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Planning for rover opportunistic science

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dc.contributor.author Gaines, Daniel M.
dc.contributor.author Estlin, Tara
dc.contributor.author Forest, Fisher
dc.contributor.author Chouinard, Caroline
dc.contributor.author Castano, Rebecca
dc.contributor.author Anderson, Robert C.
dc.date.accessioned 2006-02-08T19:04:48Z
dc.date.available 2006-02-08T19:04:48Z
dc.date.issued 2004-06-23
dc.identifier.citation International Workshop on Planning and Schedule for Space, Darmstadt, Germany en
dc.identifier.clearanceno 04-1309
dc.identifier.uri http://hdl.handle.net/2014/38447
dc.description.abstract The Mars Exploration Rover Spirit recently set a record for the furthest distance traveled in a single sol on Mars. Future planetary exploration missions are expected to use even longer drives to position rovers in areas of high scientific interest. This increase provides the potential for a large rise in the number of new science collection opportunities as the rover traverses the Martian surface. In this paper, we describe the OASIS system, which provides autonomous capabilities for dynamically identifying and pursuing these science opportunities during longrange traverses. OASIS uses machine learning and planning and scheduling techniques to address this goal. Machine learning techniques are applied to analyze data as it is collected and quickly determine new science gods and priorities on these goals. Planning and scheduling techniques are used to alter the behavior of the rover so that new science measurements can be performed while still obeying resource and other mission constraints. We will introduce OASIS and describe how planning and scheduling algorithms support opportunistic science. en
dc.description.sponsorship NASA/JPL en
dc.format.extent 894139 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US en
dc.publisher Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2004 en
dc.subject onboard science analysis en
dc.subject artificial intelligence en
dc.title Planning for rover opportunistic science en
dc.type Preprint en


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