Mathematics – Logic
Scientific paper
Dec 2008
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008agufm.p53c1471c&link_type=abstract
American Geophysical Union, Fall Meeting 2008, abstract #P53C-1471
Mathematics
Logic
5194 Instruments And Techniques, 9820 Techniques Applicable In Three Or More Fields
Scientific paper
The Onboard Autonomous Science Investigation System (OASIS) evaluates geologic data gathered by a planetary rover. The system is designed to operate onboard a rover identifying and reacting to serendipitous science opportunities, such as rocks with novel properties. OASIS operates by analyzing data the rover gathers, and then using machine learning techniques, prioritizing the data based on criteria set by the science team. This prioritization can be used to organize data for transmission back to Earth and it can be used to search for specific targets it has been told to find by the science team. If one of these targets is found, it is identified as a new science opportunity and a "science alert" is sent to a planning and scheduling system. After reviewing the rover's current operational status to ensure that it has enough resources to complete its traverse and act on the new science opportunity, OASIS can change the command sequence of the rover in order to obtain additional science measurements. Currently, OASIS is being applied on a new front. OASIS is providing a new rover mission technology that enables targeted remote-sensing science in an automated fashion during or after rover traverses. Currently, targets for remote sensing instruments, especially narrow field-of-view instruments (such as the MER Mini- TES spectrometer or the 2009 MSL ChemCam spectrometer) must be selected manually based on imagery already on the ground with the operations team. OASIS will enable the rover flight software to analyze imagery onboard in order to autonomously select and sequence targeted remote-sensing observations in an opportunistic fashion. We are in the process of scheduling an onboard MER experiment to demonstrate the OASIS capability in early 2009.
Anderson Robert C.
Bornstein Benjamin
Castano Rebecca
de Granville C.
Estlin T.
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