  Small satellites have proven to be viable Earth observation platforms. These
satellites operate in regimes of increased trajectory uncertainty where
traditional planning approaches can lead to sub-optimal task plans, limiting
science return. Previous formulations of the space mission planning problem
decouple trajectory prediction and planning, which leads to task plans that are
less robust to uncertainty. We present a Markov decision process formulation of
the problem that accounts for uncertainties by incorporating a distribution of
possible collection windows characterized through Monte Carlo simulation. An
approximate solution technique yields tasking schedules with rewards comparable
to the conventional methods while simultaneously reducing the variations caused
by uncertainties and improving runtime.
