Discovery Early Career Researcher Award - Grant ID: DE210101864
Funder
Australian Research Council
Funding Amount
$442,500.00
Summary
Unlocking Urban Airspace for Drone Transport. This project aims to accurately quantify the mid-air collision risk associated with low-altitude unmanned operations in urban airspace through the creation of new data-driven collision risk modelling techniques. Without such techniques, drone operations remain suppressed so their true potential cannot be realised. The collision risk models address this by providing the key missing knowledge that can underpin/enable vital unmanned traffic management ....Unlocking Urban Airspace for Drone Transport. This project aims to accurately quantify the mid-air collision risk associated with low-altitude unmanned operations in urban airspace through the creation of new data-driven collision risk modelling techniques. Without such techniques, drone operations remain suppressed so their true potential cannot be realised. The collision risk models address this by providing the key missing knowledge that can underpin/enable vital unmanned traffic management applications, including airspace design and the development of separation standards. This can ultimately enable greater access to urban airspace without compromising air safety such that we unlock the commercial and societal benefits of drone use and help modernise urban air transportation.
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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 proje ....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.Read moreRead less
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. This project expects to generate new knowledge in UAV navigation using an innovative approach by combining Simultaneous Localizatio ....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. This project expects to generate new knowledge in UAV navigation using an innovative approach by combining Simultaneous Localization and Mapping (SLAM) algorithms with Partially Observable Markov Decision Processes (POMDP) and Deep Reinforcement learning. This should provide significant benefits, such as more responsive search and rescue inside collapsed buildings or underground mines, as well as fast target detection and mapping under the tree canopy. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE120100802
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Developing novel concepts for improved safety in aircraft emergency situations. The outcomes of this project will enable the creation of an emergency system that can improve visual situation awareness in emergency landing scenarios by investigating novel detection, control and planning algorithms. The project will contribute significantly to Australia's share in technologies for aircraft automation.
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.
Nonlinear frequency mixing methods for materials and damage evaluation. This project aims to investigate new approaches for frequency mixing in nonlinear ultrasonics, and to demonstrate their potential for the non-destructive evaluation of material degradation and early damage detection. The anticipated outcomes will be increased detection sensitivity relative to current inspection techniques and an enhanced capability for quantifying the damage. This will provide the basis for more cost efficie ....Nonlinear frequency mixing methods for materials and damage evaluation. This project aims to investigate new approaches for frequency mixing in nonlinear ultrasonics, and to demonstrate their potential for the non-destructive evaluation of material degradation and early damage detection. The anticipated outcomes will be increased detection sensitivity relative to current inspection techniques and an enhanced capability for quantifying the damage. This will provide the basis for more cost efficient safety management of high-value assets and infrastructure, and for enhancing Australia’s competitiveness in advanced manufacturing.Read moreRead less