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
- Deep Learning
- Marine & Environmental
- Medical & Healthcare
- Robotic Grasping
- Scene Understanding
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.
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.
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.
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
We're solving complex developmental problems related to autonomous driving, to help deliver game-changing autonomous vehicle technologies in Australia.
In this project the team undertook a review and preliminary evaluation of existing technical solutions for hazard detection using vision sensors only.
Use of UAS and Hyperspectral Remote Sensing for Early detection of Phylloxera Infestation in Vineyards
The aim of this project is to use predictive models combined with high-resolution detection technologies to increase sampling efficiency and improve first detection rates.
The ultimate aim of this project is to add real time weed management capabilities to low cost UAS for control of invasive species such as sphere thistle weed.
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.
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.
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.
Plant Biosecurity CRC
The aim of this project is to develop a system which combines new detection methods (UAVs and specialised sensors) with advanced modelling techniques to determine high-risk areas for pest risk surveillance, namely buffel grass.
The aim of this research was to establish the best mounting point for four gas sensors and a Particle Number Concentration (PNC) monitor, onboard a hexacopter, so to develop a UAV system capable of measuring point source emissions.
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
This project aims to assess the use of aerial manipulation in remote sampling applications, and the capability of such systems in outdoor environments.
This project designed a system to detect bitou bush in coastal dunes using unmanned aircraft and machine learning classification algorithms towards the development of a flexible approach to monitoring and track similar weeds of interest in New South Wales and Queensland.
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.
13/07/2020 - 14/12/2020
A review on map creation, monitoring and maintenance to facilitate automated driving including government's potential role.
12/01/2018 - 21/12/2020
This project investigates the use of semantic mapping methods for the purpose of robotic maintenance in mining
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.
Much research has focused on standard multi-rotor position and attitude control with and without a slung load. However, predictive control schemes, such as Nonlinear Model Predictive Control (NMPC), have not yet been fully explored.
Plant Biosecurity CRC (2014 - 2018)
This project investigates the sensitivities and capacity of emerging unmanned aerial systems (UASs) and imaging technologies for biosecurity surveillance in viticulture, horticultural and grain industries.
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.
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.
In order to fully integrate deep learning into robotics, it is important that deep learning systems can reliably estimate the uncertainty in their predictions. This would allow robots to treat a deep neural network like any other sensor, and use the established Bayesian techniques to fuse the network’s predictions with prior knowledge or other sensor measurements, or to accumulate information over time. Our work focusses on Bayesian Deep Learning approaches for the specific use case of object detection on a robot in open-set conditions.
The Inference boats are designed to be our eyes, ears and nose on waterways, 24 hours a day - rain, hail or cyclone.
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.
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.
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 developing new techniques for automated early detection of the invasive grass African Lovegrass, using methods ranging from machine learning to psychophysics.
This cross disciplinary project aims to develop, validate and implement novel methods for high sensitivity atmospheric sensing and apply cutting-edge statistical and analytic techniques to the data sets, unprecedented in scope and resolution.
This project is supported by a 2019 Amazon Research Award to Assoc Prof Niko Suenderhauf.
This project investigates how such graph-based maps, containing both semantic and geometric information of objects in the environment, can be utilised to learn complex robotic tasks that require navigation, exploration, and interaction with the environment.
RangerBot is a low-cost, vision-enabled autonomous underwater vehicle for monitoring a wide range of issues facing coral reefs across the globe.
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.
This project is conducting research on visual place recognition for ground-based robots in extreme environments, such as underground mining and nuclear decommissioning scenarios.
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.
We develop novel methods for Semantic Mapping and Semantic SLAM by combining object detection with simultaneous localisation and mapping (SLAM) techniques.
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.
Funded by Queensland Department of Agriculture and Fisheries
Meet AgBot II, a new generation tool for robotic site-specific crop and weed management.
Aerial Mapping of Forests Affected by Pathogens using UAVs, Hyperspectral Sensors and Artificial Intelligence: Myrtle Rust
This project aims to provide end-users with the value of these technologies in guiding decisions and adopting systems based on capabilities to detect exotic pathogens on host plantation and natural forests.
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.
03/01/2017 - Ongoing
QUT has led development of a fleet of miniature autonomous vehicles as part of the Australian Centre for Robotic Vision.
The team investigate how classical navigation algorithms can be improved by learning-based approaches.
We have developed an autonomy package to ensure that human-piloted inspection drones do not collide with poles, cross arms and wires.
Th aim of this research is to develop a framework of a team of UAVs to cooperatively finding one or multiple targets in a real-world based environment with obstacles is being developed.
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.
25/02/2019 - Ongoing
This project aims to incorporate semantic cues into the navigation pipeline of UAVs. One of the projects key aspects is the influence of the unique positions of UAVs on the semantic information extracted from the vision system. Another key aspect is the transferral of algorithms to the constrained computing environment provided by UAVs.
The goal of this project is to research and develop a fully autonomous robotic crop management system for protected cropping systems
01/01/2019 - 21/12/2020
The project evaluates the feasibility of current state-of-the-art robotic manipulation solutions to be applied to the task of automated vehicle maintenance
This project aims to investigate the incorporation of a sequential decision-making model framework for fully autonomous UAV operations, able to navigate under unstructured environments and reduce levels of target detection uncertainty.
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.
Securing Antarctica’s Environmental Future (SAEF) is an Australian Research Council Special Research Initiative that aims to strengthen Antarctic science, policy and governance at a time of rapid environmental and geopolitical change.
01/01/2020 - 02/08/2024
ARC INDUSTRIAL TRANSFORMATION AND TRAINING CENTRE FOR JOINT BIOMECHANICS INNOVATION FOR AUSTRALIAN BIOMECHANICAL RESEARCH The ARC ITTC for Joint Biomechanics aims to bring together leading researchers, industry partners and end-users to train a new generation of interdisciplinary and skilled graduates to tackle industry-focused challenges in joint biomechanics.
This project is investigating smarter ways for robots to see around all the clutter in order to better monitor crops in agricultural environments.
Commonwealth Grant CRCPIX000099 (Cooperative Research Centre Project); Agent Oriented Software (Lead Partner)
The project aims to develop a robot to autonomously find and identify individual noxious weeds, spraying or using an alternative method to eliminate them. It will produce a series of operational prototypes ("Kelpie"), based upon a commercially available agricultural chassis.
This research is aimed at developing a framework for mission planning and geological surface features detection using UAV in GPS-denied environments such as Mars. It involves the development and flight-testing of UAV prototypes capable of navigating in unknown GPS-denied environments in the search of geological features using on-board cameras.
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.
07/05/2018 - Ongoing
This project focuses on the automatic detection of surface cracks (a.k.a fracture or sharp deformation breaks).
This project demonstrated that the PNC sampled with a constant traffic flow, increased from a concentration of 2×104 p/cm^3 near the ground up to 10 m, and then sharply decreased attaining a steady value of 4×103 p/cm^3 beyond a height of about 40 m.
QUT has developed a prototype robotic capsicum (sweet pepper) harvester nicknamed ‘Harvey’, combining robotic vision and automation expertise to benefit agricultural producers.
This project aims to develop a robotic crop management system for the growth of nutrient-dense crops within Vertical Farming Systems