Cosmic AEGIS: An Intelligent Orbital Object Trajectory Prognostication and Proactive Risk Reduction Framework for Advancing Autonomous Spacecraft

Abstract

The escalating presence of orbital entities, including satellites, asteroids, and debris, highlights the pressing need for robust Space Traffic Management (STM) systems to preserve orbital integrity and sustainability. This research presents an innovative framework leveraging artificial intelligence (AI) and machine learning (ML) methodologies to enhance the accuracy of orbital object trajectory predictions and assess potential collision risks. By incorporating diverse image data sources, the framework generates probabilistic projections of object trajectories, enabling proactive risk mitigation measures. This paper focuses on the performance of object tracking-by-detection algorithm called YOLOv8. It explores a range of models and algorithms, spanning neural networks, computer vision techniques, and deep learning
architectures, for trajectory prediction and collision risk evaluation. The effectiveness of the proposed framework is evaluated through metrics. The integration of AI technology will enhance spacecraft autonomy, facilitate independent navigation and maneuver in space with reduced human intervention. Continued advancements in algorithms for collision detection, avoidance, and celestial object tracking will enhance human spaceflight initiatives and lay the groundwork for future deep space exploration endeavors.