urban air mobility simulator

12 December 2025

With the ability to operate within urban airspace, a wide range of transfer and transportation tasks becomes possible. However, due to the close proximity to built-up and inhabited areas, necessary emergency or precautionary landings within the city may pose a potential risk to uninvolved third parties. Coupling air traffic management with the management of ground-based urban transport systems offers significant potential for risk mitigation in this context.

Within the framework of student research projects, the simulation environment developed at the chair is to be extended by various components and functionalities. These include, but are not limited to, the following aspects.

  • Preparation and processing of infrastructure and traffic data

  • Flight attitude visualization as well as assistance systems for navigation and flight guidance

  • Integration of external control and visualization environments (e.g. a 200° dome)

  • Coupling of the flight simulation environment with ground traffic management systems

  • Development of communication protocols

  • Development of an autopilot to support a potentially impaired pilot

Aktuelles

digital twin Cologne-Bonn Airport

Design and integration of new components within the developed virtual airport environment

digital cabin

Design of the experimental setup and conduct of experiments in a digital aircraft cabin

Implications of runway direction changes

This paper presents a runway-aware approach for Singapore Changi Airport that adjusts aircraft trajectories based on dynamic runway capacity, reducing arrival overloads by 81% through modest upstream time shifts without aggressive tactical intervention.

Delivery route optimization for network efficiency

This paper presents a two-stage optimization method for urban drone route networks that balances delivery efficiency, airspace deconfliction, and noise exposure, validated through a real-world medical delivery case study in Paris.

Mitigate air traffic complexity in moving sectors

Moving sectors enable flow-centric management with enhanced airspace capacity, as controller workload is dynamically allocated according to traffic conditions.

Range-angle likelihood maps for indoor positioning

We develop a ResNet-based deep learning approach for indoor aircraft cabin positioning, achieving centimeter-level localization accuracy by transforming range and angle measurements into likelihood grid maps and optimizing model hyperparameters.