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Automatic Planning of Satellite Observations
Professor John McInroy is developing new methods for regularly characterizing Resident Space Objects (RSOs) at high resolution and low recurring cost using Observer satellites whose combined orbits collectively have excellent views of the RSOs. The new methods automatically generate plans using convex optimization methods so Humans and autonomous spacecraft can effectively collaborate. Humans modify and direct both overall objectives and short term goals, while autonomous planning techniques manage the complex, yet well-defined and predictable orbital events and sensor allocations. This allows humans to provide input where they are strong, such as understanding high level goals and reasoning amidst uncertainty. Simultaneously, it allows autonomous optimization and planning techniques to operate in domains where they are strong, in this case when many spacecraft must be jointly controlled over space and time to achieve a clearly defined overall final result. The figure illustrates the planning results, where Observer satellites are indicated by O’s, and sensed RSOs are depicted as X’s.