Science Goal Monitor: science goal driven automation for NASA missions

Details Science Goal Monitor: science goal driven automation for NASA missions Science Goal Monitor: science goal driven automation for NASA missions

Sep 2004

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004spie.5493...33k&link_type=abstract

Optimizing Scientific Return for Astronomy through Information Technologies. Edited by Quinn, Peter J.; Bridger, Alan. Proceed

Astronomy and Astrophysics

Astronomy

Scientific paper

Infusion of automation technologies into NASA's future missions will be essential because of the need to: (1) effectively handle an exponentially increasing volume of scientific data, (2) successfully meet dynamic, opportunistic scientific goals and objectives, and (3) substantially reduce mission operations staff and costs. While much effort has gone into automating routine spacecraft operations to reduce human workload and hence costs, applying intelligent automation to the science side, i.e., science data acquisition, data analysis and reactions to that data analysis in a timely and still scientifically valid manner, has been relatively under-emphasized. In order to introduce science driven automation in missions, we must be able to: capture and interpret the science goals of observing programs, represent those goals in machine interpretable language; and allow spacecrafts' onboard systems to autonomously react to the scientist's goals. In short, we must teach our platforms to dynamically understand, recognize, and react to the scientists' goals. The Science Goal Monitor (SGM) project at NASA Goddard Space Flight Center is a prototype software tool being developed to determine the best strategies for implementing science goal driven automation in missions. The tools being developed in SGM improve the ability to monitor and react to the changing status of scientific events. The SGM system enables scientists to specify what to look for and how to react in descriptive rather than technical terms. The system monitors streams of science data to identify occurrences of key events previously specified by the scientist. When an event occurs, the system autonomously coordinates the execution of the scientist's desired reactions. Through SGM, we will improve our understanding about the capabilities needed onboard for success, develop metrics to understand the potential increase in science returns, and develop an "operational" prototype so that the perceived risks associated with increased use of automation can be reduced. SGM is currently focused on two collaborations: 1. Yale University's SMARTS (Small and Moderate Aperture Research Telescope System) observing program - Modeling and testing ways in which SGM can be used to improve scientific returns on observing programs involving intrinsically variable astronomical targets. 2. The EO-1 (Earth Observing-1) mission - Modeling and testing ways in which SGM can be used to autonomously coordinate multiple platforms based on a set of scientific criteria. In this paper, we will discuss the status of the SGM project focusing primarily on our progress with the SMARTS collaboration.

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