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Design and build a hybrid tail-sitter aircraft for payload delivery and reconnaissance missions in a humanitarian aid scenario
Group Members
William Garside, Mitchell Buxton, John Pekins, Ben Smart, Suzanna Lucarotti, Matthew Wellman
Professor Keith Towell
RS Components, Flying Tech, Rapid Online, Tekever
This project is an entry to the IMechE UAS (Unmanned Aerial System) Challenge. The challenge consists of two missions to simulate a humanitarian aid scenario. The first mission is a payload drop mission where the system must drop bags of flour (simulating lifesaving supplies) on a designated target. The second mission is a reconnaissance mission where the system must use a camera to identify targets (simulating disaster survivors) on the ground.

To complete these missions effectively a tail-sitter aircraft has been designed. It has VTOL (Vertical Take-Off and Landing) capability so can be operated without a runway. Its capabilities to hover and fly wing-borne enables much greater accuracy in payload delivery than fixed-wing aircraft and much further and faster travel than conventional multirotor aircraft. The four-motor configuration gives far greater control authority in hover over the more conventional two motor alternative.

This design also changes the way autonomous UAVs make decisions. Most use a dedicated microcontroller to run the autopilot, any added intelligence needs to be run on a separate computer with a complex and often challenging integration between the two. This project uses a Linux based single board computer to handle everything on one board, allowing a much simpler integration between image recognition and autonomous navigation.

Key elements in the projects were a deep research into possible configurations, aerofoil and structural analysis. There was extensive prototyping during the manufacturing phase and software development. The functionality of the aircraft as VTOLcapable was successfully demonstrated

Tail-sitting transitioning into fixed wing flight at the Southampton Air Show
Exploded view of computational model