Robust learning of dynamic systems. Robots and other autonomous machines use models of the real world to predict the result of their actions and make decisions, but existing methods used for machine-learning are unreliable in many cases and can be easily fooled. This project aims to make machine-learning of dynamic system models reliable, accurate, and secure. The outcomes of this project will be new models and algorithms that ensure safety and increase accuracy of models learned from data. This ....Robust learning of dynamic systems. Robots and other autonomous machines use models of the real world to predict the result of their actions and make decisions, but existing methods used for machine-learning are unreliable in many cases and can be easily fooled. This project aims to make machine-learning of dynamic system models reliable, accurate, and secure. The outcomes of this project will be new models and algorithms that ensure safety and increase accuracy of models learned from data. This project will benefit robotics, control engineering, infrastructure automation, and other fields that demand the capability to model physical systems from limited data. It will also improve cybersecurity by making learning algorithms resilient to deliberate attacks with false data.Read moreRead less
Exploiting the Symmetry of Spatial Awareness for 21st Century Automation. This project aims to enable autonomous robotic systems to operate more robustly and more reliably in the complex, cluttered and dynamic environments found in real-world applications. Applying the latest understanding of symmetry in non-linear systems and control provides tools that can be used to develop new design methodologies for spatial awareness algorithms. The outcomes of this project should increase Australia's ca ....Exploiting the Symmetry of Spatial Awareness for 21st Century Automation. This project aims to enable autonomous robotic systems to operate more robustly and more reliably in the complex, cluttered and dynamic environments found in real-world applications. Applying the latest understanding of symmetry in non-linear systems and control provides tools that can be used to develop new design methodologies for spatial awareness algorithms. The outcomes of this project should increase Australia's capacity in high-tech systems and deliver world best open source code for spatial awareness problems to enable the next generation of automation in Australia.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE130100885
Funder
Australian Research Council
Funding Amount
$374,723.00
Summary
Aerial robots contacting objects in dynamic environments. This project will allow small unmanned aerial vehicles to touch objects to perform tasks and to fly confidently in complex and cluttered environments where contact with surroundings is inevitable. This will enable robots to perform critical tasks such as servicing power lines, bridges and other elevated infrastructure.
Optimal design of controlled aerodynamic bodies: from concept to prototype. This interdisciplinary project will deliver technological advances in the areas of fluid dynamics, control systems and optimisation. It utilises advanced knowledge in these areas to design manoeuvrable aerodynamic bodies and will have a direct effect on Australian defence capability.
Visual guidance of flight in birds. Birds flying rapidly amidst the branches of trees engage continually in a three-dimensional slalom. This project will study birds flying through tunnels and gaps, to understand how they use their eyes and wings to achieve this agility. The results could suggest better designs for unmanned aerial vehicles operating in dense urban environments.