This paper presents a comparison of heuris- tics used to estimate the amount of time it would take for a spacecraft to image an area using Boustrophedon decomposition (Choset and Pignon 1998). Machine learning tech- niques are used to characterize algorithmic performance of coverage algorithms. It is shown that an ordinary least-squares linear model is among the most accurate in a set of constant and linear order regression models both in terms of memory consumption and schedule duration. These are demonstrated using the ASPEN planning system (Fukunaga et al. 1997) on the Eagle Eye domain.


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    Titel :

    Heuristic Area Cost Estimation for Observational Coverage Schedulers


    Beteiligte:

    Erscheinungsdatum :

    15.06.2017


    Medientyp :

    Preprint


    Format :

    Keine Angabe


    Sprache :

    Englisch



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