Scheduling observation tasks for large-scale satellite constellation

Abstract

With the increasing demand for observations and the development of satellite technology, the scale of the Earth Observation Satellites (EOS) constellation is growing. To make the constellation more efficient during operation, scheduling imaging tasks of large-scale constellations is imperative. To solve the scheduling problem, we designed an allocation strategy for the daily operation and a Deep reinforcement learning (DRL) method for in-orbit emergencies. The results show that both of the method is effective.