CERES
Library Services
  • Communities & Collections
  • Browse CERES
  • Library Staff Log In
    Have you forgotten your password?
  1. Home
  2. Browse by Author

Browsing by Author "Tamanakijprasart, Komsun"

Now showing 1 - 1 of 1
Results Per Page
Sort Options
  • Loading...
    Thumbnail Image
    ItemOpen Access
    Autonomous path selection of unmanned aerial vehicle in dynamic environment using reinforcement learning
    (AIAA, 2025-01-06) Tamanakijprasart, Komsun; Perrusquia, Adolfo; Mondal, Sabyasachi; Tsourdos, Antonios
    The Unmanned Aerial Vehicle (UAV) is an emerging area within the aviation industry. Currently, fully autonomous UAV operations in real-world scenarios are rare due to low technology readiness and a lack of trust. However, Artificial Intelligence (AI) offers powerful tools to adapt to changing conditions and handle complex perceptions. In autonomous vehicles, automotive self-driving technologies have made significant advances. To enhance the level of autonomy in aviation, it is beneficial to analyze these frameworks and extend autonomous driving principles to autonomous flying. This research introduces a novel solution for ensuring safe navigation in UAVs by adopting the concept of autonomous lane or path selection strategies used in cars. The approach employs deep reinforcement learning (DRL) for high-level decision-making in selecting the appropriate path generated by various established algorithms that consider different scenarios. Specifically, the Interfered Fluid Dynamical System (IFDS) \cite{IFDS_OG} is utilized for guidance and the PID for the flight control system. The UAV can choose between global and local paths and determine the appropriate speed for following these paths. This proposed framework lays the foundation for future research into practical and safe navigation strategies for UAVs.

Quick Links

  • About our Libraries
  • Cranfield Research Support
  • Cranfield University

Useful Links

  • Accessibility Statement
  • CERES Takedown Policy

Contacts-TwitterFacebookInstagramBlogs

Cranfield Campus
Cranfield, MK43 0AL
United Kingdom
T: +44 (0) 1234 750111
  • Cranfield University at Shrivenham
  • Shrivenham, SN6 8LA
  • United Kingdom
  • Email us: researchsupport@cranfield.ac.uk for REF Compliance or Open Access queries

Cranfield University copyright © 2002-2025
Cookie settings | Privacy policy | End User Agreement | Send Feedback