对地观测卫星星座调度的最大独立集方法
A Maximum Independent Set Method for Scheduling Earth Observing Satellite Constellations
摘要：
运行地球观测卫星需要协调多个航天器活动的有效规划方法。卫星任务规划问题需要选择最能满足任务目标的自主执行行动。任务调度通常由人工操作员在启发式或基于规则的规划工具的帮助下执行。这种方法不能有效地扩展到多个资产，因为物流经常无法在长期内适当地协调多个车辆的动作。此外，对于大型星座，问题变得更加难以解决，因为问题的复杂性在所请求的观测数量上呈指数级变化，在航天器数量上呈线性变化。预计新的商业光学和雷达成像星座将需要自动规划方法，以满足规定的响应能力和吞吐量目标。本文介绍了一种新的方法来解决卫星调度问题，通过生成一个基于不可行性的图表示的问题，并找到一个最大的独立集的顶点图。该方法是测试的场景多达10,000个请求成像位置的天空卫星星座的光学卫星，以及模拟星座多达24颗卫星。性能与当代图遍历和混合整数线性规划方法进行比较。实证结果表明，改进的解决方案的时间以及预定的集合超过基线方法的数量。对于大型问题，最大独立集方法能够找到一个可行的时间表，在75%的时间内收集8%以上。
英文摘要：
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.
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