ARC Centre of Excellence - Vision Science. This Centre will generate important new knowledge of the performance, logic and stability of vision and visual behaviour. This knowledge will help reduce the burden of vision impairment in Australia, increasing productivity, promoting healthy ageing and reducing the community costs of visual impairment (ca. $9.85 billion in 2004). The knowledge produced will also make possible world-class innovations in robotics, leading to novel automated vision system ....ARC Centre of Excellence - Vision Science. This Centre will generate important new knowledge of the performance, logic and stability of vision and visual behaviour. This knowledge will help reduce the burden of vision impairment in Australia, increasing productivity, promoting healthy ageing and reducing the community costs of visual impairment (ca. $9.85 billion in 2004). The knowledge produced will also make possible world-class innovations in robotics, leading to novel automated vision systems with applications in industry and national security. Other knowledge will develop novel diagnostic technologies, for application in health delivery.Read moreRead less
Lifelong robotic navigation using visual perception. Service robots are becoming a major part of our working and personal environments, in much the same way as personal computers already have. This project will develop new methods of practical and useful robot navigation that will enable Australia's industries and services to remain internationally competitive.
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE160100090
Funder
Australian Research Council
Funding Amount
$250,000.00
Summary
Computational infrastructure for developing deep machine learning models. Computational infrastructure for developing deep machine learning models:
The computational infrastructure for developing deep machine learning models aims to enable new developments in machine learning of deep neural network models by providing the specialised computing necessary to train and evaluate the networks. In the last three years, deep networks have smashed previous performance ceilings for tasks such as object ....Computational infrastructure for developing deep machine learning models. Computational infrastructure for developing deep machine learning models:
The computational infrastructure for developing deep machine learning models aims to enable new developments in machine learning of deep neural network models by providing the specialised computing necessary to train and evaluate the networks. In the last three years, deep networks have smashed previous performance ceilings for tasks such as object recognition in images, speech recognition and automatic translation, bringing the prospect of machine intelligence closer than ever. Modern machine learning techniques have had huge impact in the last decade in fields such as robotics, computer vision and data analytics. The facility would enable Australian researchers to develop, learn and apply deep networks to problems of national importance in robotic vision and big data analytics. Read moreRead less
ARC Centre of Excellence for Robotic Vision. Robots are vital to Australia's future prosperity in the face of high relative wages, low or decreasing productivity, and impending labour shortages. However the work and workplaces of our most important industries are unstructured and changeable and current robots are challenged by their inability to quickly, safely and reliably "see" and "understand" what is around them. The Centre's research will create the fundamental science and technologies th ....ARC Centre of Excellence for Robotic Vision. Robots are vital to Australia's future prosperity in the face of high relative wages, low or decreasing productivity, and impending labour shortages. However the work and workplaces of our most important industries are unstructured and changeable and current robots are challenged by their inability to quickly, safely and reliably "see" and "understand" what is around them. The Centre's research will create the fundamental science and technologies that will allow robots to see as we do, and overcome the last barrier to the ubiquitous deployment of robots into society for the benefit of all.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE130100203
Funder
Australian Research Council
Funding Amount
$385,000.00
Summary
Autonomous benthic observing system. This project seeks to improve our ability to monitor marine habitats and characterise their variability by enhancing the Integrated Marine Observing system (IMOS) Autonomous Underwater Vehicle (AUV) Facility. The new AUV infrastructure will reduce operating costs, increase robustness of the sampling effort and insure continued operation for the next decade.
Image-based teleoperation of semi-autonomous robotic vehicles. This project will contribute strongly to Australia's robotic service industry in the development of semi-autonomous robotic inspection vehicles by; developing core technology in image-based teleoperation, training experts in the area, promoting the study of this topic within the Australian academia, and developing test facilities and prototype vehicles. Robotic inspection vehicles have the potential to replace direct human presence i ....Image-based teleoperation of semi-autonomous robotic vehicles. This project will contribute strongly to Australia's robotic service industry in the development of semi-autonomous robotic inspection vehicles by; developing core technology in image-based teleoperation, training experts in the area, promoting the study of this topic within the Australian academia, and developing test facilities and prototype vehicles. Robotic inspection vehicles have the potential to replace direct human presence in difficult, dangerous or simply uncomfortable inspection tasks such as; inspection of industrial pressure vessels, piping and conduits in factories or mines, undersea cabling, inspection of bridges, dams and other large scale civil buildings, amongst many other possibilities.Read moreRead less