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Multi-agent reinforcement learning satellite guidance for triangulation of a moving object in a relative orbit frame
- Source :
- JDMS: The Journal of Defense Modeling and Simulation; 20240101, Issue: Preprints
- Publication Year :
- 2024
-
Abstract
- Multi-agent systems are of ever-increasing importance in a contested space environment—use of multiple, cooperative satellites potentially increases positive mission 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 multi-agent satellite swarm Guidance, Navigation, and Control (GNC) using deep reinforcement learning (DRL). DRL policies 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 maneuvering to triangulate an object that is non-stationary in the relative orbit frame. Reward shaping is used to encourage learning guidance that positions swarm members to maximize triangulation accuracy, using angles-only observations for navigation relative to the target. Results show the policies successfully learn guidance through reward shaping to improve triangulation accuracy by a significant factor.
Details
- Language :
- English
- ISSN :
- 15485129 and 1557380X
- Issue :
- Preprints
- Database :
- Supplemental Index
- Journal :
- JDMS: The Journal of Defense Modeling and Simulation
- Publication Type :
- Periodical
- Accession number :
- ejs65757566
- Full Text :
- https://doi.org/10.1177/15485129231197437