AI-driven collision risk prediction and debris optimization in low earth orbit

Abstract

Debris congestion and collision in low Earth orbit have become a major point of concern due to the rapid expansion of activities in that region. In this work, an AI-driven framework is developed and deployed to predict, analyze, and optimize collision risk across the altitude inclination space. Local debris object population, mass density distribution, and conjunction derived risk are computed. Then, an AI surrogate model, viz., gradient-boosted decision trees, was trained to predict collision conjunction relative probabilities and risk. Thereafter, data analysis and explain ability techniques are utilized to uncover underlying risk drivers and reveal how altitude, inclination, mass loading, and crowding interact across the orbital space. The analyses showed how the AI surrogate model reliably captures underlying nonlinear risk behavior and interactions. More importantly, it suggested that meaningful capacity increases are achievable with only a moderate rise in predicted risk. Finally, the AI surrogate model was employed in a multi-objective evolutionary optimization to quantify the trade off between minimizing collision risk and maximizing usable orbital capacity, revealing a set of optimal, feasible, and well-balanced points of operation in this regime. Ultimately, the proposed pipeline offers a computationally efficient pathway for prediction and interpreting orbital safety, guiding constellation deployment, and supporting sustainable space traffic management strategies.