AI BASED COLLISION AVOIDANCE SYSTEM

Abstract— The sharp rise in the number of active satellites and debris has increased the probability of satellite collision, which is a major threat to the success of space operations. This project proposes the development of an AI-based satellite collision avoidance system that can accurately predict the possibility of satellite collision using machine learning and neural networks. The Two-Line Element (TLE) data set is used to simulate the satellite orbit using standard orbital mechanics equations. For this project we have used different predictive models to suit the needs of the different types of satellite orbits, such as Low Earth Orbit (LEO), Geostationary Earth Orbit(GEO) and finally the Polar Orbits. This is done to factor for the different orbital characteristics of the satellites. The project proposes the development of frontend using opensource library like Cesium JS, which will enable us to visualize the satellites and the avoidance feature in 3D. This system will help in increasing the situational awareness and decision-making process of the satellite operators, furthermore this will also enable students and like to play around with the system.