On-orbit Servicing for Spacecraft Collision Avoidance With Autonomous Decision Making
It addresses spacecraft collision avoidance for satellite operators, but is incremental as it builds on existing methods for a specific application.
This study developed an AI-based autonomous on-orbit servicing system using reinforcement learning to detect potential collisions, rendezvous with endangered satellites, and execute optimal collision avoidance maneuvers, demonstrating initial feasibility for a one-servicer-to-one-satellite scenario.
This study develops an AI-based implementation of autonomous On-Orbit Servicing (OOS) mission to assist with spacecraft collision avoidance maneuvers (CAMs). We propose an autonomous `servicer' trained with Reinforcement Learning (RL) to autonomously detect potential collisions between a target satellite and space debris, rendezvous and dock with endangered satellites, and execute optimal CAM. The RL model integrates collision risk estimates, satellite specifications, and debris data to generate an optimal maneuver matrix for OOS rendezvous and collision prevention. We employ the Cross-Entropy algorithm to find optimal decision policies efficiently. Initial results demonstrate the feasibility of autonomous robotic OOS for collision avoidance services, focusing on one servicer spacecraft to one endangered satellite scenario. However, merging spacecraft rendezvous and optimal CAM presents significant complexities. We discuss design challenges and critical parameters for the successful implementation of the framework presented through a case study.