Satellite Scheduling

Satellite scheduling optimizes the allocation of satellite resources to efficiently complete observation tasks, considering constraints like visibility windows, maneuver limitations, and communication bandwidth. Current research heavily emphasizes the use of advanced algorithms such as Monte Carlo Tree Search, graph neural networks, and reinforcement learning-enhanced genetic algorithms to address the complexity of scheduling in diverse scenarios, including heterogeneous constellations and uncertain conditions like cloud cover. These improvements are crucial for maximizing the scientific return of Earth observation missions and enabling efficient data collection for various applications, including federated learning across satellite networks. The development of more efficient and robust scheduling techniques is vital for the increasing number and complexity of satellite missions.