AUV Sentry and WHOI Optic...

oceans MTS/IEEE Washington (Conference)

作者： Clifford Pontbriand Norm Farr Johanna Hansen James C. Kinsey Leo-Paul Pelletier Jonathan Ware Dehann Fourie Advanced Ocean Physics and Engineering Department Woods Hole Oceanographic Institution MIT-WHOI Joint Program in Oceanography/Applied Ocean Science & Engineering CSAIL

ISBN: (纸本)9781467393744

The optical communications group at Woods Hole oceanographic Institution has demonstrated wireless optical transmission of data from a seafloor node to the Sentry AUV. This effort is based on the latest version of the optical modem under continual development at whoi, and it builds on fieldwork conducted in 2012 and 2013 during which we successfully transferred data from seafloor-deployed optical modem systems using a wire-lowered optical modem from a ship. While our previous work used a high-bandwidth optical modem for the data uplink and an acoustic modem "backlink" to control the file transfer, the optical modem described in this paper supports fully bidirectional optical communication - essentially subsea WiFi. A subordinate objective of our work was to literally map the operational space for an AUV-bottom node optical link. Armed with this spatial map, we can plan for future "connected" AUV operations in support of data mule, inspection, manipulation, and other underwater semi-autonomous tasks.

关键词： Underwater optical communications Freespace Optical modem Underwater wireless communications A UV Data mule

Exactly sparse delayed-state filters

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Exactly sparse delayed-state filters

2005 IEEE International Conference on Robotics and Automation

作者： Eustice, Ryan M. Singh, Hanumant Leonard, John J. MIT/WHOI Joint Program in Applied Ocean Science and Engineering Woods Hole Oceanographic Institution Woods Hole MA United States Department of Applied Ocean Physics and Engineering Woods Hole Oceanographic Institution Woods Hole MA United States Department of Ocean Engineering Massachusetts Institute of Technology Cambridge MA United States

ISBN: (纸本)078038914X

This paper presents the novel insight that the SLAM information matrix is exactly sparse in a delayed-state framework. Such a framework is used in view-based representations of the environment which rely upon scan-matching raw sensor data. Scan-matching raw data results in virtual observations of robot motion with respect to a place its previously been. The exact sparseness of the delayed-state information matrix is in contrast to other recent feature-based SLAM information algorithms like Sparse Extended Information Filters or Thin Junction Tree Filters. These methods have to make approximations in order to force the feature-based SLAM information matrix to be sparse. The benefit of the exact sparseness of the delayed-state framework is that it allows one to take advantage of the information space parameterization without having to make any approximations. Therefore, it can produce equivalent results to the "full-covariance" solution. ©2005 IEEE.

关键词： Digital filters

Active reward learning for co-robotic vision based exploration in bandwidth limited environments

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arXiv 2020年

作者： Jamieson, Stewart How, Jonathan P. Girdhar, Yogesh MIT-WHOI Joint Program in Applied Ocean Science and Engineering United States United States

We present a novel POMDP problem formulation for a robot that must autonomously decide where to go to collect new and scientifically relevant images given a limited ability to communicate with its human operator. From this formulation we derive constraints and design principles for the observation model, reward model, and communication strategy of such a robot, exploring techniques to deal with the very highdimensional observation space and scarcity of relevant training data. We introduce a novel active reward learning strategy based on making queries to help the robot minimize path "regret" online, and evaluate it for suitability in autonomous visual exploration through simulations. We demonstrate that, in some bandwidth-limited environments, this novel regret-based criterion enables the robotic explorer to collect up to 17% more reward per mission than the next-best criterion. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine design

Discovering Biological Hotspots with a Passively Listening AUV

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Discovering Biological Hotspots with a Passively Listening A...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Seth McCammon Stewart Jamieson T. Aran Mooney Yogesh Girdhar Woods Hole Oceanographic Institution (WHOI) Woods Hole MA Department of Aeronautics and Astronautics MIT-WHOI Joint Program in Oceanography/Applied Ocean Science and Engineering Massachusetts Institute of Technology (MIT)

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).

关键词： Surveys Temperature measurement Visualization Array signal processing Habitats Marine vegetation Fish