Towards automated sample collection and return in extreme underwater environments

dc.contributor.author Billings, Gideon
dc.contributor.author Walter, Matthew R.
dc.contributor.author Pizarro, Oscar
dc.contributor.author Johnson-Roberson, Matthew
dc.contributor.author Camilli, Richard
dc.date.accessioned 2022-10-11T20:02:42Z
dc.date.available 2022-10-11T20:02:42Z
dc.date.issued 2022-06-24
dc.description © The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Billings, G., Walter, M., Pizarro, O., Johnson-Roberson, M., & Camilli, R. Towards automated sample collection and return in extreme underwater environments. Journal of Field Robotics, 2(1), (2022): 1351–1385, https://doi.org/10.55417/fr.2022045. en_US
dc.description.abstract In this report, we present the system design, operational strategy, and results of coordinated multivehicle field demonstrations of autonomous marine robotic technologies in search-for-life missions within the Pacific shelf margin of Costa Rica and the Santorini-Kolumbo caldera complex, which serve as analogs to environments that may exist in oceans beyond Earth. This report focuses on the automation of remotely operated vehicle (ROV) manipulator operations for targeted biological sample-collection-and-return from the seafloor. In the context of future extraterrestrial exploration missions to ocean worlds, an ROV is an analog to a planetary lander, which must be capable of high-level autonomy. Our field trials involve two underwater vehicles, the SuBastian ROV and the Nereid Under Ice (NUI) hybrid ROV for mixed initiative (i.e., teleoperated or autonomous) missions, both equipped seven-degrees-of-freedom hydraulic manipulators. We describe an adaptable, hardware-independent computer vision architecture that enables high-level automated manipulation. The vision system provides a three-dimensional understanding of the workspace to inform manipulator motion planning in complex unstructured environments. We demonstrate the effectiveness of the vision system and control framework through field trials in increasingly challenging environments, including the automated collection and return of biological samples from within the active undersea volcano Kolumbo. Based on our experiences in the field, we discuss the performance of our system and identify promising directions for future research. en_US
dc.description.sponsorship This work was funded under a NASA PSTAR grant, number NNX16AL08G, and by the National Science Foundation under grants IIS-1830660 and IIS-1830500. The authors would like to thank the Costa Rican Ministry of Environment and Energy and National System of Conservation Areas for permitting research operations at the Costa Rican shelf margin, and the Schmidt Ocean Institute (including the captain and crew of the R/V Falkor and ROV SuBastian) for their generous support and making the FK181210 expedition safe and highly successful. Additionally, the authors would like to thank the Greek Ministry of Foreign Affairs for permitting the 2019 Kolumbo Expedition to the Kolumbo and Santorini calderas, as well as Prof. Evi Nomikou and Dr. Aggelos Mallios for their expert guidance and tireless contributions to the expedition. en_US
dc.identifier.citation Billings, G., Walter, M., Pizarro, O., Johnson-Roberson, M., & Camilli, R. (2022). Towards automated sample collection and return in extreme underwater environments. Journal of Field Robotics, 2(1), 1351–1385. en_US
dc.identifier.doi 10.55417/fr.2022045
dc.identifier.uri https://hdl.handle.net/1912/29409
dc.publisher Wiley en_US
dc.relation.uri https://doi.org/10.55417/fr.2022045
dc.rights Attribution 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ *
dc.subject Underwater robotics en_US
dc.subject Mobile manipulation en_US
dc.subject Marine robotics en_US
dc.subject Exploration en_US
dc.title Towards automated sample collection and return in extreme underwater environments en_US
dc.type Article en_US
dspace.entity.type Publication
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relation.isAuthorOfPublication.latestForDiscovery 9168be96-4714-4f46-b084-0b6d342a863f
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