AstroShield: A Physics-Verified Learning Framework for Explainable Spacecraft Collision Avoidance
Year: 2026
Type: article
Abstract: 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.
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225103433