Innovative X-by-Wire Control Systems for Improved Vehicle Manoeuvrability and Stability. Future automobiles will be equipped with safety-critical ‘x-by-wire’ systems, such as ‘steer-by-wire’, ‘brake-by-wire’, and ‘drive-by-wire’, to enable active safety control and improve reliability and performance. This project aims to develop a new coordinated control strategy based on an in-depth understanding of the fundamental dynamics and stability characteristics of vehicles. Corresponding x-by-wire sys ....Innovative X-by-Wire Control Systems for Improved Vehicle Manoeuvrability and Stability. Future automobiles will be equipped with safety-critical ‘x-by-wire’ systems, such as ‘steer-by-wire’, ‘brake-by-wire’, and ‘drive-by-wire’, to enable active safety control and improve reliability and performance. This project aims to develop a new coordinated control strategy based on an in-depth understanding of the fundamental dynamics and stability characteristics of vehicles. Corresponding x-by-wire systems will then be implemented, using a novel networked bilateral-control concept and new haptic devices for enhancing the overall performance and safety of vehicles. This project will lead to the innovative design of vehicle active safety systems for automobile manufacturing in Australia and the rest of the world.Read moreRead less
Context-aware verification and validation framework for autonomous driving. This project aims to enhance the reliability and safety of emerging self-driving vehicles, through a framework that supports the validation and verification of autonomous driving systems. This project expects to generate new knowledge in areas of software engineering, intelligent transport, and machine learning, using a multi-disciplinary research combining expertise from various fields. Expected outcomes of this project ....Context-aware verification and validation framework for autonomous driving. This project aims to enhance the reliability and safety of emerging self-driving vehicles, through a framework that supports the validation and verification of autonomous driving systems. This project expects to generate new knowledge in areas of software engineering, intelligent transport, and machine learning, using a multi-disciplinary research combining expertise from various fields. Expected outcomes of this project are a family of new context-aware techniques to verify and validate complex behaviours in autonomous driving. This should provide significant benefits, such as safe autonomous driving systems and the improved journey experience and security for road users.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.
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