Researchers at the University of Surrey are developing fixed-wing unmanned aerial vehicles (UAVs) capable of agile manoeuvres such as perching and obstacle avoidance, paving the way for smarter, energy-efficient aerial systems.
Overcoming the Limitations of Conventional Drones
Traditional rotary-wing drones are highly manoeuvrable but energy-intensive, while fixed-wing drones are efficient but lack agility. The Surrey team’s project, Learning2Fly, combines the strengths of both approaches by mimicking the flight behaviours of owls and other precision flyers.
Lessons from Nature: Birds of Prey as Models
Birds of prey perform complex manoeuvres in tight and turbulent environments. By studying their flight dynamics, engineers are designing drones that can operate safely in crowded cities and windy offshore conditions, delivering packages or inspecting wind turbines with precision.
AI and Machine Learning for Real-Time Flight Control
Instead of relying solely on computational fluid dynamics, the project uses real-world flight data and machine learning to predict and control drone behaviour in real time. Lightweight prototypes, some adapted from toy planes, are tested in Surrey’s motion capture lab with high-speed cameras and onboard sensors.
Toward Deployment and Real-World Testing
With further outdoor trials planned, the project aims to demonstrate how bird-inspired drones can combine energy efficiency with agility, unlocking applications in urban logistics, renewable energy, and beyond. As researcher Owen Wastell notes, “In an era of advanced technology, we’re still looking to the natural world for inspiration.”
