Uas conflict resolution in continuous action space using deep reinforcement learning

Jueming Hu, Xuxi Yang, Weichang Wang, Peng Wei, Lei Ying, Yongming Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations


Ensuring safety and providing obstacle conflict alerts to small unmanned aircraft is vital to their integration into civil airspace. There are many techniques for real-time robust drone guidance, but many of them need expensive computation time or large memory requirements, which is not applicable to deploy onboard of an aircraft with limited computation resources. To provide a safe and efficient computational guidance of operations for unmanned aircraft, we provide a framework using deep reinforcement learning algorithm to guide autonomous UAS to their destinations while avoiding the static and moving obstacles through continuous control. After offline training, the model only requires less than 100KB of memory, and the online computation for the conflict resolution advisory only takes 2ms. For the algorithm verification and validation, an airspace simulator is built in Python and numerical experiments show that the trained model can provide accurate and robust guidance with the environment uncertainty.

Original languageEnglish (US)
Title of host publicationAIAA AVIATION 2020 FORUM
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105982
StatePublished - 2020
EventAIAA AVIATION 2020 FORUM - Virtual, Online
Duration: Jun 15 2020Jun 19 2020

Publication series

Volume1 PartF


CityVirtual, Online

ASJC Scopus subject areas

  • Nuclear Energy and Engineering
  • Aerospace Engineering
  • Energy Engineering and Power Technology


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