CUREE: A Curious Underwater Robot for Ecosystem Exploration

• URL: http://arxiv.org/abs/2303.03943v2
• Date: Thu, 20 Apr 2023 15:54:44 GMT
• Title: CUREE: A Curious Underwater Robot for Ecosystem Exploration
• Authors: Yogesh Girdhar, Nathan McGuire, Levi Cai, Stewart Jamieson, Seth McCammon, Brian Claus, John E. San Soucie, Jessica E. Todd, T. Aran Mooney
• Abstract summary: The CUREE platform provides a unique set of capabilities in the form of robot behaviors and perception algorithms. Examples of these capabilities include low-altitude visual surveys, soundscape surveys, habitat characterization, and animal following.
• Score: 6.486523185975219
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Combining Observational Data and Language for Species Range Estimation [63.65684199946094]
  We propose a novel approach combining millions of citizen science species observations with textual descriptions from Wikipedia. Our framework maps locations, species, and text descriptions into a common space, enabling zero-shot range estimation from textual descriptions. Our approach also acts as a strong prior when combined with observational data, resulting in more accurate range estimation with less data.
  arXiv  Detail & Related papers  (2024-10-14T17:22:55Z)
• Deep learning for multi-label classification of coral conditions in the Indo-Pacific via underwater photogrammetry [24.00646413446011]
  This study created a dataset representing common coral conditions and associated stressors in the Indo-Pacific. It assessed existing classification algorithms and proposed a new multi-label method for automatically detecting coral conditions and extracting ecological information. The proposed method accurately classified coral conditions as healthy, compromised, dead, and rubble.
  arXiv  Detail & Related papers  (2024-03-09T14:42:16Z)
• Robot Goes Fishing: Rapid, High-Resolution Biological Hotspot Mapping in Coral Reefs with Vision-Guided Autonomous Underwater Vehicles [6.658103076536836]
  Biological hotspot detection can help coral reef managers prioritize limited resources for monitoring and intervention tasks. Here, we explore the use of autonomous underwater vehicles (AUVs) with cameras, coupled with visual detectors and photogrammetry, to map and identify these hotspots. To the best of our knowledge, we present one of the first attempts at using an AUV to gather visually-observed, fine-grain biological hotspot maps.
  arXiv  Detail & Related papers  (2023-05-03T16:12:47Z)
• Reef-insight: A framework for reef habitat mapping with clustering methods via remote sensing [0.3670422696827526]
  We present Reef-Insight, an unsupervised machine learning framework that features advanced clustering methods and remote sensing for reef habitat mapping. Our framework compares different clustering methods for reef habitat mapping using remote sensing data. Our results indicate that Reef-Insight can generate detailed reef habitat maps outlining distinct reef habitats.
  arXiv  Detail & Related papers  (2023-01-26T00:03:09Z)
• Climate Change & Computer Audition: A Call to Action and Overview on Audio Intelligence to Help Save the Planet [98.97255654573662]
  This work provides an overview of areas in which audio intelligence can contribute to overcome climate-related challenges. We categorise potential computer audition applications according to the five elements of earth, water, air, fire, and aether.
  arXiv  Detail & Related papers  (2022-03-10T13:32:31Z)
• Fish sounds: towards the evaluation of marine acoustic biodiversity through data-driven audio source separation [1.9116784879310027]
  The marine ecosystem is changing at an alarming rate, exhibiting biodiversity loss and the migration of tropical species to temperate basins. One of the most popular and effective methods for monitoring marine biodiversity is passive acoustics monitoring (PAM) In this work, we show that the same techniques can be successfully used to automatically extract fish vocalizations in PAM recordings.
  arXiv  Detail & Related papers  (2022-01-13T14:57:34Z)
• Information is Power: Intrinsic Control via Information Capture [110.3143711650806]
  We argue that a compact and general learning objective is to minimize the entropy of the agent's state visitation estimated using a latent state-space model. This objective induces an agent to both gather information about its environment, corresponding to reducing uncertainty, and to gain control over its environment, corresponding to reducing the unpredictability of future world states.
  arXiv  Detail & Related papers  (2021-12-07T18:50:42Z)
• Discriminative Singular Spectrum Classifier with Applications on Bioacoustic Signal Recognition [67.4171845020675]
  We present a bioacoustic signal classifier equipped with a discriminative mechanism to extract useful features for analysis and classification efficiently. Unlike current bioacoustic recognition methods, which are task-oriented, the proposed model relies on transforming the input signals into vector subspaces. The validity of the proposed method is verified using three challenging bioacoustic datasets containing anuran, bee, and mosquito species.
  arXiv  Detail & Related papers  (2021-03-18T11:01:21Z)
• Movement Tracks for the Automatic Detection of Fish Behavior in Videos [63.85815474157357]
  We offer a dataset of sablefish (Anoplopoma fimbria) startle behaviors in underwater videos, and investigate the use of deep learning (DL) methods for behavior detection on it. Our proposed detection system identifies fish instances using DL-based frameworks, determines trajectory tracks, derives novel behavior-specific features, and employs Long Short-Term Memory (LSTM) networks to identify startle behavior in sablefish.
  arXiv  Detail & Related papers  (2020-11-28T05:51:19Z)
• Automated Stitching of Coral Reef Images and Extraction of Features for Damselfish Shoaling Behavior Analysis [0.0]
  In marine ethology, the investigation of behavioral patterns in schooling species can provide supplemental information in the planning and management of marine resources. Currently, damselfish species, although prevalent in tropical waters, have no adequate established base behavior information. Visual marine data captured in the wild are scarce and prone to multiple scene variations, primarily caused by motion and changes in the natural environment.
  arXiv  Detail & Related papers  (2020-06-28T00:56:51Z)
• VisualEchoes: Spatial Image Representation Learning through Echolocation [97.23789910400387]
  Several animal species (e.g., bats, dolphins, and whales) and even visually impaired humans have the remarkable ability to perform echolocation. We propose a novel interaction-based representation learning framework that learns useful visual features via echolocation. Our work opens a new path for representation learning for embodied agents, where supervision comes from interacting with the physical world.
  arXiv  Detail & Related papers  (2020-05-04T16:16:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.