Dr Timothy Molloy
This person does not currently hold a position at QUT.
Biography
I am a Research Associate at QUT on industry-funded and ARC projects to investigate enabling signal processing and control technologies for autonomous mid-air collision avoidance and vision-based detect and avoid (DAA). I am the recipient of an Advance Queensland Early-Career Research Fellowship Safely Guiding Unmanned Aircraft Through Queensland Skies to develop novel control and guidance strategies for unmanned aircraft in potential collision scenarios. This fellowship is supported by Boeing Research and Technology Australia, QUT, and the Queensland State Government. More broadly, my research interests span the fields of control engineering, signal processing, information theory, and robotics. I have particular interest in the problems of robust detection and estimation, inverse optimal control, and inverse dynamic and differential game theory.
I received the B.E. (Aero Av) and Ph.D. degrees from QUT in 2010 and 2015, respectively. I was awarded a QUT University Medal in 2010 for exceptional academic performance in B.E. (Aero Av), and a QUT Outstanding Doctoral Thesis Award in 2016 for outstanding contribution to the field of study and excellence in higher degree research practice.
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
Discipline
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)
Selected publications
- Molloy T, Ford J, Perez T, (2018) Finite-horizon inverse optimal control for discrete-time nonlinear systems, Automatica, 87, pp. 442-446.
- James J, Ford J, Molloy T, (2018) Learning to detect aircraft for long range, vision-based sense and avoid systems, IEEE Robotics and Automation Letters, 3 (4), pp. 4383-4390.
- Molloy T, Ford J, (2019) Minimax robust quickest change detection in systems and signals with unknown transients, IEEE Transactions on Automatic Control, 64 (7), pp. 2976-2982.
- 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), pp. 754-759.
- 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), pp. 1378-1391.
QUT ePrints
For more publications by Timothy, explore their research in QUT ePrints (our digital repository).