Study level

PhD

Master of Philosophy

Honours

Faculty/School

Science and Engineering Faculty

School of Electrical Engineering and Robotics

Topic status

We're looking for students to study this topic.

Supervisors

Dr Aaron Mcfadyen
Position
Lecturer
Division / Faculty
Science and Engineering Faculty

Overview

Unmanned Traffic Management (UTM) describes a set of systems, services and procedures that will be developed to manage drone (unmanned aircraft systems/unmanned aerial vehicle/remotely piloted aircraft system) operations in and around our cities, including package delivery and inspection tasks and passenger transport.

Essentially, UTM is a new air traffic control system for drones with high levels of automation and advanced features. We are developing powerful and scalable technologies that allow thousands of drones to operate safely in our skies.

Research activities

This project has multiple research threads related to Unmanned Traffic Management (UTM) and Urban Air Mobility (UAM). Each research thread can be undertaken at an honours, masters or PhD level on their own.

Autonomous control of drones

  • Investigate robust and stable control algorithms that enable multiple drones to coordinate their motion for formation flight, collision avoidance, platooning, optimised surveillance and flight along intersecting routes.
  • Automate scheduling/sequencing of drone transit through an aerial highway network, at arrival and departure nodes or hubs, and dynamic re-routing including priority assignments.

Shaping low-level airspace and traffic networks

  • Investigate manned and unmanned traffic modelling approaches for collision probability or risk analysis to aid airspace design and characterisation, automated flight approval, separation standard development and tactical mitigation performance metrics.
  • Investigate unmanned traffic design concepts (free flight, aerial highways, networks, etc) subject to economic, social and technical constraints.

Air traffic configuration modelling and prediction

  • Derive novel representations of air traffic movement, patterns and configurations using machine learning, Markov Chains or other methods.
  • Investigate the predictive power of the models for use in automated or semi-automated dynamic airspace allocation tasks (airspace routes or volumes).
  • Investigate conformance monitoring.

Outcomes

Project outcomes are highly relevant to the emerging UTM sector and are positioned to inject new technologies and insights to help shape and safely manage our future airspace.

Depending on your topic choice, it is likely that you will develop fundamental technologies and approaches that extend well beyond UTM and contribute to our scientific knowledge. For example, you may derive new robust methods to control multiple platforms, or uncover new collision risk models that can be deployed across any modern transport system involving connected agents.

QUT research and development in UTM has led to multiple commercial research projects, PhD student placements with industry and multiple academic publications including best paper awards.

Skills and experience

We recommend you have skills in:

  • programming:
    • MATLAB and/or Python
    • associated visualisation tools (Google Earth, Cesium QGIS etc)
  • maths and science:
    • discrete-time systems
    • dynamic system modelling
    • Markov models
    • data analysis
    • applied statistics
    • machine learning
    • control system design.

Scholarships

You may be able to apply for a research scholarship in our annual scholarship round.

Annual scholarship round

Keywords

Contact

Contact the supervisor for more information.