I am a Principal Research Fellow (Autonomous Systems) at the Queensland University of Technology's (QUT) Institute for Future Environments, and an Associate Investigator in the Australian Centre for Robotic Vision. I am known for my research into field (environmental) robotics and their application to large-scale environmental monitoring, management and change quantification, particularly in marine and aquatic systems.
I received a B.Eng in Aerospace Engineering from the Royal Melbourne Institute of Technology and a PhD from QUT. I started my professional career in 1995 as a project engineer at Roaduser Research International. Following my PhD I joined the CSIRO Autonomous Systems Laboratory in 2001. As a Principal Research Scientist at CSIRO I held various roles including project leader and the Robotics Systems and Marine Robotics team leader before moving to QUT in 2013.
I have wide research interests including adaptive sampling and path planning, vision-based navigation, cooperative robotics, visual and acoustic stealth, as well as robot and sensor network interactions. A detailed bio is on my QUT profile.
I undertake a range of robotics research particularly focused around adaptive sampling, associative learning, and image-based habitat mapping and change quantification. This fundamental research is typically applied to help solve challenging environmental science problems. Below is a summary of current projects.
Visual and Acoustic Stealth
Tracking dynamic targets without being detected requires not only visual but also acoustic stealth. Our goal is to significantly extend both these concepts by uniquely combining visual and acoustic stealth to maintain continuous line-of-sight observation to a moving natural object of interest, such as wild animals, in outdoor environments without being detected. We have demonstrated the combined acoustic and visual stealth approach for covertly tracking a moving target and more recently extended this for the robot to recognise and use shadows as more discreet vantage points (paper). This work is in collaboration with Ashley Tews from CSIRO.
Large-scale aquatic greenhouse gas quantification
This project is developing novel techniques for the large-scale temporal quantification of greenhouse gases (particularly methane) from inland waterways. It is uniquely combining persistent robotic platforms, image-processing, sensor networks, and automated sensors. The techniques and sampling paradigms developed in this project are providing limnologists and ecologists the ability to accurately quantify methane flux rates, improving model development and fundamental process understanding.
Inference: Robotic adaptive sampling
This project is creating and demonstrating new scalable adaptive sampling capabilities to enable large-scale monitoring of the environment, including dynamic and extreme events (e.g. floods, cyclones, fires) using multiple, persistent robotic sensors. To facilitate algorithm development, a novel persistent robotic system has been developed called Inference. The system consists of multiple networked robotic boats which provides an open architecture allowing researchers to evaluate new sampling algorithms on real-world processes over extended periods of time.
Automated marine pest population monitoring
This project is developing advanced image processing techniques and underwater robotic platforms to detect, count and map the distribution of a range of marine pests. It expands previous research into automated marine pest classification for Crown-of-Thorns Starfish (Acanthaster planci) and Northern Pacific Sea Star (Asterias amurenis), with the goal of improving detection rates and providing tools for accurately measuring their spatial and temporal distribution. The results will assist marine scientists and authorities in understanding pest movement dynamics, their impact, and in managing threats.
For more information check out the project page here.
Image-based landform change quantification
This project is developing new image processing and associative learning techniques to improve 3D digital terrain mapping fidelity from low-altitude aerial images. The goal is to improve the detection and quantification of small-scale land-form change, in particular rill erosion, to sufficient fidelity to allow regulators and scientists to estimate sediment load into receiving waterways.
Maritime RobotX Challenge
QUT was selected as one of three teams to represent Australia at the Maritime RobotX Challenge taking place October 20-26, 2014 in Singapore. TeamQUT consists of a group of enthusiastic students studying a range of engineering majors, including mechatronics, electrical, and computer and software systems. The Challenge sponsors provided all competing teams with a WAM-V USV manufactured by Marine Advanced Research Inc. Supplied with no propulsion or sensors, TeamQUT have fitted their platform with an electric propulsion system and various localisation and perception sensors. Their task is to develop vision and laser-based navigation algorithms to complete five challenging tasks.
For more information check out teamQUT's official website.
Robots Past and Present
Over the last 12 years I have built and developed many field robot platforms for the sea, land and air domains. Particular emphasis has been on applying them to undertake complex tasks and answering specific questions particularly relating to environmental science.
Robots developed and used since joining QUT in June 2013.
These are the many robots I worked on and developed whilst working at the CSIRO Autonomous Systems Laboratory.
Complete publication list and citation analysis is available from Google Scholar.
You can also access most of my publications at the QUT ePrints repository.
Dunbabin, M. and Grinham, A. (2017). Quantifying Spatiotemporal Greenhouse Gas Emissions Using Autonomous Surface Vehicles, Journal of Field Robotics, 34(1), pp 151-169.
Phillips, B.T., Dunbabin, M., Henning, B., Howell, C., DeCiccio, A., Flinders, A., Kelley, K.A., Scott, J.J., Albert, S., Carey, S., Tsadok, R. and Grinham, A. (2016). Exploring the "Sharkcano";Biogeochemical observations of the Kavachi submarine volcano (Solomon Islands). Oceanography, 29(4), pp. 160-169.
Dunbabin, M. and Marques, L. (2012). Robotics for environmental monitoring: Significant advancments & applications, IEEE Robotics & Automation Magazine, 19(1), pp. 24-39.
Grinham, A., Dunbabin, M., Gale, D. and Udy, J. (2011). Quantification of ebullitive and diffusive methane release to atmosphere from a from a water storage, Atmospheric Environment, 45(39), pp. 7166-7173, doi:10.1016/j.atmosenv.2011.09.011.
Roser, M., Dunbabin, M., and Geiger, A. (2014). Simultaneous Underwater Visibility Assessment, Enhancement and Improved Stereo, In Proc. International Conference on Robotics & Automation (ICRA), Hong Kong, Accepted 14 January 2014.
Witt, J., and Dunbabin, M. (2008). Go with the flow: Optimal AUV path planning in coastal environments. In Proc. 2008 Australasian Conference on Robotics & Automation, Canberra, pp. 1-9 (online proceedings).
Dunbabin, M., Corke, P., Vascilescu, I., and Rus, D. (2006). Data muling over underwater wireless sensor networks using autonomous underwater vehicles. In Proc. International Conference on Robotics & Automation (ICRA), pp. 2091-2098.
Vasilescu, I., Kotay, K., Rus, D., Dunbabin, M., and Corke, P. (2005). Data collection, storage and retrieval with an underwater sensor network. In Proc. IEEE SenSys, pp.154-165.
Dunbabin, M., Roberts, J., Usher K., Winstanley, G., and Corke, P. (2005). A hybrid AUV design for shallow water reef navigation. In Proc. of the International Conference on Robotics & Automation (ICRA), April, pp. 2117-2122.
Dr Matthew Dunbabin | Principal Research Fellow (Autonomous Systems)
Institute for Future Environments | School of Electrical Engineering and Computer Science
Science and Engineering Faculty | Queensland University of Technology
phone: + 61 7 3138 0392 | fax: + 61 7 3138 1469
Gardens Point, S Block 1107 | 2 George Street, Brisbane, QLD 4000 | CRICOS No. 00213J