Aerospace Autonomy

Why it matters

The airspace environment is rapidly changing as commercial and general aviation traffic is increasing and new users (including unmanned aircraft) desire greater and less restricted access to the airspace. New processes and autonomous systems will need to be developed to safely and efficiently manage air traffic and allow higher levels of autonomy for greater capability and efficiency of operations.

Project overview

QUT currently has projects on airspace integration technology (detect and avoid, unmanned traffic management, and air traffic analysis), single and multiple UAV navigation and target detection in GPS /GNSS denied environments,  platform autonomy (large scale flight planning, fault detection, sensor coverage, and aerial manipulation), cognitive onboard decision making and technologies for multi-UAS integration (decision making, fault tolerance, and human factors).

Real-world impact

QUT provides access to world-leading aerospace autonomy research capabilities in areas of subsystem analysis, design and build, optimisation and data-driven air-traffic analysis. Our expertise and experience in working with the aerospace industry offers capabilities in:

  • vision-based detect and avoid, control, navigation and situational awareness
  • single and multiple UAV navigation in GPS/GNSS denied environments
  • multi-sensor navigation, fusion, estimation, fault detection and integrity monitoring
  • single and multi-vehicle mission planning, scheduling, on-board decision making and human factors
  • data-driven air traffic modelling and analysis, and autonomy assessment and qualification.
    Example of how data-driven models and live traffic feeds may be combined to improve situational awareness in shared airspace. Low-level (below 500 feet) air traffic density (coloured patches on the ground) is shown along with some example traffic tubes or flows (blue tubes). Live traffic (green and orange paths) is shown and labelled as well. Notice how the live traffic aligns to the expected tubes and density patterns (essentially verifying the models).
    Example of the dominant air traffic flows or tubes around Brisbane city. These models are generated automatically form primary radar data. Over 85% of the air traffic resides inside the tubes. The tube width denotes two standard deviation about the mean for each flow.


























These projects are funded by the Queensland State Government, the Australian Research Council, Cooperative Research Centres and industry partners. We welcome industry collaborations in this field. Contact us for more information about collaborating with our team.