S&AS: INT: Traffic Deconfliction for Smart and Autonomous Unmanned Aircraft Systems in Congested Environments

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Unmanned air vehicle (UAV) operations bring considerable scientific and societal benefits with its applications ranging from package delivery, linear infrastructure inspection, to large-scale environmental monitoring. However, Autonomous UAV operation is not yet allowed by the Federal Aviation Administration (FAA) without airborne chase aircraft or ground observers to ensure traffic deconfliction, which is complicated and costly. The goal of the proposed project is to enable a given UAV to predict the trajectories of its neighboring UAVs, dynamically assess collision risks, and automatically resolve potential conflicts without the need for constant human supervision. This project seeks to: 1) implement the ability to operate a multitude of UAVs without direct human oversight in a way that might satisfy future FAA regulations; 2) establish a mathematical representation of a UAV traffic that can be scaled to handle a complex, heterogeneous, and high-dimensional traffic; and 3) mature UAV operations for civil and commercial applications in harmony with existing commercial air traffic.The project aims at developing a real-time, distributed decision-making algorithm, the Dynamic Network-based Probabilistic Route Planner (DNPRP), for autonomous operation of multiple UAVs in congested environments. The key technical components will include 1) a cloud-sourced database system for UAV data sharing, 2) an algorithm for virtual and dynamic UAV network discovery, 3) time-dependent, Bayesian nonparametric Gaussian process regression-based trajectory prediction models, and 4) a time-dependent collision probability map for conflict-free route generation. For physical flight testing, DNPRP will be evaluated by using a couple of multirotor UAVs that operate within an enclosed outdoor facility (i.e., the Virginia Tech Drone Park) for hours to days without human intervention. Trade-offs between computational cost and solution optimality in real-time operation will be assessed rigorously with measurable metrics, which will inform possible extensions to large-scale complicated systems. A rapid path for transitioning the research results to open-source or commercial software will be formed in close collaboration with an industrial partner.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.