Machine Learning to Perfect UAV Landings
Wednesday, January 11, 2017 @ 02:01 PM gHale
An unmanned aerial vehicle (UAV) to perform a perched landing using machine learning algorithms is now in development.
Development of a fixed wing aircraft that can land in a small or confined space has the potential to significantly impact intelligence-gathering and the delivery of aid in a humanitarian disaster.
By using a combination of a morphing wing UAV and machine learning can be used to generate a trajectory to perform a perched landing on the ground, said researchers with the University of Bristol and BMT Defence Services (BMT).
The UAV has been tested at altitude to validate the approach and the team are working toward a system that can perform a repeatable ground landing.
Current UAVs are somewhat restrictive in that they have fixed and rigid wings, which reduces the flexibility in how they can fly. The primary goal of the work was to look at extending the operation of current fixed wing UAVs by introducing morphing wing structures inspired by those found in birds. To control these complex wing structures, BMT utilized machine learning algorithms to learn a flight controller using inspiration from nature.
“The application of these new machine learning methods to nonlinear flight dynamics and control will allow us to create highly maneuverable and agile unmanned vehicles,” said Dr. Tom Richardson, senior lecturer in flight mechanics in the Department of Aerospace Engineering at the University of Bristol. “I am really excited about the potential safety and operational performance benefits that these new methods offer.”
The 18-month research project was a part of the Defense Science and Technology Laboratory’s (Dstl) Autonomous Systems Underpinning Research (ASUR) program.
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