Computer Science – Artificial Intelligence
Scientific paper
Dec 2001
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001aas...199.0804t&link_type=abstract
American Astronomical Society, 199th AAS Meeting, #08.04; Bulletin of the American Astronomical Society, Vol. 33, p.1316
Computer Science
Artificial Intelligence
Scientific paper
The planning and scheduling (P&S) systems used in operating the Hubble Space Telescope (HST) have undergone such substantial and pervasive re-engineering that today they dimly resemble those used when HST was launched. Processes (i.e., software, procedures, networking, etc.) which allow program implementation, the generation of a Long Range Plan (LRP), and the scheduling of science and mission activities have improved drastically in nearly 12 years, resulting in a consistently high observing efficiency, a stable LRP that principal investigators can use, exceptionally clean command loads uplinked to the spacecraft, and the capability of a very fast response time due to onboard anomalies or targets of opportunity. In this presentation we describe many of the systems which comprise the P&S ("front-end") system for HST, how and why they were improved, and what benefits have been realized by either the HST user community or the STScI staff. The systems include the Guide Star System, the Remote Proposal Submission System - 2 (RPS2), Artificial Intelligence (AI) planning tools such as Spike, and the science and mission scheduling software. We also describe how using modern software languages such as Python and better development practices allow STScI staff to do more with HST (e.g., to handle much more science data when ACS is installed) without increasing the cost to HST operations.
Chance Don R.
Jordan Ian J. E.
Patterson Alan P.
Stanley Matthew
Taylor David K.
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