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.


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.

Visual Place Recognition for Robotics in Extreme Environments

This project is conducting research on visual place recognition for ground-based robots in extreme environments, such as underground mining and nuclear decommissioning scenarios.

Rheinmetall Defence Australia: Advanced Terrain Detection (ATD)

We're solving complex developmental problems related to autonomous driving, to help deliver game-changing autonomous vehicle technologies in Australia.

[COMPLETED] How automated vehicles will interact with road infrastructure

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.

Automation-enabling positioning for underground mining

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.

[COMPLETED] ARC Future Fellowship: Superhuman place recognition

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.

US Air Force / AOARD: An infinitely scalable learning and recognition network

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

Mini Autonomous Vehicles

03/01/2017 - Ongoing

QUT has led development of a fleet of miniature autonomous vehicles as part of the Australian Centre for Robotic Vision.

AgBot II

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.

Semi-automated power pole inspection

We have developed an autonomy package to ensure that human-piloted inspection drones do not collide with poles, cross arms and wires.

Inference boats

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.

Robotic knee arthroscopy

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.

Cameras and sensors

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.

Continuum robots

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.

Effect of lighting on visual odometry performance in underground mines

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.

Robotic Manipulation for Automated Maintenance

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

Semantic Mapping for Robotic 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 for Grasping and Manipulation

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.

ACRV Picking Benchmark

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.

Amazon Picking Challenge (2016)

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.

Amazon Picking Challenge (2017)

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.

Learning Robotic Navigation and Interaction from Object-based Semantic Maps

This project is supported by a 2019 Amazon Research Award to Dr Niko Suenderhauf.

UAVs/ Drones for Agriculture and Plant Biosecurity

Plant Biosecurity CRC (2014 - 2018)

Autonomous Robotic Platforms for Greenhouses

The goal of this project is to research and develop a fully autonomous robotic crop management system for protected cropping systems

Robotic Vertical Farming Systems

This project aims to develop a robotic crop management system for the growth of nutrient-dense crops within Vertical Farming Systems

Harvey – The Robotic Capsicum Harvester

QUT has developed a prototype robotic capsicum (sweet pepper) harvester nicknamed ‘Harvey’, combining robotic vision and automation expertise to benefit agricultural producers.

Moving to See

This project is investigating smarter ways for robots to see around all the clutter in order to better monitor crops in agricultural environments.

HD maps for automated driving

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.

Surface Crack Detection and Localisation for Automated Underground Mine Void Characterisation

07/05/2018 - Ongoing


This project focuses on the automatic detection of surface cracks (a.k.a fracture or sharp deformation breaks).

Navigating Under the Forest Canopy and in the Urban Jungle

01/01/2018 - 31/12/2020

ARC DP180102250

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.

UAVs, Hyperspectral Remote Sensing and Machine learning Revolutionizing Reef Monitoring

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.

When every second counts: Multi-drone navigation in GPS-denied environments

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.