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Operating Earth observing satellites requires efficient planning methods thatcoordinate activities of multiple spacecraft. The satellite task planningproblem entails selecting actions that best satisfy mission objectives forautonomous execution. Task scheduling is often performed by human operatorsassisted by heuristic or rule-based planning tools. This approach does notefficiently scale to multiple assets as heuristics frequently fail to properlycoordinate actions of multiple vehicles over long horizons. Additionally, theproblem becomes more difficult to solve for large constellations as thecomplexity of the problem scales exponentially in the number of requestedobservations and linearly in the number of spacecraft. It is expected that newcommercial optical and radar imaging constellations will require automatedplanning methods to meet stated responsiveness and throughput objectives. Thispaper introduces a new approach for solving the satellite scheduling problem bygenerating an infeasibility-based graph representation of the problem andfinding a maximal independent set of vertices for the graph. The approach istested on a scenarios of up to 10,000 requested imaging locations for theSkysat constellation of optical satellites as well as simulated constellationsof up to 24 satellites. Performance is compared with contemporarygraph-traversal and mixed-integer linear programming approaches. Empiricalresults demonstrate improvements in both the solution time along with thenumber of scheduled collections beyond baseline methods. For large problems,the maximum independent set approach is able find a feasible schedule with 8%more collections in 75% less time.

A Maximum Independent Set Method for Scheduling Earth Observing Satellite Constellations