Abstract—In the coming decades, drones are expected to operate within urban areas at high volumes, and if implemented suc- cessfully, applications such as infrastructure inspection, medical supply and parcel delivery can be improved by the technology. This poses a challenge: how are these drones to be guided in this highly-constrained airspace? Many existing projects have approached the problem from different angles: some place more importance on the Tactical Layer and thus resolving conflicts in flight, while other research focuses on the Strategic Layer with scheduling or airspace design. While analysis is done on a complete system, with all separation management layers implemented, work remains to be done regarding quantifying how these layers interact, and what positive characteristics of these interactions can be utilised to make the system more efficient, safe, and robust to uncertainties. This paper proposes a framework on which this analysis can be performed. Firstly, lay- ers are investigated independently. A feedback system is proposed, where layer outputs are measured, as is the resulting system performance. For instance, an initial hypothesis is that reducing airspace complexity in the Strategic layer, while accounting for uncertainty, will lead to better overall system performance. This can help with minimising flight times and improving overall safety. Also, manoeuvres performed by the Tactical (in-flight) layer should take this complexity metric into account. The feedback loop approach also proposes that the complexity be fed back to the central planner, and that the Strategic (Pre-Flight) layer should be able to take system status into account when performing planning. @en

U-Space drone operations are expected to be a driver for further urban development, especially through use cases such as medical and commercial parcel delivery. In particular, package delivery using small drones shows great promise, with e-commerce giants such as Amazon deploying limited-scale drone delivery trials in rural areas. As the technology matures, large-scale operations will take place in constrained urban areas, leading to high airborne traffic densities. It is necessary to develop a robust automated separation management system that actively ensures safe separation of drones both in the air and on the ground. This paper focuses on the Strategic element, more specifically on pre-departure planning. The aim of this is to reduce the chance of conflicts around vertiports, where spatial and environmental constraints make tactical resolutions difficult. This work focuses on two scenarios: a single pad for both takeoffs and landings (in a spatially constrained urban area) and 4 takeoff-landing pad pairs (for a distribution center). Several methods are compared for this takeoff sequencing task, coupled with a conflict detection algorithm: A First-Come First-Served method that applied delay to conflicting flights, a Mixed-Integer Programming approach, a Genetic Algorithm, Particle Swarm Algorithm and Simulated Annealing were used. For a single-pad approach, first-come first-served works best in terms of computation time and total deployment time (or makespan). For the multi-pad approach however, changing the flight sequence through metaheurisitic methods and mixed-integer linear programming show a reduction in total deployment time.

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