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Please use this identifier to cite or link to this item: http://hdl.handle.net/2014/38447

Title: Planning for rover opportunistic science
Authors: Gaines, Daniel M.
Estlin, Tara
Forest, Fisher
Chouinard, Caroline
Castano, Rebecca
Anderson, Robert C.
Keywords: onboard science analysis
artificial intelligence
Issue Date: 23-Jun-2004
Publisher: Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2004
Citation: International Workshop on Planning and Schedule for Space, Darmstadt, Germany
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.
URI: http://hdl.handle.net/2014/38447
Appears in Collections:JPL TRS 1992+

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