A Markov Decision Process Framework for Early Maneuver Decisions in Satellite Collision Avoidance

A new Markov decision process (MDP) framework has been developed to autonomously guide satellite collision avoidance maneuvers (CAMs). This framework utilizes a reinforcement learning policy gradient algorithm to optimize guidance policies based on historical CAM data, aiming to reduce collision risks while minimizing propellant consumption through earlier maneuver decisions.
This advancement is significant as it enhances the efficiency of satellite operations, potentially lowering costs and improving safety in space by reducing the likelihood of collisions, which is crucial for the sustainability of satellite networks.
The integration of reinforcement learning in MDPs reflects a growing trend in artificial intelligence, where optimizing decision-making processes in uncertain environments is becoming increasingly important. This approach not only applies to satellite navigation but also resonates with broader applications in various fields, including robotics and chemical processes, where safety and efficiency are paramount.

A Markov Decision Process Framework for Early Maneuver Decisions in Satellite Collision Avoidance