- Computer Science - Nov 20 Wind energy project receives a boost with £1.2 million award
- Physics - Nov 20 New way to write magnetic info could pave the way for hardware neural networks
- Microtechnics - Nov 14 QMUL awarded Â£1m grant to establish robotics centre for tackling nuclear waste
- Computer Science - Oct 25 London air quality monitoring improved with new project
- Earth Sciences - Oct 23 Machine learning used to predict earthquakes in a lab setting
- Microtechnics - Oct 20 UK can be a world leader in drone tech, says Imperial academic at lab launch
- Computer Science - Oct 19 BBC and UCL launch major partnership to unlock potential of data
- Computer Science - Oct 19 BBC and UK universities launch major partnership to unlock potential of data
- Computer Science - Oct 19 QMUL and BBC launch major partnership to unlock potential of data
- Microtechnics - Oct 18 Podcast: Reporting on climate change, underfloor robots and the latest Fringe
- Life Sciences - Oct 17 UofG hosts Human Brain Project
- Microtechnics - Oct 17 Liquid metal brings soft robotics a step closer
UAV performs first ever perched landing using machine learning algorithms
The very first unmanned aerial vehicle (UAV) to perform a perched landing using machine learning algorithms has been developed in partnership with the University of Bristol and BMT Defence Services (BMT). The revolutionary 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.
BMT, a subsidiary of BMT Group Ltd, and the University of Bristol have 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 a flight controller using inspiration from nature.
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 18-month research project was delivered as part of the Defence Science and Technology Laboratory’s (Dstl) Autonomous Systems Underpinning Research (ASUR) programme.
Last job offers
- Computer Science/Telecom - 15.11
Associate Professorship of Algorithms and Complexity Theory
- Computer Science/Telecom - 8.11
Assistant Professor In Computer Science (Vacancy Reference COMP18-11)
- Computer Science/Telecom - 25.10
Lecturer or Associate Professor in Computer Graphics
- Computer Science/Telecom - 24.8
Chair in Software Engineering