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With the expertise of internationally recognised researchers into field robotics, particularly environmental robots, and their application to large-scale marine habitat monitoring, marine pest (Crown-of-Thorns Starfish) control, and aquatic greenhouse gas mapping, such as Dr Matthew Dunbabin. He and his team have expertise in adaptive sampling and path planning, vision-based navigation, cooperative robotics, as well as robot and sensor network interactions.
Current projects in marine robotics include successfully testing COTSbot on the Great Barrier Reef, and extending this success to produce the smaller version, RangerBot.
We welcome industry collaborations in this field. For more information about collaborating with our team, please contact us.
03/01/2016 - 08/01/2019
LunaRoo was started as a proposal for the Lunar Payload Opportunity by the Google Lunar X Prize team scientists.
The Inference boats are designed to be our eyes, ears and nose on waterways, 24 hours a day - rain, hail or cyclone.
RangerBot is a low-cost, vision-enabled autonomous underwater vehicle for monitoring a wide range of issues facing coral reefs across the globe.
COTSbot seeks out and controls the Great Barrier Reef's crown-of-thorns starfish (COTS), which are responsible for an estimated 40 per cent of the reef's total decline in coral cover.
Unmanned Aerial Vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation
The aim of this research is to explore and developed a system which includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas.
Assessing the capabilities of digital imaging and Unmanned Aerial Systems (UAS) for species management
Logan, Tweed Shire and Gold Coast City Council
The key aim of this Project is to assess the utility of digital imaging for the cost effective detection and assessment of koala abundance in Tweed, Gold Coast and Logan local government areas (LGAs) using an innovative approach which combines Unmanned Aerial Vehicles, digital imaging, and statistical modelling.
The aim of this project is to predictive models and deep learning combined with high resolution hyperspectral detection technologies to increase surveying efficiency and to develop methodology for aerial coral bleaching detection.