The cornerstone of the QUT Centre for Robotics is that we are able to solve problems in a broad sphere of applications. Our research interests are diverse and we are continually in discussion with industry and government about how we can address their most pressing current and future challenges. Contact our team to discuss how we can collaborate with you.
- All Categories
- Aerospace Autonomy
- Autonomous Vehicles
- Marine & Environmental
- Medical & Healthcare
- Robotic Grasping
- Vision Learning & Understanding
AUSMURI: Neuro-Autonomy: Neuroscience-Inspired Perception, Navigation, and Spatial Awareness for Autonomous Robots
09/01/2019 - 09/01/2024
State-of-the-art Autonomous Vehicles (AVs) are trained for specific, well-structured environments and, in general, would fail to operate in unstructured or novel settings. This project aims at developing next-generation AVs, capable of learning and on-the-fly adaptation to environmental novelty.
This project is conducting research on visual place recognition for ground-based robots in extreme environments, such as underground mining and nuclear decommissioning scenarios.
We're solving complex developmental problems related to autonomous driving, to help deliver game-changing autonomous vehicle technologies in Australia.
QUT researchers who took an Artificial Intelligence (AI) system on a south-east Queensland road trip have identified the key role high-definition annotated maps will likely play in autonomous driving on Australian roads.
Advance Queensland Innovation Partnership, Caterpillar, Mining3
The project offers potential solutions to the challenge of accurately estimating the position of vehicles in underground mining environments.
01/06/2015 - 31/12/2019
Australian Research Council Future Fellowship Scheme FT140101229
By modelling the behaviour of rats, we can create better algorithms to make cheaper robots.
09/01/2016 - 09/01/2020
By creating better neural networks, we can ensure that we don't need massive amounts of data or computation to make robots
01/01/2020 - 02/08/2024
03/01/2017 - Ongoing
QUT has led development of a fleet of miniature autonomous vehicles as part of the Australian Centre for Robotic Vision.
Funded by Queensland Department of Agriculture and Fisheries
Meet AgBot II, a new generation tool for robotic site-specific crop and weed management.
Modelling the neural mechanisms in the brain underlying tasks like 3D navigation and 3D spatial cognition to develop new neuromorphic 3D SLAM and 3D cognitive navigation techniques.
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.
We have developed an autonomy package to ensure that human-piloted inspection drones do not collide with poles, cross arms and wires.
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.
This project aims at developing a robotic surgical assistant for knee arthroscopy, composed of a robotic arm with an attached camera-arthroscope bundle for intra-articular navigation, and a robotic knee manipulator.
The system designed by the interdiscipliary robotics team combines advanced functionalities in pixel design to develop state-of-the-art miniaturised cameras for robotic vision.
This project aims to develop highly dexterous snake-like tools for both manual and robotic orthopaedic surgeries by applying novel continuum mechanisms to the design and implementation.
01/02/2017 - Ongoing
This project evaluates the effect of illumination on the performance of Visual Odometry (VO) in underground mining environments to identify suitable illumination configurations that should be used to obtain the best performance of VO in these environments.
01/01/2019 - 22/10/2020
The project evaluates the feasibility of current state-of-the-art robotic manipulation solutions to be applied to the task of automated vehicle maintenance
12/01/2018 - 22/10/2020
This project investigates the use of semantic mapping methods for the purpose of robotic maintenance in mining
A baseline dataset for performance evaluation of visual detection and classification techniques in mining environments
03/01/2018 - Ongoing
This project aims at building a reference dataset to evaluate the performance of state-of-the-art visual-based object detection and classification methods in mining contexts
Deep learning has taken the research world by storm. At QUT, researchers are using advanced deep learning techniques in combination with established approaches to solve new problems.
There is an ongoing issue in research fields known as the reproducibility issue. Here, QUT researchers are fostering an important bench-marking system for robot pick and place research.
At the 2016 Amazon Picking Challenge in Germany, 16 teams from around the world competed. The Australian Centre for Robotic Vision team reached sixth place in the picking task using a Rethink Robotics Baxter.
Our team came first in the global competition, against 16 teams from around the world competed, and winning the $80,000USD first prize. To do this, we created a novel Cartesian manipulator dubbed ‘Cartman’ with a rotating gripper to allow item pick-up using either suction or a simple two-finger grip.
This project is supported by a 2019 Amazon Research Award to Dr Niko Suenderhauf.
Plant Biosecurity CRC (2014 - 2018)
The goal of this project is to research and develop a fully autonomous robotic crop management system for protected cropping systems
This project aims to develop a robotic crop management system for the growth of nutrient-dense crops within Vertical Farming Systems
QUT has developed a prototype robotic capsicum (sweet pepper) harvester nicknamed ‘Harvey’, combining robotic vision and automation expertise to benefit agricultural producers.
This project is investigating smarter ways for robots to see around all the clutter in order to better monitor crops in agricultural environments.
13/07/2020 - 14/12/2020
A review on map creation, monitoring and maintenance to facilitate automated driving including government's potential role.
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.
07/05/2018 - Ongoing
This project focuses on the automatic detection of surface cracks (a.k.a fracture or sharp deformation breaks).
01/01/2018 - 31/12/2020
This Project aims to develop a framework for Unmanned Aerial Vehicles (UAV), which optimally balances localisation, mapping and other objectives in order to solve sequential decision tasks under map and pose uncertainty.
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.
The aim of this research is to develop a framework for multiple Unmanned Aerial Vehicles (UAV), that balances information sharing, exploration, localization, mapping, and other planning objectives thus allowing a team of UAVs to navigate in complex environments in time critical situations.