Autonomous Rendezvous and Docking (RVD) have been extensively studied in recent years, addressing the stringent requirements of spacecraft dynamics variations and the limitations of GNC systems. This paper presents an innovative approach employing Artificial Neural Networks (ANN) trained through Reinforcement… Read More »Neural-based Control for CubeSat Docking Maneuvers
Spacecraft operate in harsh environments with relevant computational constraints as well as limited communication to Earth. Traditional Fault Detection, Isolation, and Recovery (FDIR) systems rely on pre-programmed thresholds, which can miss unforeseen anomalies, thus requiring frequent human intervention from ground.… Read More »Onboard AI for Enhanced FDIR: Revolutionizing Spacecraft Operations with Anomaly Detection
Effective routing in satellite mega-constellations has become crucial to facilitate the handling of increasing traffic loads, more complex network architectures, as well as the integration into 6G networks. To enhance adaptability as well as robustness to unpredictable traffic demands, and… Read More »Shaping Rewards, Shaping Routes: On Multi-Agent Deep Q-Networks for Routing in Satellite Constellation Networks
Satellites play a crucial role in the global communications system, while being subjected to the harsh space environment that often causes component degradation. Early failure detection is important to ensure that the criticalservices they provide are not interrupted. This work… Read More »Graph Neural Networks for Anomaly Detection in Spacecraft