An intelligent algorithm for autonomous scientific sampling with the VALKYRIE cryobot
Abstract The development of algorithms for agile science and autonomous exploration has been pursued in contexts ranging from spacecraft to planetary rovers to unmanned aerial vehicles to autonomous underwater vehicles. In situations where time, mission resources and communications are limited and t...
Published in: | International Journal of Astrobiology |
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Language: | English |
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Online Access: | http://dx.doi.org/10.1017/s1473550417000313 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S1473550417000313 |
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crcambridgeupr:10.1017/s1473550417000313 2023-05-15T16:20:39+02:00 An intelligent algorithm for autonomous scientific sampling with the VALKYRIE cryobot Clark, Evan B. Bramall, Nathan E. Christner, Brent Flesher, Chris Harman, John Hogan, Bart Lavender, Heather Lelievre, Scott Moor, Joshua Siegel, Vickie Stone, William C. 2017 http://dx.doi.org/10.1017/s1473550417000313 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S1473550417000313 en eng Cambridge University Press (CUP) https://www.cambridge.org/core/terms International Journal of Astrobiology volume 17, issue 3, page 247-257 ISSN 1473-5504 1475-3006 Earth and Planetary Sciences (miscellaneous) Space and Planetary Science Physics and Astronomy (miscellaneous) Ecology, Evolution, Behavior and Systematics journal-article 2017 crcambridgeupr https://doi.org/10.1017/s1473550417000313 2023-02-24T07:12:27Z Abstract The development of algorithms for agile science and autonomous exploration has been pursued in contexts ranging from spacecraft to planetary rovers to unmanned aerial vehicles to autonomous underwater vehicles. In situations where time, mission resources and communications are limited and the future state of the operating environment is unknown, the capability of a vehicle to dynamically respond to changing circumstances without human guidance can substantially improve science return. Such capabilities are difficult to achieve in practice, however, because they require intelligent reasoning to utilize limited resources in an inherently uncertain environment. Here we discuss the development, characterization and field performance of two algorithms for autonomously collecting water samples on VALKYRIE (Very deep Autonomous Laser-powered Kilowatt-class Yo-yoing Robotic Ice Explorer), a glacier-penetrating cryobot deployed to the Matanuska Glacier, Alaska (Mission Control location: 61°42′09.3″N 147°37′23.2″W). We show performance on par with human performance across a wide range of mission morphologies using simulated mission data, and demonstrate the effectiveness of the algorithms at autonomously collecting samples with high relative cell concentration during field operation. The development of such algorithms will help enable autonomous science operations in environments where constant real-time human supervision is impractical, such as penetration of ice sheets on Earth and high-priority planetary science targets like Europa. Article in Journal/Newspaper glacier Alaska Cambridge University Press (via Crossref) Valkyrie ENVELOPE(162.317,162.317,-77.550,-77.550) International Journal of Astrobiology 17 3 247 257 |
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Open Polar |
collection |
Cambridge University Press (via Crossref) |
op_collection_id |
crcambridgeupr |
language |
English |
topic |
Earth and Planetary Sciences (miscellaneous) Space and Planetary Science Physics and Astronomy (miscellaneous) Ecology, Evolution, Behavior and Systematics |
spellingShingle |
Earth and Planetary Sciences (miscellaneous) Space and Planetary Science Physics and Astronomy (miscellaneous) Ecology, Evolution, Behavior and Systematics Clark, Evan B. Bramall, Nathan E. Christner, Brent Flesher, Chris Harman, John Hogan, Bart Lavender, Heather Lelievre, Scott Moor, Joshua Siegel, Vickie Stone, William C. An intelligent algorithm for autonomous scientific sampling with the VALKYRIE cryobot |
topic_facet |
Earth and Planetary Sciences (miscellaneous) Space and Planetary Science Physics and Astronomy (miscellaneous) Ecology, Evolution, Behavior and Systematics |
description |
Abstract The development of algorithms for agile science and autonomous exploration has been pursued in contexts ranging from spacecraft to planetary rovers to unmanned aerial vehicles to autonomous underwater vehicles. In situations where time, mission resources and communications are limited and the future state of the operating environment is unknown, the capability of a vehicle to dynamically respond to changing circumstances without human guidance can substantially improve science return. Such capabilities are difficult to achieve in practice, however, because they require intelligent reasoning to utilize limited resources in an inherently uncertain environment. Here we discuss the development, characterization and field performance of two algorithms for autonomously collecting water samples on VALKYRIE (Very deep Autonomous Laser-powered Kilowatt-class Yo-yoing Robotic Ice Explorer), a glacier-penetrating cryobot deployed to the Matanuska Glacier, Alaska (Mission Control location: 61°42′09.3″N 147°37′23.2″W). We show performance on par with human performance across a wide range of mission morphologies using simulated mission data, and demonstrate the effectiveness of the algorithms at autonomously collecting samples with high relative cell concentration during field operation. The development of such algorithms will help enable autonomous science operations in environments where constant real-time human supervision is impractical, such as penetration of ice sheets on Earth and high-priority planetary science targets like Europa. |
format |
Article in Journal/Newspaper |
author |
Clark, Evan B. Bramall, Nathan E. Christner, Brent Flesher, Chris Harman, John Hogan, Bart Lavender, Heather Lelievre, Scott Moor, Joshua Siegel, Vickie Stone, William C. |
author_facet |
Clark, Evan B. Bramall, Nathan E. Christner, Brent Flesher, Chris Harman, John Hogan, Bart Lavender, Heather Lelievre, Scott Moor, Joshua Siegel, Vickie Stone, William C. |
author_sort |
Clark, Evan B. |
title |
An intelligent algorithm for autonomous scientific sampling with the VALKYRIE cryobot |
title_short |
An intelligent algorithm for autonomous scientific sampling with the VALKYRIE cryobot |
title_full |
An intelligent algorithm for autonomous scientific sampling with the VALKYRIE cryobot |
title_fullStr |
An intelligent algorithm for autonomous scientific sampling with the VALKYRIE cryobot |
title_full_unstemmed |
An intelligent algorithm for autonomous scientific sampling with the VALKYRIE cryobot |
title_sort |
intelligent algorithm for autonomous scientific sampling with the valkyrie cryobot |
publisher |
Cambridge University Press (CUP) |
publishDate |
2017 |
url |
http://dx.doi.org/10.1017/s1473550417000313 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S1473550417000313 |
long_lat |
ENVELOPE(162.317,162.317,-77.550,-77.550) |
geographic |
Valkyrie |
geographic_facet |
Valkyrie |
genre |
glacier Alaska |
genre_facet |
glacier Alaska |
op_source |
International Journal of Astrobiology volume 17, issue 3, page 247-257 ISSN 1473-5504 1475-3006 |
op_rights |
https://www.cambridge.org/core/terms |
op_doi |
https://doi.org/10.1017/s1473550417000313 |
container_title |
International Journal of Astrobiology |
container_volume |
17 |
container_issue |
3 |
container_start_page |
247 |
op_container_end_page |
257 |
_version_ |
1766008606576082944 |