PhD Position on Autonomous Separation Management for Urban Air Mobility

Urban Air Mobility (UAM) concepts, such as flying taxis and package delivery drones, are increasingly viewed as an essential component of future transportation systems. But before UAM flights can occur on a meaningful scale, several challenges need to be tackled including airspace integration. Recognizing the need to address this challenge, several initiatives are underway worldwide to develop the new Unmanned Traffic Management (UTM) services needed to facilitate UAM flights. In this context, the European Commission has initiated the European U-space UTM system. U-space development has been divided into four distinct phases named U1-U4 where the complexity of the resulting operations are gradually increased.

This PhD research project is focused on the fourth and final phase of U-space development, namely U4 full U-space services which is expected to be rolled out by the mid-2030s. In contrast to previous development phases, U4 services are expected to rely heavily on novel Artificial Intelligence (AI) and Machine Learning (ML) techniques, particularly for services related to separation management that will be essential for safely separating large numbers of UAM in urban airspaces.

Project Description

The goal of this PhD project is to develop and test autonomous separation management concepts for U4 U-space applications. The candidate will be expected to build on previous research in this domain, including but not limited to the results of the Metropolis (https://metropolis2.eu/) series of projects. Here specific focus will be placed on developing a unified approach to separation management that takes into account the strategic (e.g. airspace design and flow management), tactical (in-flight conflict resolution and flow management) and detect and avoid (last-resort collision avoidance maneuvering) safety layers at the same time and using AI/ML methods where appropriate. In addition to UAM-UAM interactions, it will also be necessary to consider the UAM-manned aircraft interactions in very low level airspace (e.g. medical helicopters). The candidate will be expected to test and validate the developed algorithms using large-scale fast-time simulations with the BlueSky open-source simulator (https://github.com/TUDelft-CNS-ATM/bluesky/wiki). It is anticipated that the results of this research will contribute towards increasing the scalability and complexity of UAM operations to the level required to achieve high density and fully autonomous UAM flights in constrained urban airspaces without the need for continuous human input for vehicular control or for traffic separation.

What expertise and skills do you bring?

• a Master’s degree in Aerospace Engineering, Software Engineering, Computer Science or an equivalent study.
• a Strong background in programming, preferably in Python and/or C++.
• Interest in AI and ML techniques (prior experience preferred but not necessary).
• The ability to work autonomously and interest in open-ended research questions.
• Good communication skills for spoken and written English. Knowledge of Dutch is desirable but not mandatory.

NLR offers you a 4-year research contract (1 + 3 years after a progress review towards the end of the first year), as well as the opportunity to build a professional network with leading experts in the aviation and drone domains.

As a PhD candidate, you will also be enrolled in the Graduate School of the Delft University of Technology (TU Delft).

You will spend part of your time at TU Delft’s faculty of Aerospace Engineering, with the section of Control and Simulation. C&S is a leading research group in the integration, development and testing of new theories on control, autonomous and cognitive systems (with and without human elements).

You will be employed at the ATM and Airports department of NLR as a PhD candidate.