AstroShield: A Physics-Verified Learning Framework for Explainable Spacecraft Collision Avoidance

As satellite constellations expand and the catalogue of tracked debris grows, operators need efficient ways to identify close approaches and evaluate possible avoidance actions. This paper presents AstroShield, a software prototype that combines orbital-element acquisition, local data caching, SGP4 propagation, conjunction screening, machine-learning-based maneuver proposal, numerical verification, persistent history, and a three-dimensional web interface. A Random Forest proposes maneuver parameters from encounter features; an independent physics stage then propagates the modified trajectory and checks whether the resulting closest approach satisfies a configured separation threshold. The model therefore nominates options but does not determine safety by itself. The implementation includes candidate burn directions, discrete velocity-change magnitudes and execution lead times, with a fallback search when the initial proposal is not verified. A representative software demonstration begins with a critical conjunction displayed at approximately 7.57 km. The interface proposes a 1.00 m/s prograde maneuver 60 minutes before closest approach; the numerical check reports approximately 324.4 km post-maneuver separation against a configured minimum of 25 km. These values describe one demonstration session and are not statistical evidence of operational collision-avoidance performance. The paper describes the architecture, implementation, decision logic, evaluation plan, and limitations. AstroShield is intended as human-supervised decision support and is not a certified flight-safety system.