The operational capability of drones is limited by their inability to perform aerial refueling. This can be overcome by automating the process with a computer vision solution. Previous work has demonstrated the feasibility of automated aerial refueling (AAR) in simulation. To progress this technique to the real world, this thesis conducts experiments using real images of a physical aircraft replica and a motion capture system for truth data. It also compares the error between the real and virtual experiments to validate the fidelity of the simulation. Results indicate that the current technique is effective on real images and that the simulation can predict errors in real world experiments.
Using Motion Capture and Augmented Reality to Test AAR with Boom Occlusion
2021
89 pages
Report
No indication
English
Aeronautics , Computers, Control & Information Theory , Air force , Aircrafts , Artificial intelligence , Augmented reality , Computational science , Computer science , Computer vision , Computers , Coordinate systems , Detection , Information systems , Mathematical models , Neural networks , Refueling in flight , Reliability , Tanker aircraft , Virtual reality
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