GPS-Denied Three Dimensional Leader-Follower Formation Control Using Deep Reinforcement Learning

Page view(s)
112
Checked on Jan 18, 2025
GPS-Denied Three Dimensional Leader-Follower Formation Control Using Deep Reinforcement Learning
Title:
GPS-Denied Three Dimensional Leader-Follower Formation Control Using Deep Reinforcement Learning
Journal Title:
AIAA SCITECH 2022 Forum
Keywords:
Publication Date:
29 December 2021
Citation:
Selje, R. A., Al-Radaideh, A., Dutta, R., Sun, L. (2022). GPS-Denied Three Dimensional Leader-Follower Formation Control Using Deep Reinforcement Learning. AIAA SCITECH 2022 Forum. https://doi.org/10.2514/6.2022-2237
Abstract:
In this paper, we consider a formation control problem for leader-follower unmanned aerial vehicles (UAVs) in a GPS-denied environment. The distance and the azimuth and elevation angles, defined in a local spherical coordinate frame, are used to describe the relative motion between two UAVs. A novel deep reinforcement learning (DRL) technique is leveraged to generate the required control policies that maneuver a follower UAV in a desired formation with respect to the leader. The effectiveness of the proposed DRL-based leader-follower formation is demonstrated in a simulated environment.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
ISBN:

Files uploaded: