Foundations of Vision Based Control of Robotic Vehicles. Automated and partially automated robotic vehicles are an emerging technology in society. The safety and performance of such systems depends crucially on their sensing and control algorithms. Vision sensing is one of the few sensor modalities that has the potential to adequately represent the complexity of a real world environment. By providing simple and effective vision based control algorithms this project develops Frontier Technologi ....Foundations of Vision Based Control of Robotic Vehicles. Automated and partially automated robotic vehicles are an emerging technology in society. The safety and performance of such systems depends crucially on their sensing and control algorithms. Vision sensing is one of the few sensor modalities that has the potential to adequately represent the complexity of a real world environment. By providing simple and effective vision based control algorithms this project develops Frontier Technologies for Building and Transforming Australian Industries by enabling a wide range of robotic vehicle applications, including aerial, submersible, and wheeled vehicles.Read moreRead less
Autonomous Functions for Smart Cars. The aim of this project is to develop autonomous functions for smart cars, such as lane departure warning, driver fatigue warning, and automatic lane following. Every year 70,000 people are killed in road accidents, 95% of which can be attributed to driver error. The potential outcomes of this project therefore significant. Many of the theoretical methods required for this project have been developed by our group. However, further theoretical refinements fo ....Autonomous Functions for Smart Cars. The aim of this project is to develop autonomous functions for smart cars, such as lane departure warning, driver fatigue warning, and automatic lane following. Every year 70,000 people are killed in road accidents, 95% of which can be attributed to driver error. The potential outcomes of this project therefore significant. Many of the theoretical methods required for this project have been developed by our group. However, further theoretical refinements followed by experimental verification is necessary. For smart cars to be accepted, the systems must be demonstrated to be reliable and to operate in a wide range of conditions.Read moreRead less
Vision Based Guidance, Navigation and Control of a Tail-Sitter Unmanned Aerial Vehicle. The development of a high precision visual guidance system for vertical takeoff and landing UAVs will significantly enhance their operational effectiveness by allowing them to land accurately on the back of small vessels or in confined clearings. Together with the extra navigation-system redundancy vis-a-vis GPS system failure and the ability to self-identify reasonable emergency landing sites, the proposed v ....Vision Based Guidance, Navigation and Control of a Tail-Sitter Unmanned Aerial Vehicle. The development of a high precision visual guidance system for vertical takeoff and landing UAVs will significantly enhance their operational effectiveness by allowing them to land accurately on the back of small vessels or in confined clearings. Together with the extra navigation-system redundancy vis-a-vis GPS system failure and the ability to self-identify reasonable emergency landing sites, the proposed vision-based system represents a significant capability improvement over what is currently available. It will thus enhance the ability of defence and civil-defence units to patrol Australian borders effectively and to react to threats. It will also have significant export potential to allied nations.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.
Cost-effective autonomous systems for large scale monitoring of marine protected areas. This project seeks to develop autonomous technologies that will enable Australia's expanding system of marine protected areas (MPAs) to more effectively preserve marine biodiversity and long term ecosystem function through scalable, cost-effective monitoring. Monitoring will enable MPA managers to assess whether preservation and sustainability goals are being met and to adapt zoning policies in response. Au ....Cost-effective autonomous systems for large scale monitoring of marine protected areas. This project seeks to develop autonomous technologies that will enable Australia's expanding system of marine protected areas (MPAs) to more effectively preserve marine biodiversity and long term ecosystem function through scalable, cost-effective monitoring. Monitoring will enable MPA managers to assess whether preservation and sustainability goals are being met and to adapt zoning policies in response. Australia will benefit from informed sustainable use policies that preserve the marine environment for future generations without unnecessarily restricting recreational and commercial access today.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE120100995
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Visual navigation for sunny summer days and stormy winter nights. This project will develop innovative techniques for camera-based navigation that recognise locations under a wide range of environmental conditions caused by day-night cycles, weather and seasonal change. These techniques will enable the widespread use of cheap and lightweight cameras in robot and personal navigation systems.
Learning Robotic Navigation and Interaction from Object-based Semantic Maps. Our project aims to develop new learning algorithms that enable robots to perform high-complexity tasks that are currently impossible. Compared to existing methods that rely on low-level sensor data, we aim to achieve this by learning from a high-level graph representation of the environment that captures semantics, affordances, and geometry. The outcome would be robots capable of using human instructions to efficiently ....Learning Robotic Navigation and Interaction from Object-based Semantic Maps. Our project aims to develop new learning algorithms that enable robots to perform high-complexity tasks that are currently impossible. Compared to existing methods that rely on low-level sensor data, we aim to achieve this by learning from a high-level graph representation of the environment that captures semantics, affordances, and geometry. The outcome would be robots capable of using human instructions to efficiently learn complex interaction and navigation behaviours that transfer to unseen environments. Our research should benefit new applications in domains of economic and societal importance that are currently too complex, unsafe, and uncertain for robot assistants, such as aged care, advanced manufacturing and domestic robotics.Read moreRead less
Active Visual Navigation in an Unexplored Environment. This project will develop a new method for robotic navigation in which goals can be specified at a much higher level of abstraction than has previously been possible. This will be achieved using deep learning to make informed predictions about a scene layout, and navigating as an active observer in which the predictions informs actions. The outcome will be robotic agents capable of effective and efficient navigation and operation in previous ....Active Visual Navigation in an Unexplored Environment. This project will develop a new method for robotic navigation in which goals can be specified at a much higher level of abstraction than has previously been possible. This will be achieved using deep learning to make informed predictions about a scene layout, and navigating as an active observer in which the predictions informs actions. The outcome will be robotic agents capable of effective and efficient navigation and operation in previously unseen environments, and the ability to control such agents with more human-like instructions. Such capabilities are desirable, and in some cases essential, for autonomous robots in a variety of important application areas including automated warehousing and high-level control of autonomous vehicles. 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
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