Industrial Transformation Training Centres - Grant ID: IC200100001
Funder
Australian Research Council
Funding Amount
$4,879,415.00
Summary
ARC Training Centre for Collaborative Robotics in Advanced Manufacturing. The Centre aims to build the human and technical capability Australia needs to underpin our global competitiveness in advanced manufacturing. The Centre will unite manufacturing businesses, including SMEs, and universities to develop collaborative robotics applications which combine the strengths of humans and robots in shared work environments. The Centre will train researchers, engineers, technologists and manufacturing ....ARC Training Centre for Collaborative Robotics in Advanced Manufacturing. The Centre aims to build the human and technical capability Australia needs to underpin our global competitiveness in advanced manufacturing. The Centre will unite manufacturing businesses, including SMEs, and universities to develop collaborative robotics applications which combine the strengths of humans and robots in shared work environments. The Centre will train researchers, engineers, technologists and manufacturing leaders with the expertise industry needs to boost safety, quality assurance, production efficiency, and workforce readiness. The intended outcome is to support Australian manufacturers to shift toward higher-potential markets, compete globally and attract and retain a digitally-capable workforce for the future.Read moreRead less
Repetitive control systems in networked environments. Repetitive control is used in many industry applications to track periodic references and reject periodic disturbances. The development of digital technology brings in more networked control systems, greatly improving distributed manufacturing, which creates new design challenges due to network-induced constraints such as delay, data packet dropouts and cyber-attacks. This project aims to provide new understanding of dynamic behaviours of rep ....Repetitive control systems in networked environments. Repetitive control is used in many industry applications to track periodic references and reject periodic disturbances. The development of digital technology brings in more networked control systems, greatly improving distributed manufacturing, which creates new design challenges due to network-induced constraints such as delay, data packet dropouts and cyber-attacks. This project aims to provide new understanding of dynamic behaviours of repetitive control systems in networked environments, and develop a new theory for the analysis and design of networked repetitive control, subject to network induced constraints. The new control systems should reduce the cost of automation systems and will significantly enhance their performance, allowing Australian industry to remain economically competitive.Read moreRead less
High yield adaptive laser nanomanufacturing system for photonic devices. This project aims at developing an entirely new nanofabrication platform combining adaptive beamshaping with highly accurate large area nanopositioner to simultaneously address the throughput and accuracy challenges in nanomanufacturing. The proposed prototype system and fabricated photonic chips have performance far surpassing the state-of-the-art. Through trial in the industrial best laser nanofabrication system, commerci ....High yield adaptive laser nanomanufacturing system for photonic devices. This project aims at developing an entirely new nanofabrication platform combining adaptive beamshaping with highly accurate large area nanopositioner to simultaneously address the throughput and accuracy challenges in nanomanufacturing. The proposed prototype system and fabricated photonic chips have performance far surpassing the state-of-the-art. Through trial in the industrial best laser nanofabrication system, commercial benefits can be fast tracked for Australian industry in the rapidly expanding nanomanufacturing field. The outcomes lead to a platform technology enabling broad impact and benefits to other high-tech applications requiring high precision and throughput, enhancing Australia’s leading position in advanced manufacturing.Read moreRead less
Muscle-based Signals for Responsive Physically-Assistive Robotics. This project aims to develop a physically assistive robot for industrial use that interprets signals from the human user’s muscles during a physical activity and responds with appropriate assistance. This is significant because the robot must accommodate the complexity of movement required in industrial settings and adapt to variabilities in muscle activation signals among users that also change in time. The expected research out ....Muscle-based Signals for Responsive Physically-Assistive Robotics. This project aims to develop a physically assistive robot for industrial use that interprets signals from the human user’s muscles during a physical activity and responds with appropriate assistance. This is significant because the robot must accommodate the complexity of movement required in industrial settings and adapt to variabilities in muscle activation signals among users that also change in time. The expected research outcome is an intuitive, assistive robot worn by the human workforce that enhances their productivity and longevity, improves working conditions, lowers production costs, and increases workforce resilience. The robot’s capabilities will be demonstrated in this project through the challenging activity of sheep shearing.Read moreRead less
Human-Robot Co-Evolution: Achieving the full potential of future workplaces. Physical human-robot systems are widely used to amplify the capability of human labourers and improve ergonomics in the workplace. This project aims to develop robot controllers that shape the co-evolution of these systems. Through physical human-robot interaction studies it will generate new knowledge of how humans adapt to working with robots, which will then be incorporated into the robot controller design. Expected ....Human-Robot Co-Evolution: Achieving the full potential of future workplaces. Physical human-robot systems are widely used to amplify the capability of human labourers and improve ergonomics in the workplace. This project aims to develop robot controllers that shape the co-evolution of these systems. Through physical human-robot interaction studies it will generate new knowledge of how humans adapt to working with robots, which will then be incorporated into the robot controller design. Expected outcomes include a better understanding of human adaptation and a systematic approach to shaping human-robot interaction over time. This should provide significant benefits across different skill and labour-intensive industries in Australia, such as improved worker productivity and safer human-robot collaboration.Read moreRead less
Characterization, modelling and control for robotic thermal ablation. This project aims to study the fundamental issues in robotic-assisted minimally invasive thermal ablation, an important therapy for patients with cancer. It aims to establish advanced characterisation and modelling methodologies for thermomechanical behaviours of soft tissues, together with automatic planning and precise manipulation control techniques for robotic thermal ablation therapies. The project will establish new know ....Characterization, modelling and control for robotic thermal ablation. This project aims to study the fundamental issues in robotic-assisted minimally invasive thermal ablation, an important therapy for patients with cancer. It aims to establish advanced characterisation and modelling methodologies for thermomechanical behaviours of soft tissues, together with automatic planning and precise manipulation control techniques for robotic thermal ablation therapies. The project will establish new knowledge and instrumentation for robotic-assisted thermal ablation and minimally invasive surgery. The outcomes of this project will produce important benefits to medicine, healthcare and medical technology industry, and further consolidate Australia’s position in innovative technologies and research and development of advanced healthcare systems and instruments.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
Accelerated Finite-time Learning and Control in Cyber-Physical Systems. Efficient learning and control in cyber-physical systems such as smart grids and robotic systems are very important for achieving economic and social benefits. This project aims to establish a breakthrough accelerated finite-time dynamics theory and technology to assist in delivering efficient learning and control. Expected outcomes include new distributed accelerated finite-time dynamics based learning and control algorithm ....Accelerated Finite-time Learning and Control in Cyber-Physical Systems. Efficient learning and control in cyber-physical systems such as smart grids and robotic systems are very important for achieving economic and social benefits. This project aims to establish a breakthrough accelerated finite-time dynamics theory and technology to assist in delivering efficient learning and control. Expected outcomes include new distributed accelerated finite-time dynamics based learning and control algorithms and tools for optimal operations in cyber-physical systems. This should provide significant benefits including a practical technology for industry applications in smart grids and robotic systems, and training of the next generation engineers in this technology for Australia.Read moreRead less
Human models for accelerated robot learning and human-robot interaction. This project aims to develop novel approaches to teach robots to proficiently interact with humans in a safe and low-cost manner. To achieve this aim, this project will develop novel models from which various human behaviours can be generated and used to train human-robot interaction policies in simulation. Expected outcomes of this project include new computational models of human behaviour built using cognitive science th ....Human models for accelerated robot learning and human-robot interaction. This project aims to develop novel approaches to teach robots to proficiently interact with humans in a safe and low-cost manner. To achieve this aim, this project will develop novel models from which various human behaviours can be generated and used to train human-robot interaction policies in simulation. Expected outcomes of this project include new computational models of human behaviour built using cognitive science theories and limited data and new training schemes for robot learning in simulation. By training robots in simulation with accurate human models, this research will enable fast and safe robot training to support the deployment and adoption of robots in human contexts such as healthcare facilities, homes, and workplaces.Read moreRead less
Self-supervised feature learning for rapid processing of marine imagery. Fast and reliable quantitative estimates of marine environmental health are needed for scientific studies, design and management of protected areas, and regulatory compliance of industrial activity in the ocean. Australia is collecting seafloor images at increasing rates but expert annotations are not keeping up, meaning that typical machine learning approaches struggle. This project will develop self-supervised techniques ....Self-supervised feature learning for rapid processing of marine imagery. Fast and reliable quantitative estimates of marine environmental health are needed for scientific studies, design and management of protected areas, and regulatory compliance of industrial activity in the ocean. Australia is collecting seafloor images at increasing rates but expert annotations are not keeping up, meaning that typical machine learning approaches struggle. This project will develop self-supervised techniques that use large amounts of unlabeled data to enhance performance. Our design takes advantage of additional information available for marine imagery such as geolocation and remote sensing context. We will explore how these representations can guide additional sampling and improve performance in classification tasks.Read moreRead less