  This paper presents a semi-Markov decision process (SMDP) formulation of the
satellite task scheduling problem. This formulation can consider multiple
operational objectives simultaneously and plan transitions between distinct
functional modes. We consider the problem of scheduling image collections,
ground contacts, sun-pointed periods for battery recharging, and data recorder
management for an agile, resource-constrained Earth-observing spacecraft. By
considering multiple mission objectives simultaneously, the algorithm is able
to find optimized task schedule that satisfies all operational constraints in a
single planning step, thus reducing the operational complexity and number of
steps involved in mission planning. We present two solution approaches based on
forward search and Monte Carlo Tree search. We baseline against rule-based,
graph search, and mixed-integer-linear programming approaches. The SMDP
formulation is evaluated in both single-objective and multi-objective
scenarios. The SMDP solution is found to perform comparably with the baseline
methods at greatly increased speed in single-objective scenarios and greater
schedule reward in multi-objective.
