This innovation creates observations of both targeted geographical regions of interest and general mapping observations, while respecting spacecraft constraints such as data volume, observation timing, visibility, lighting, season, and science priorities. This tool, therefore, addresses both geometric and state/timing/resource constraints by using a grid-based approach. These set covering constraints are then incorporated into a greedy optimization scheduling algorithm that incorporates operations constraints to generate feasible schedules. The resultant tool generates schedules of hundreds of observations per week out of potentially thousands of observations.
Using greedy combinatorial optimization with gridded coverage representation, both targeted mapping observations (small geographical regions that can be covered in one or a small number of observations) and general mapping observations (large geographical regions that would take large numbers of observations, e.g. hundreds or more) can be scheduled. Using gridded coverage representation of a planetary surface, which maps all polygons (regions) into sets of points on a grid, makes polygon intersection very fast, and compiles the coverage problem into a set point covering the problem. At this point, the problem can be attacked using one of a set of combinatorial optimization techniques.
This work was done by Steve A. Chien, Gregg R. Rabideau, David A. McLaren, and Russell L. Knight of Caltech for NASA’s Jet Propulsion Laboratory.
This Brief includes a Technical Support Package (TSP).
Scheduling Targeted and Mapping Observations with State, Resource, and Timing Constraints
(reference NPO-47603) is currently available for download from the TSP library.
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