Information consensus and coordination of multiagent systems. Revolutions in information and communication technologies create a complex 'network of everything'. This project will develop advanced control techniques for such networks, to make the nation's power systems safer, to fly formations of unmanned airborne vehicles, and to extract key information from networks of environmental monitoring sensors.
Discovery Early Career Researcher Award - Grant ID: DE120102873
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Securing networked control and estimation systems and safeguarding critical infrastructure. The purpose of this project is to reduce the likelihood of success, and the severity of impact, of a cyber-attack against networked control and estimation systems operating within critical infrastructure. The outcome will be a suite of algorithms, tools and design considerations for networked, industrial, control systems that satisfy this purpose.
Spatially distributed complex multiagent systems. This project will develop design methodologies for two related classes of technological systems: wireless sensor networks (in particular mobile sensor networks) and formations of mobile robotic agents. These technologies find application today in defence, and will probably become pervasive in the civilian sector.
Industrial Transformation Training Centres - Grant ID: IC140100003
Funder
Australian Research Council
Funding Amount
$2,389,935.00
Summary
The ARC Research Training Centre for Naval Design and Manufacturing. ARC Training Centre for Transforming Australia's Naval Manufacturing Industry. The aim of the Training Centre is to transform the Australian naval manufacturing industry by creating a new cohort of industry-focused, high-level and broadly skilled engineers and researchers. The resulting network of engineering researchers will enable the industry to more rapidly innovate and solve key problems concerning the efficient design, co ....The ARC Research Training Centre for Naval Design and Manufacturing. ARC Training Centre for Transforming Australia's Naval Manufacturing Industry. The aim of the Training Centre is to transform the Australian naval manufacturing industry by creating a new cohort of industry-focused, high-level and broadly skilled engineers and researchers. The resulting network of engineering researchers will enable the industry to more rapidly innovate and solve key problems concerning the efficient design, construction and sustainment of naval platforms. This industrial transformation will bring significant benefits to Australia as it commences a very ambitious shipbuilding program comprising the design and manufacture of new fleets of submarines, future frigates and patrol boats. The success of these major projects is reliant on developing this cohort of researchers to solve the key research questions.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE120102388
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
From Bayesian filtering to smoothing and prediction for multiple object systems. This project will develop new and improved algorithms for tracking multiple targets, such as tanks, submarines or planes, using the state of the art in mathematical and computational design. These will enable more efficient and accurate technologies for defence related applications including intelligence, surveillance and reconnaissance.
Parameter estimation for multi-object systems. Parameter estimation in multi-object system is essential to the application of multi-object filtering to a wider range of practical problems with social and commercial benefits. This project develops the necessary parameter estimation techniques for complete 'plug-and-play' multi-object filtering solutions that facilitates widespread applications.
Biologically-inspired detection, pursuit and interception of moving objects by unmanned aircraft systems. Although it is well known that aggressive honeybees are very effective at detecting, pursuing and intercepting moving targets, this behaviour has never been studied quantitatively. This project will use high-speed video cinematography to investigate this behaviour, to develop visual algorithms for the detection of moving targets, and to create dynamical models of the mechanisms that control ....Biologically-inspired detection, pursuit and interception of moving objects by unmanned aircraft systems. Although it is well known that aggressive honeybees are very effective at detecting, pursuing and intercepting moving targets, this behaviour has never been studied quantitatively. This project will use high-speed video cinematography to investigate this behaviour, to develop visual algorithms for the detection of moving targets, and to create dynamical models of the mechanisms that control pursuit. The resulting algorithms will be incorporated into unmanned aerial vehicles for detecting, monitoring and tracking other objects in the sky, and their performance will be evaluated. The results will provide a better understanding of the biological basis of pursuit behaviour, as well as lead to novel technologies for aerial surveillance and safety.Read moreRead less
Strategies for mid-air collision avoidance in aircraft: lessons from bird flight. Birds seldom collide with each other and other objects, despite the high speeds at which they fly in complex environments. This project will examine how birds sense and avoid impending collisions, and will use these results to design novel strategies for the detection and avoidance of aircraft mid-air collisions.
Discovery Early Career Researcher Award - Grant ID: DE120101503
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Design of a biologically inspired running and climbing robotic lizard. Watch any movie and it will tell you that robots are the future. The trouble is that recent attempts to build running and climbing robots have had limited success. This project explores locomotion of lizards to improve upon shortfalls in current robotic design, to build biologically inspired robots capable of running and climbing up and down walls.
A stochastic geometric framework for Bayesian sensor array processing. This project develops a mathematical framework, and a new generation of techniques, for sensor array processing to address real-world problems with uncertainty in array parameters and number of signals. The outcomes will enhance the capability of sensors in many application areas including, radar, sonar, astronomy and wireless communications.