Satellite Guidance with Multi-Agent Reinforce Learning for Triangulating a Moving Object in a Relative Orbit Frame

Multi-agent systems and swarms in spacecraft formation flying are of ever-increasing importance in a contested space environment-use of multiple spacecraft to contribute to a cooperative mission potentially increases positive outcomes on orbit, while autonomy becomes an ever increasing requirement to increase reaction time to dynamic situations and lower the burden on space operators. This research explores difficult swarm Guidance Navigation and Control (GNC) scenarios using Deep Reinforcement Learning (DRL). DRL polices are trained to provide guidance inputs to agents in multi-agent swarm environments for completing complex, teamwork focused objectives in geosynchronous orbit. An example scenario is explored for a group of satellite agents moving to triangulate an object in a relative orbit space that potentially maneuvers. Reward shaping is used to encourage learning guidance that positions swarm members to maximize cooperative triangulation accuracy, using angles-only sensor information for navigation relative to the target. Results show the policies successfully learn guidance through reward shaping to improve triangulation accuracy by a significant factor.