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What is it about?

Urban air mobility (UAM) using electrical vertical take-off and landing (eVTOL) aircraft is an emerging way of air transportation within metropolitan areas. A key challenge for the success of UAM is how to manage large-scale flight operations with safety guarantee in high-density, dynamic and uncertain airspace environments in real time. To deal with these challenges, in this paper we combine the concept of geofence and chance constraints to obtain chance-constrained geofences under data-driven uncertainties, which can guarantee that the probability of potential conflicts between eVTOL aircraft is bounded given a general empirical distribution. To evaluate the chance-constrained geofences in an online fashion, Kernel Density Estimation (KDE) based on Fast Fourier Transform (FFT) is adopted and customized to model data-driven uncertainties. Comprehensive numerical simulations demonstrate the feasibility and efficiency of the online evaluation of chance-constrained geofence through the algorithm of FFT-based KDE.

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Why is it important?

The work is dedicated to bounding the location uncertainty of aircraft in an online fashion. The proposed algorithm runs efficiently to make sure the aircraft fly safely.