In the first track, 11 lesser black backed gulls were tagged with GPS backpacks and their flight paths recorded for four breeding seasons. Computer models of the wind flows encountered by the birds were also developed. This was the first high resolution dataset of its kind, measuring bird flight behaviour in an urban environment and showed that the birds perform a range of energy saving behaviours such as thermalling, and using orographic lift, and that they adjust their flight patterns to suit local weather conditions in a highly efficient manner. We discovered that gulls systematically change how they use the wind flows around buildings in relation to wind speed and turbulence and that these changes minimized the flight control cost for simulated UAV flight under the same conditions. We then tested the optimality of bird flight behaviours using flight dynamics modelling and path planning approaches. We discovered that by responding appropriately to local wind conditions birds were potentially reducing their flight costs by up to 50% and that a path planning approach for UAVs using the same strategy could potentially achieve the same level of energy saving.
In the second track we investigated the mechanics of how birds cope with gusty wind conditions. We did this by measuring the wing shape and movements of birds of prey as they flew through a controlled gust using an array of high-speed video cameras. We found that the birds wings rotated rapidly at their shoulder when the load on them increased due to the gust, acting as a passive suspension system. This fast initial response was enhanced by the geometry and mass distribution of the wing. Based on our measurements of birds’ gust rejection we have patented a concept for gust rejection for aircraft. We have also looked at the aerodynamics of bird flight using flow imaging techniques with free-flying birds and wind tunnel models. This revealed how the aerodynamics of the wings and tail are closely coupled and act in concert much more than is the case for conventional aircraft. This has suggested ways of improving the flight performance of small UAVs.
In the third track a series of distributed force and flow sensing systems for a small-scale fixed wing UAVs were developed. Each iteration featuring more advanced sensing capabilities. These systems were characterised using wind tunnel and free-flight testing. We found that distributed force and flow sensing offers advantages beyond conventional sensors, such as enabling the detection of stall and the measurement of the load and flow patterns over the wing. When used as part of a conventional flight control system we found that the distributed sensor data could be used to robustly estimate parameters not available with conventional sensors. We then developed a series of machine learning based flight controllers which demonstrated the potential of machine learning combined with the rich sensor data from distributed array. Overall, this demonstrated the potential for force and flow sensing to enhance the performance of UAVs by allowing them to operate at the edge of the flight envelop and the potential for this bird-inspired approach to enable enhanced flight control in future flexible and morphing wing aircraft.
These results have been disseminated through 23 scientific publications and over 50 scientific conferences and workshops, as well a range of public events. These outreach events included exhibiting at Royal Society Summer Science 2021 where we shared our research online using a website, videos, online game, and augmented reality app receiving >20,000 views and reaching 900 school students through online workshops. We also ran in-person hands-on workshops with 12 schools on “Bioinspired Engineering: From Gulls to Drones” where the students got to fly their own bio-inspired drones. Finally, we have presented our work at a range of aerospace industry events and are currently exploring commercialization of the technologies developed in this project.
