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
Insect-inspired flapping wing robots: autonomous flight control systems. This project aims to design a novel control scheme for insect-inspired, flapping-wing, micro aerial vehicles. This type of micro aerial vehicle has complex, periodic, time-varying and inherently unstable dynamics, which are practically challenging to model and implement in hardware. This project will design energy-based automatic stabilization and task-dependent control, and develop the insect-inspired platform for testing ....Insect-inspired flapping wing robots: autonomous flight control systems. This project aims to design a novel control scheme for insect-inspired, flapping-wing, micro aerial vehicles. This type of micro aerial vehicle has complex, periodic, time-varying and inherently unstable dynamics, which are practically challenging to model and implement in hardware. This project will design energy-based automatic stabilization and task-dependent control, and develop the insect-inspired platform for testing nonlinear control strategies. The expected outcomes will include new system and control theories, concepts, principles and technologies in controller design that can provide reliable flight control for bio-inspired, flapping-wing systems.Read moreRead less
Fault detection and identification in nonlinear complex systems. Complex systems usually comprise a large number of inter-dependent subsystems linked together to perform a certain task. Examples of such systems are power systems, irrigation systems, air traffic control systems, to name a few. Such systems are subject to component failure or malfunction. Total failure can cause an unacceptable financial losses and/or danger to personnel. It is therefore extremely essential, from economic and safe ....Fault detection and identification in nonlinear complex systems. Complex systems usually comprise a large number of inter-dependent subsystems linked together to perform a certain task. Examples of such systems are power systems, irrigation systems, air traffic control systems, to name a few. Such systems are subject to component failure or malfunction. Total failure can cause an unacceptable financial losses and/or danger to personnel. It is therefore extremely essential, from economic and safety view points, that a way be found to ensure reliable and viable operation of complex plants. A first step in achieving this goal is to detect faults on-line and in real-time when they occur and identify their location and characteristics, which is the aim of this project.Read moreRead less