We investigate strategies for management of conflicts among autonomous unmanned aerial vehicles (UAVs) in high-density very low level (VLL) uncontrolled airspace. We consider deconfliction procedures which do not involve horizontal maneuvers, and study two types of airspace structure: single- and multi-layered airspace. We compare different deconfliction paradigms by simulating a busy day of operations over a geographical area. Our main contributions are a proposal of new deconfliction schemes for UAVs and an assessment of the single- and multi-layered airspace designs. Our work aims to provide regulators and policy-makers with a framework for choosing between resolution strategies for the airspace management.

We study separating urban unmanned aerial vehicles (UAV) traffic into altitude levels, using a PBN-inspired approach in which low-density airspace has few layers while congested areas in the city center are split into a larger number of layers. Navigating in the many-layers environment may require better vehicle equipage to support higher performance in terms of altimetry precision; our work thus follows the stakeholders encouragements to use performance-based navigation (PBN) in UAV traffic management (UTM). We present results for several traffic volume scenarios over Norrköping municipality in Sweden, demonstrating applicability of our solutions in a city setting.

We give algorithms for splitting a geographical region into ”approximately flat” rectangular zones. Each zone is assigned a feasible flight altitude, providing simple flight level guidance for future operations of unmanned aerial systems in low-level airspace. We consider a rural scenario with uneven ground level and an urban setting featuring many tall structures. In both cases, our solutions adapt to the underlying terrain or city landscape. In the rural scenario, operating on a fixed altitude within a rectangle allows the drone to stay within the upper limit of 120m while not flying too close to the ground; our objective is to minimize the complexity of the airspace. In the urban case, we aim at minimizing the volume of airspace reserved for drone operations, while allowing overflight over tall buildings in the city. Experiments with real landscape and city skyline data demonstrate output of our solutions with various input parameters.

This paper explores tradeoffs between ground impact and efficiency of drone flights in urban scenarios. We give an algorithm which produces a set of routes with different lengths and varying number of people affected by the drone. We also present an interactive online visualization tool allowing the user to modify flightpaths in order to explore routing options. Our path finder and the GUI are implemented for a metropolitan area of Norrköping municipality in Sweden. The methods studied in this paper may give UTM service provider the tools to negotiate flightplans which will be acceptable by both the regulator and the drone operator.

We extend the classical SIR epidemic spread model by introducing the "quarantined" compartment. We solve (numerically) the differential equations that govern the extended model and quantify how quarantining "flattens the curve" for the proportion of infected population over time. Furthermore, we explore the potential of using drones to deliver tests, enabling mass-testing for the infection; we give a method to estimate the drone fleet needed to deliver the tests in a metropolitan area. Application of our models to COVID-19 spread in Sweden shows how the proposed methods could substantially decrease the peak number of infected people, almost without increasing the duration of the epidemic.