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Agility, robustness, endurance, and sustainability are the main challenges of the current distributed systems for ocean objects identification. Nowadays, developing a novel marine observation network to help identify threats and to provide both an early warning and data for forecasting models is a priority of marine missions. Autonomous systems, such as underwater robots and drones, can provide worthwhile information from the ocean environment; still, they have challenges associated with endurance, performance, and recovery. Skimming drones cannot be used to perform underwater missions, need a significant amount of energy to take off, and have stability problems due to the constant ocean wave motion. As for underwater swimming robots, they are generally slow and use a significant amount of energy. To this end, there is a need to design some novel bioinspired amphibious concepts that can overcome these challenges. In this paper, a network of distributed hybrid-amphibious robots with energy harvesting capabilities will be presented. This is accomplished through novel robot systems. The Lizard-Spider Octopus-Jellyfish-Rolling Robot (LSOJRR) is one of these novel ideas, which imitates the characteristics of a Golden wheel spider with rolling, jumping, and folding capabilities over the water, a Green Basilisk lizard with running capability over the water, and an octopus with unique underwater propulsion mechanism. The LSOJRR also has applications beyond Earth, and alternative designs of this robot are explored, particularly those involving the dispersal of swarms of smaller robots that also derive their design from biology. All of the designs presented in this paper draw inspiration from nature, and strive to achieve the goal of furthering the development for marine exploration.
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FathomNet: A global image database for enabling artificial intelligence in the ocean
Abstract The ocean is experiencing unprecedented rapid change, and visually monitoring marine biota at the spatiotemporal scales needed for responsible stewardship is a formidable task. As baselines are sought by the research community, the volume and rate of this required data collection rapidly outpaces our abilities to process and analyze them. Recent advances in machine learning enables fast, sophisticated analysis of visual data, but have had limited success in the ocean due to lack of data standardization, insufficient formatting, and demand for large, labeled datasets. To address this need, we built FathomNet, an open-source image database that standardizes and aggregates expertly curated labeled data. FathomNet has been seeded with existing iconic and non-iconic imagery of marine animals, underwater equipment, debris, and other concepts, and allows for future contributions from distributed data sources. We demonstrate how FathomNet data can be used to train and deploy models on other institutional video to reduce annotation effort, and enable automated tracking of underwater concepts when integrated with robotic vehicles. As FathomNet continues to grow and incorporate more labeled data from the community, we can accelerate the processing of visual data to achieve a healthy and sustainable global ocean.
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- Award ID(s):
- 2230776
- PAR ID:
- 10379815
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41598-022-19939-2
