Problem Utilizing Deep Reinforcement Learning to Effect Autonomous Results The growth in space-capable entities has caused a rapid rise in Orbit Transfers and Intercepts via Electromagnetic Propulsion ➢ Analysis the number of derelict satellites and space debris in orbit Gabriel Sutherland (
[email protected]), Oregon State University, Corvallis, OR, USA ❖ The data suggests spacecraft highly capable of around Earth, which pose a significant navigation hazard. Frank Soboczenski (
[email protected]), King’s College London, London, UK neutralizing/capturing small to intermediate debris Athanasios Vlontzos (
[email protected]), Imperial College London, London, UK ❖ In simulations, spacecraft was able to capture and/or Objectives neutralize debris ranging in mass from 1g to 100g ❖ Satellites suffered damage in some Develop a system that is capable of autonomously neutralizing simulations multiple pieces of space debris in various orbits. Software & Simulations ❖ Simulations show that damaged segments of satellite were vaporized, which means Background that no additional debris was added to orbit ➢ Dangerous amounts of space debris in orbit, estimates vary ❖ Neural Network based on Deep Deterministic Policy ➢20,000+ tracked objects larger Gradient (DDPG) effective at low-thrust orbit transfers in than 10 cm diameter 2D Hohmann transfer problem ➢Est 500,000 objects larger ❖ Using ASTOS simulation software, mission-representative than 1 cm in diameter model of spacecraft and experimental propulsion system ➢Est 100 million objects ❖ Interfaced with DRL to train the Neural smaller than 1cm in diameter ➢ Orbital speeds of these objects Network with realistic data vary from 100+ kph to 28,100 kph ➢ Future Studies ➢ Spacecraft collisions due to space ❖ Train DDPG for 3 dimensional orbit transfer problems debris have been sparse so far Tracked spacecraft in Earth orbit.