Dr Timothy Molloy
This person does not currently hold a position at QUT.
Personal details
Keywords
Differential and Dynamic Game Theory, Optimal Control Theory, Inverse Optimal Control and Dynamic Games, Unmanned Aircraft Systems (UAS), Vision-Based Detect and Avoid, Autonomous Mid-Air Collision Avoidance, Statistical Signal Processing, Quickest Detection
Research field
Artificial Intelligence and Image Processing, Electrical and Electronic Engineering
Field of Research code, Australian and New Zealand Standard Research Classification (ANZSRC), 2008
Qualifications
- PhD (Queensland University of Technology)
- Bachelor of Engineering (Aerospace Avionics) (Queensland University of Technology)
Publications
- Molloy, T., Ford, J. & Perez, T. (2018). Finite-horizon inverse optimal control for discrete-time nonlinear systems. Automatica, 87, 442–446. https://eprints.qut.edu.au/223233
- Molloy, T., James, J. & Ford, J. (2018). Learning to detect aircraft for long range, vision-based sense and avoid systems. IEEE Robotics and Automation Letters, 3(4), 4383–4390. https://eprints.qut.edu.au/120987
- Molloy, T. & Ford, J. (2019). Minimax robust quickest change detection in systems and signals with unknown transients. IEEE Transactions on Automatic Control, 64(7), 2976–2982. https://eprints.qut.edu.au/121741
- Molloy, T., Garden, G., Perez, T., Schiffner, I., Karmaker, D. & Srinivasan, M. (2018). An inverse differential game approach to modelling bird mid-air collision avoidance behaviours. Proceedings of the 18th IFAC Symposium on System Identification, SYSID 2018 (IFAC-PapersOnLine, Volume 51, Issue 15), 754–759. https://eprints.qut.edu.au/118485
- Molloy, T., Ford, J. & Mejias Alvarez, L. (2017). Detection of aircraft below the horizon for vision-based detect and avoid in unmanned aircraft systems. Journal of Field Robotics, 34(7), 1378–1391. https://eprints.qut.edu.au/105590
QUT ePrints
For more publications by Timothy, explore their research in QUT ePrints (our digital repository).