A Maximum Independent Set Method for Scheduling Earth Observing Satellite Constellations

TL;DR

This paper presents a novel maximum independent set method for scheduling Earth observing satellite constellations, significantly improving solution time and schedule quality for large-scale problems compared to existing approaches.

Contribution

It introduces an infeasibility-based graph representation and maximum independent set approach for satellite scheduling, enabling efficient handling of large constellations.

Findings

Achieves 8% more scheduled collections on large problems.

Reduces scheduling time by 75% compared to baseline methods.

Effective for scenarios with up to 10,000 imaging requests.

Abstract

Operating Earth observing satellites requires efficient planning methods that coordinate activities of multiple spacecraft. The satellite task planning problem entails selecting actions that best satisfy mission objectives for autonomous execution. Task scheduling is often performed by human operators assisted by heuristic or rule-based planning tools. This approach does not efficiently scale to multiple assets as heuristics frequently fail to properly coordinate actions of multiple vehicles over long horizons. Additionally, the problem becomes more difficult to solve for large constellations as the complexity of the problem scales exponentially in the number of requested observations and linearly in the number of spacecraft. It is expected that new commercial optical and radar imaging constellations will require automated planning methods to meet stated responsiveness and throughput objectives. This paper introduces a new approach for solving the satellite scheduling problem by generating an infeasibility-based graph representation of the problem and finding a maximal independent set of vertices for the graph. The approach is tested on a scenarios of up to 10,000 requested imaging locations for the Skysat constellation of optical satellites as well as simulated constellations of up to 24 satellites. Performance is compared with contemporary graph-traversal and mixed-integer linear programming approaches. Empirical results demonstrate improvements in both the solution time along with the number 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.