Air transportation systems have the potential to be revolutionized worldwide by drone technologies (unmanned aircraft) that could bring huge commercial (trillion dollar), economic and social (improved security, emergency and medical services, package delivery etc.) benefits. To unlock these benefits, drones need fast, regular and safe access to urban airspace. Currently, access is restricted due to mid-air collision risk concerns and the absence of unmanned traffic management (UTM) systems to manage these risks.
New data driven modelling and simulation techniques are crucial to enhancing our understanding of the collision risk associated with drone operations, and provide the key enabling capability to underpin drone traffic management systems. This research will derive such models and investigate their use in real automated drone traffic management applications. Example applications include low-altitude airspace design, automated flight approval, routing and scheduling through to separation standard development and counter-drone surveillance.
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