Fully-distributed autonomous scheduling for Earth-observing constellations

The coordination of multiple Earth-observing satellites presents a significant scheduling challenge. This paper introduces a fully distributed autonomous scheduling solution that utilizes a learning-based mechanism through an independent proximal policy optimization (IPPO) algorithm. Each satellite independently makes decisions regarding tasks, such as imaging, desaturation, and charging, while adapting to dynamic environmental changes to enhance its real-time constellation scheduling performance. The proposed fully distributed strategy enables individual satellites to update their policies based solely on their observations. The only requirement is the unidirectional broadcast of a completion flag upon target observation. This approach distinguishes itself from traditional centralized methods, thus enhancing the overall robustness and security of the system. In simulations, our strategy exhibited effective observational mission planning results for major cities worldwide. The results show that the proposed method addresses both autonomous scheduling and significantly improves constellation performance and reliability.