Closed-Loop Satellite Collision Avoidance: A Benchmark and Planner-Guided Learning Framework

Satellite collision avoidance (SCA) is a closed-loop sequential decision problem: maneuvers must reduce conjunction risk, limit delivered velocity increment, and recover the spacecraft toward its designated orbit. Existing studies use incompatible assumptions that obscure this trade-off. We introduce SCA-Bench, combining catalog-derived states and historical encounter geometries with finite-burn control, online re-screening, recovery, and single- and multi-object scenarios. Decoupled warning and execution models include measured prediction-execution discrepancy in warning covariance. We also present SCA-Planner, which behavior-clones offline finite-burn planner trajectories before warning-gated proximal policy optimization. On a fixed 128-scenario test set, OrbitZoo obtains the highest collision-probability compliance (99.21%), whereas SCA-Planner uses less cumulative maneuver effort (0.1850 vs. 0.4300 m/s), recovers closer to the designated orbit (0.5800 vs. 0.4600), and attains a higher composite score. Ablations expose distinct operating points; no method dominates safety, maneuver effort, and recovery.