QUT conducts a wide variety of research into marine and aquatic environmental monitoring using robotics and computer vision. Our research is focused around advanced image processing, path-planning, adaptive sampling and control algorithms for applications such as large-scale process charactersiation, habitat and marine pest classification, and persistent autonomy in highly dynamic environments.
QUT has a range of underwater, surface and aerial robotic platforms for operation in marine and aquatic environments.
|Swinglet CAM UAV|
We 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.
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.
This project is developing advanced image processing techniques and underwater robotic platforms to detect, count, map and manage populations 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 as well as control outbreak populations. The results will assist marine scientists and authorities in understanding pest movement dynamics, their impact, and in managing threats.
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.
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.