UAV performs perched landing using algorithms

The design combines a morphing wing with machine learning and can improve intelligence gathering and delivery of aid in humanitarian disasters.

The University of Bristol has developed an unmanned aerial vehicle (UAV) that performed a perched landing using machine learning algorithms, in partnership with BMT Defence Services.

The 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.

The university has demonstrated how 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. 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.

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 utilised machine learning algorithms to learn to develop flight control using inspiration from nature.

Dr Tom Richardson, senior lecturer in flight mechanics in the department of aerospace engineering at the university, said: "The application of these machine learning methods to nonlinear flight dynamics and control will allow us to create highly maneuverable and agile unmanned vehicles. I am really excited about the potential safety and operational performance benefits that these methods offer."

The 18-month research project is a part of the Defence Science and Technology Laboratory's Autonomous Systems Underpinning Research programme.