Air Route Design for Unmanned Aerial System Traffic Management in Urban Environment
- Lishuai LI (Principal Investigator / Project Coordinator)School of Data Science
- Wei LIU (Co-Investigator)
DescriptionWith drone technologies maturing, unmanned aircraft system (UAS)-based commercial services are rapidly emerging, such as small package delivery services via drones. Such service offers a range of benefits, e.g. enabling unmanned timely deliveries, connecting remote areas, and reduced road congestion and pollution. However, a key challenge lies in how to scale it up, how tomanage a large number of drones’ operations in high-density urban areas safely and efficiently.Among different Concepts of Operations (ConOps) that have been proposed for the traffic management of the UAS operations, structured air route-based operations are employed in a few regions and are anticipated to continue serving scenarios with dense and complex airspace. To enable this kind of operations in scale, a key research problem is how to design these air routes toform a network that best supports UAS operations using limited airspace in urban environment while considering all safety, privacy and operational constraints. This is essentially a form of multipath planning with constraints problem, which has not been well addressed due to the problem size (3D urban space) and the problem complexity (multi air routes with couplingconflicts and spatiotemporal uncertainties) in this application.In this project, we aim to leverage on the theoretical frameworks of network flows and the computational power of recent artificial intelligence (AI) techniques to tackle this challenge. We plan to model the air route design problem using network flows theories. Then we will develop a set of algorithms to solve the network flows problems and design static and dynamic air-routenetworks for UAS traffic management (UTM). Also, we will derive analytical properties of the problems and the developed algorithms, e.g. problem size, problem complexity, computational complexity, algorithm optimality, etc. Lastly, the solutions will be tested in simulated scenarios and real-world environment and compared to other state-of-the-art methods.
|Effective start/end date||1/01/24 → …|