RESEARCH THEMES
Our primary research focus areas include:
- UAVs for Precision Agriculture and Plant Biosecurity
- UAVs for Air Quality and Air pollution
- Environment: Wildlife and Conservation and Reef Monitoring
- UAV Autonmomy : UAV navigation in GPS Denied Environments, Aerial Manipulation
UAVs for Precision Agriculture and Plant Biosecurity
The aim of this research stream is to address the challenges in autonomy, sensor integration and data analysis when using UAS for precision agriculture and
We have developed and flight tested novel tools and algorithms. One of the unique aspects of our project is that we operate and integrate multiple sensors: visible, thermal, multispectral, an onboard image classification algorithm and LiDAR. We have worked on a range of applications, including precision agriculture; mapping and monitoring vegetation in remote locations.
PBCRC2164: Developing pest risk models of Buffel Grass using Unmanned Aerial Systems and statistical methods
Overview
Buffel grass (Cenchrus Ciliaris) is an introduced, perennial pasture grass that is found across much of the Australian continent, including arid and semi-arid regions. For many decades it has been widely planted for livestock production and land rehabilitation. In the cattle industry it is well regarded as pasture because it grows rapidly under warm, moist conditions and persists under heavy grazing and drought.
However, buffel grass has spread well beyond planted areas and dominates the ground layer in many native plant communities. It reduces native plant diversity and can affect vegetation structure by changing fire regimes. In arid Australian areas such as Uluru-Kata Tjuta National Park, buffel grass invades some of the wetter, more fertile parts of the landscape, limiting the chances of survival of native plant and animal populations. Although it was planted for dust control in central Australia, it also imposes economic costs through the need to manage fire risks and to protect biodiversity assets and infrastructure.
Buffel grass has been identified as a major threat to biodiversity in certain areas of Australia. On Barrow Island (WA) for example, Chevron Australia has embarked on a significant campaign to map and eradicate buffel grass as a part of the environmental commitments related to the Gorgon Gas Project.
Aims and Objectives
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 project requires advanced engineering, ecological and statistical skills. In essence, the idea is to fly a UAV with sensors across a swathe of landscape, identify pests of interest, develop a predictive risk model based on the received information, apply this model to a wider landscape acknowledging uncertainties in prediction, and develop corresponding surveillance protocols for pest detection.
This project has both immediate and longer-term strategic benefits. The protocols and methodology arising from this project will focus on detection of a specific invasive species, Buffel Grass, on Barrow island (BI). However the general skills, know-how and methods that are developed in the project will be much more widely applicable to other pest risk problems and more broadly to other geographic detection and modelling projects.
Milestones & Achievements
- Flight campaign using UAVs and imaging sensors onboard unmanned aircraft for invasive species detection and identification at exemplar site completed;
- Delivery of review of existing methods for detection and progress report assessing feasibility of using imaging sensors on board unmanned aircraft for invasive species detection and identification, based on limited datasets;
- Delivery of progress report on review of predictive modelling approaches combined with feasibility of using imaging sensors on board unmanned aircraft for invasive species detection and identification;
- Flight campaign using UAVs and imaging sensors onboard unmanned aircraft for invasive species detection and identification conducted at candidate locations completed.
Project Contacts
For further information regarding this project please contact the project contacts below:
- Associate Professor Felipe Gonzalez
PBCRC2135 – Optimising Surveillance Protocols Using Unmanned Aerial Systems
Overview
This project will investigate the sensitivities and capacity of emerging unmanned aerial systems (UASs) and imaging technologies for biosecurity surveillance in viticulture, horticultural and grain industries. The overarching aim is to investigate the use of these technologies to support claims of pest freedom and low pest prevalence compared to commonly deployed surveillance practices and utility, to inform pest management decisions for established species. This project will investigate the sensitivities and capacity of emerging unmanned aerial systems (UASs) and imaging technologies for biosecurity surveillance in viticulture, horticultural and grain industries. The overarching aim is to investigate the use of these technologies to support claims of pest freedom and low pest prevalence compared to commonly deployed surveillance practices and utility, to inform pest management decisions for established species.
Aims & Objectives
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 project objectives are:
- Modelling region-wide environmental changes to identify criteria for selecting high-risk surveillance areas and compare these predictors to current selection methods deployed by biosecurity personnel;
- Prioritise sampling times and areas within targeted areas to direct surveillance efforts and increase rate of first detection using higher-resolution surveillance technologies (fixed-wing UAVs) and unique spectral signatures;
- Evaluate utility of higher-resolution cameras and robotic technologies on multi-rotor UASs to categorise and/or collect target pests on different plant structures for identification by trained diagnosticians; and
- Synthesise modelling and improved UAS technologies to demonstrate a practical application for surveillance of high priority plant pests in commercial crops.
Milestones & Achievements
- Integration of multi-spectral and thermal sensor technologies with UAS on multi-rotor completed;
- Initial identification of farms for all case studies;
- Initial identification of end-user/reference team and development of an engagement plan
Project Contacts
For further information regarding this project please contact the project contacts below:
- Associate Professor Felipe Gonzalez
Air Quality
The aim of this research stream is to address the challenges in autonomy, sensor integration and data analysis when using UAVs for remote gas sensing applications.
We have developed and tested artificial intelligence, autonomy and a gas sensing capability for electric and solar powered UAS.
Work is currently focused in two streams; the first is on optimal path planning using Markov Decision Process (MDP), the second is a gas sensing environmental monitoring capability. In the first research stream we are using artificial intelligence based on MDP and studying the manner in which birds make use of wind energy to fly with minimum power, the manner in which they glide and their use of wind to move and change their flight path.
We have developed algorithms that in addition to using solar energy, take advantage of thermals, rising columns of hot air that birds use to fly using less energy. In the second research stream we are using efficient multidisciplinary aircraft design techniques, solar cells, conventional infrared and cutting edge nano-scale technologies to track gases of environmental and agricultural interest. The stream focuses on optimising weight, energy consumption and performance. The principal gases of interest are Carbon Dioxide (CO2), Methane (CH4) and Nitrogen Dioxide (NO2).
Autonomous Aerial Atmospheric Sampling above GBR
Overview
Understanding the role of clouds in the warming and cooling of the planet, and how that role changes in a warming world is one of the biggest uncertainties climate change researchers face. A key feature in this regard is the influence on cloud properties of cloud condensation nuclei (CCN), the very small atmospheric aerosol particles necessary for the nucleation of every single cloud droplet. The anthropogenic contribution to CCN is known to be large in some regions; however, the natural processes that regulate CCN over large parts of the globe are less well understood, and particularly in the Great Barrier Reef. The production of new aerosol particles from biogenic sources (forests, marine biota, etc.) is a frequent phenomenon capable of affecting aerosol concentrations, and therefore CCN, on both regional and global scales. The biogenic aerosol particles therefore have a major influence on cloud properties and hence climate and the hydrological cycle. Determining the magnitude and drivers of biogenic aerosol production in different ecosystems is therefore crucial for the future development of climate models.
Aims & Objectives
The fundamental questions that this study will address are:
- What is the significance of this ecosystem as a natural source of aerosol particles?
- How strong is this source at the regional level?
- What is the mechanism of particle production over the GBR?
Milestones & Achievements
A UAV was equipped with a DISCmini (Diffusion Size Classifier) and a TSI’s Q-TRAK Indoor Air Quality Monitor sensor, offer a new method to capture fine scale data of the air composition, including emission from GBR and combustion sources such as ship chimneys. UAV’s will assist in the monitoring and detection of ambient air particles by allowing the evaluation of sources and their emissions.
The aim of this project was to gather data within the use of a portable ultrafine particle counter (DISCmini) and a TSI’s Q-TRAK Indoor Air Quality Monitor sensor, integrated on board a multi-rotor, for environmental air monitoring from transportation as the anthropogenic source.
Project Contacts
Associate Professor Felipe Gonzalez
ARC LP Establishing advanced networks for air quality sensing and analyses
Overview
This project aims to develop innovative, cost-effective, high resolution air quality networks. Recent developments in sensor technologies improve the ability to harvest atmospheric data. This project will develop, validate and implement methods for high sensitivity atmospheric sensing and apply cutting-edge statistical and analytic techniques to the data sets, unprecedented in scope and resolution. Outcomes include an open access database to quantify and visualise intra-urban air pollution and human exposure and develop air quality maps and smoke pollution management tools. It is expected to advance the evidence-based management of air as a resource, increasing economic prosperity and enhancing human health and quality of life.
Investigators
Queensland University of Tehnology
AUD445,000
LP160100051
Wildlife and Conservation
The aim of this research stream is to address the challenges in autonomy, sensor integration and data analysis when using UAVs for wildlife monitoring and conservation
Assessing the capabilities of digital imaging and Unmanned Aerial Systems (UAS) for species management
Overview
Understanding the abundance of a species in an area is fundamental to the management of that species (including the control of pest and feral species). This information is critical to allow Local Government to assess the need for population support or control, and to assess the effectiveness of management actions.
Improved abundance estimates of koalas will assist with cost effective management – since the correct management action can be applied at the correct time. Moreover, by utilising emerging technology such as unmanned aircraft to assist with koala population abundance estimates, local councils can make substantial cost savings through reductions in personnel and equipment necessary to conduct ground based surveys, and they can conduct the task in a more timely manner. Other benefits include the ability to access terrain which would be otherwise difficult, dangerous or inaccessible by foot, and the associated safety concerns when traversing these areas.
Aims & Objectives
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. This proposed collaboration between three councils is a bold and positive step towards taking the lead in achieving a coordinated approach to koala monitoring and population assessment.
Milestones & Achievements
Project Contacts
- Associate Professor Felipe Gonzalez
UAVs, Hyperspectral Remote Sensing and Machine learning Revolutionizing Reef Monitoring
Recent advances in Unmanned Areal Systems (UASUAS) also commonly known as drones or Remotely Piloted Aircraft (RPA) sensed imagery, sensor quality/size and geospatial image processing enable UAS’s to rapidly and continually monitor coral reefs for any determining the type of coral and signs of coral bleaching. This research focuses on an Unmanned Aerial Vehicle (UAV) remote sensing based methodology to increase the efficiency and accuracy of existing surveillance practices to monitor corals and to detect coral bleaching. The methodology uses a UAV UAS integrated with advanced digital hyperspectral RGB sensors and machine learning algorithms. We evaluate the methodology on a predictive model for Bleaching detection.
In this project we explore the combination of air-borne RGB and hyperspectral imagery with in-water based data at sixty-four distinct locations with several types of coral under diverse levels of bleaching. We describe the technology used, the sensors, the UAV, the flight operations, the processing workflow of the datasets from each imagery type, the methods for combining multiple air-borne with ground based datasets and we finally present relevant results of correlation between the different processed datasets. The development of a methodology for the collection and analysis of airborne multi and hyperspectral imagery would provide coral reef researchers, scientists and UAV practitioners, with reliable data
UAV Autonomy : GPS Denied Environments, Aerial Manipulation
a) ARC Discovery (DP180102250) Navigating under the forest canopy and in the urban jungle
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 expects to generate new knowledge in UAV navigation using an innovative approach by combining simultaneous localisation and mapping algorithms with partially observable markov decision processes. The project’s expected outcomes will enable UAVs to solve multiple objectives under map and pose uncertainty in GPS-denied environments. This will provide significant benefits, such as more responsive disaster management, bushfire monitoring and biosecurity, and improved environmental monitoring
b) Development of a robust framework for an outdoor mobile manipulation UAV
There is a growing interest to autonomously collect or manipulate objects in remote or unknown environments, such as mountains, gullies, bush-land, or rough terrain. There are several limitations of conventional methods using manned or remotely controlled aircraft. The capability of small Unmanned Aerial Vehicles (UAV) used in parallel with robotic manipulators could overcome some of these limitations. By enabling the autonomous exploration of both naturally hazardous environments, or areas which are biologically, chemically, or radioactively contaminated, it is possible to collect samples and data from such environments without directly exposing personnel to such risks. This research covers the design, integration, and initial testing of a framework for outdoor mobile manipulation UAV. The framework can allow further integration and testing of complex control theories, with the capability to operate outdoors in unknown environments.
Integrated Intelligent Airborne Sensing 