Lightweight Task Coordination of LEO Satellite Cluster based on Distributed Reinforcement Learning

Low earth orbit (LEO) mega-constellation is in the stage of rapid construction, which is expected to serve more regions and apply to more scenarios, such as regional navigation enhancement, emergency communication, collaborative detection, etc. For such typical regional tasks, the construction of temporary LEO satellite clusters is an effective way to improve the quality of temporary services. However, LEO satellites move too fast, and relying on ground-based manual control can no longer meet the mission requirements. The cluster task coordination based on reinforcement learning can effectively improve the autonomous performance of the cluster, but it is difficult to apply to satellites, especially LEO satellites with extremely limited resources. Based on this, this paper proposes a lightweight collaboration method for LEO satellite constellation based on distributed reinforcement learning, which optimizes resources on the premise of ensuring the independent realization of task requirements. In this paper, the Q-learning reinforcement learning algorithm is used to compare the performance of the centralized Q-learning algorithm and distributed AC algorithm. The experiment of the star cluster cooperative detection task shows that the convergence time of the proposed algorithm is 81.5% less than that of the centralized algorithm, and it can run on embedded raspberry pi. It shows that the algorithm is expected to be applied to cluster task coordination of LEO satellites.