Performance Based Determination of Detect-and-Avoid Ranges in a Constrained Airspace

Abstract

Urban air mobility concepts and unmanned aircraft have the potential to increase the number of unmanned aircraft sharing the same airspace. An effective method, among others, to reduce the probability of conflicts or even collisions is to constrain the velocity and direction of vehicles in close proximity. Effectively, reducing the relative velocities of nearby vehicles makes conflicts more unlikely and easier to solve. This approach is well known from automobile highways, and it forms the basis of concepts like air corridors, air tubes, and, in general, Geovectoring. By constraining and synchronizing velocity and direction the likelihood of a collision is reduced even at higher absolute velocities. Nevertheless, Geovectoring may still require a detect-and-avoid (DAA) solution to become aware of potential conflicts by monitoring the distances and velocities of surrounding traffic. In a previous publication a method for calculating the minimal required detection ranges for several constrained airspaces have already been developed. The method is based on parallel simulations considering the individual performance parameters of involved vehicles. It was shown that constraining the direction of movement in an air corridor for drones is most effective in terms of reducing the required minimal range of a DAA solution. Constraining the magnitude of velocity reduces the required range further, depending on the chosen method of avoid. In this paper the calculations are extended to a number of drone types that can be expected to be found in a future urban delivery network. Three different types of avoid solutions are compared, namely horizontal avoid, vertical avoid and speed change. It is shown that although drone performances may differ the resulting DAA ranges share similar characteristics allowing for a common DAA solution for all types. The results of these calculations have been used in the European project USEPE to strategically determine the capacity of given airspace sectors as well as implement a simulated tactical DAA solution. Further, examples of the resulting tables are presented that can be used to implement a state-based DAA solution as well as to determine requirements for strategical and tactical separation services.