UAV performs perched landing using machine learning
12 January 2017
The very first unmanned aerial vehicle (UAV) to perform a perched landing using machine learning algorithms has been developed.
Developed in partnership with the University of Bristol and BMT Defence Services (BMT), the fixed wing aircraft has the potential to impact intelligence-gathering and delivery of aid missions.
Current UAV’s have fixed and rigid wings which restricts their movements. By introducing morphing wing structures inspired by birds, the aircraft’s movements can be controlled. To control the complex wings, BMT utilised machine learning algorithms to learn a flight controller. The combination of a morphing wing UAV and machine learning can be used to generate a trajectory to perform a perched landing on the ground.
Simon Luck, Head of Information Services and Information Assurance at BMT Defence Services, commented: “Innovation is at the heart of everything we do at BMT and R&D projects provide us with the opportunity to work with our partners to develop cutting edge capabilities that have the potential to revolutionise the way we gather information.”
Dr Tom Richardson, Senior Lecturer in Flight Mechanics in the Department of Aerospace Engineering at the University of Bristol, added: “The application of these new machine learning methods to nonlinear flight dynamics and control will allow us to create highly manoeuvrable and agile unmanned vehicles. I am really excited about the potential safety and operational performance benefits that these new methods offer.”
The UAV has been tested at altitude to validate the approach and the team are working towards a system that can perform a repeatable ground landing.
The 18-month research project was delivered as part of the Defence Science and Technology Laboratory’s (Dstl) Autonomous Systems Underpinning Research (ASUR) program.
Video courtesy of BMT Defence.