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
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
Disassembly Automation of End-of-Life Electric Vehicle Batteries. This project aims to develop an automated disassembly solution for End-of-Life (EOL) Electric Vehicle (EV) batteries, which is flexible and modular to handle the uncertainties associated with model changes, condition of the EOL battery packs as well as the projected volume growth. The outcome of this project will lead to a better separation of EV battery components and materials. This will allow recycling of EOL EV batteries with ....Disassembly Automation of End-of-Life Electric Vehicle Batteries. This project aims to develop an automated disassembly solution for End-of-Life (EOL) Electric Vehicle (EV) batteries, which is flexible and modular to handle the uncertainties associated with model changes, condition of the EOL battery packs as well as the projected volume growth. The outcome of this project will lead to a better separation of EV battery components and materials. This will allow recycling of EOL EV batteries with a higher material recovery efficiency and a lower cost due to the significantly reduced labor cost; hence substantially reduce the environmental footprint associated with EOL treatment of these batteries.Read moreRead less
Industrial Transformation Research Hubs - Grant ID: IH230100013
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
ARC Research Hub for Future Digital Manufacturing. This Hub aims to grow and accelerate Australian digital manufacturing (DM) transformation by devising novel DM technology and commercialisation/adoption pathways. The Hub expects to transform industry by developing novel AI and IoT-powered DM technology that provides for dramatic improvement in manufacturing productivity, resilience and competitiveness. Expected outcomes include novel DM technology for digitally representing, predicting, and imp ....ARC Research Hub for Future Digital Manufacturing. This Hub aims to grow and accelerate Australian digital manufacturing (DM) transformation by devising novel DM technology and commercialisation/adoption pathways. The Hub expects to transform industry by developing novel AI and IoT-powered DM technology that provides for dramatic improvement in manufacturing productivity, resilience and competitiveness. Expected outcomes include novel DM technology for digitally representing, predicting, and improving production and its outcomes via an open platform that supports reusing industry co-created DM solutions. Through supporting advanced manufacturing priorities and Industry 4.0, the Hub should provide significant benefits by increasing Australian manufacturing productivity and resilience by 30%.Read moreRead less
Non-invasive and safe human-machine interface (HMI) systems . This project aims to establish novel non-invasive human-machine interface systems based on multi-modal sensing and machine learning to intuitively command and control robotic and autonomous systems safely interacting and cooperating with humans. This will be achieved by harnessing the synergies across design optimisation, multi-modal sensing, additive manufacturing, machine learning, and assistive and cooperative robotic devices. Expe ....Non-invasive and safe human-machine interface (HMI) systems . This project aims to establish novel non-invasive human-machine interface systems based on multi-modal sensing and machine learning to intuitively command and control robotic and autonomous systems safely interacting and cooperating with humans. This will be achieved by harnessing the synergies across design optimisation, multi-modal sensing, additive manufacturing, machine learning, and assistive and cooperative robotic devices. Expected outcomes are a novel human-machine interface methodology, a new multi-purpose wearable data glove, and function and application-specific machine learning methods for cutting-edge applications in assistive robotic devices such as a prosthetic hand, advanced manufacturing, construction and agriculture.Read moreRead less
Visual methods for advanced automation of underwater manipulation. This project will increase the autonomy of underwater robotic systems engaged in intervention and inspection tasks. Such activities are essential for the operation of subsea robotic systems used in offshore industries, scientific exploration and defence. Our approach will improve perception and situational awareness through the principled fusion of multiple navigation and camera sensors. We will use this improved scene understand ....Visual methods for advanced automation of underwater manipulation. This project will increase the autonomy of underwater robotic systems engaged in intervention and inspection tasks. Such activities are essential for the operation of subsea robotic systems used in offshore industries, scientific exploration and defence. Our approach will improve perception and situational awareness through the principled fusion of multiple navigation and camera sensors. We will use this improved scene understanding to effectively plan the motion of vehicles and manipulators through larger and more complex workspaces, enabling semi-supervised and autonomous task execution. Our project will demonstrate these capabilities in real-world deployments relevant to industry and marine science.Read moreRead less
Swarm construction: ant-inspired processes for teams of building robots. Construction and manufacturing can be dangerous, wasteful industries—prime candidates for automation by teams of mobile robot builders. However, our understanding of how to program robots for teamwork is limited. This project aims to understand how colonies of weaver ants build complex nest structures, using novel 3D-imaging and ant tracking techniques. The anticipated outcomes of the project are i) a framework for how indi ....Swarm construction: ant-inspired processes for teams of building robots. Construction and manufacturing can be dangerous, wasteful industries—prime candidates for automation by teams of mobile robot builders. However, our understanding of how to program robots for teamwork is limited. This project aims to understand how colonies of weaver ants build complex nest structures, using novel 3D-imaging and ant tracking techniques. The anticipated outcomes of the project are i) a framework for how individual-level behaviour drives structure-level outcomes, applicable to many complex systems, and ii) novel software and hardware for robot swarms that can 3D-print structures using ant inspired teamwork strategies. Benefits of the project include new construction technologies that are safer, greener, cheaper and faster.Read moreRead less
A System Behavioral Approach to Big Data-driven Nonlinear Process Control. This project aims to develop a novel process control approach that utilises big process data to improve the cost-effectiveness of industrial processes. Existing monitoring systems in the industry have been collecting a tremendous amount of process operation data but little effort has been made to use the big process data to enhance process operations. Based on the system behavioural approach and dissipativity theory, inte ....A System Behavioral Approach to Big Data-driven Nonlinear Process Control. This project aims to develop a novel process control approach that utilises big process data to improve the cost-effectiveness of industrial processes. Existing monitoring systems in the industry have been collecting a tremendous amount of process operation data but little effort has been made to use the big process data to enhance process operations. Based on the system behavioural approach and dissipativity theory, integrated with machine learning techniques, this project expects to develop a novel framework for data-driven control using big process data. The outcomes are expected to benefit the Australian process industry, where many processes are controlled by inadequate logic controllers, by improving their operational efficiency.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC160100040
Funder
Australian Research Council
Funding Amount
$3,815,143.00
Summary
ARC Training Centre for Automated Manufacture of Advanced Composites. ARC Training Centre for Automated Manufacture of Advanced Composites. This centre aims to develop innovative researchers who can transform Australia’s high-performance carbon composites manufacturing industry. This aim will be achieved through the adoption and creative use of advanced automation technology, which brings benefits of speed, flexibility and accuracy. Industry-based research experience will be enhanced through exp ....ARC Training Centre for Automated Manufacture of Advanced Composites. ARC Training Centre for Automated Manufacture of Advanced Composites. This centre aims to develop innovative researchers who can transform Australia’s high-performance carbon composites manufacturing industry. This aim will be achieved through the adoption and creative use of advanced automation technology, which brings benefits of speed, flexibility and accuracy. Industry-based research experience will be enhanced through exposure to international partners at the cutting edge of advanced composites manufacturing research and development in developed economies. The intended outcome is a generation of innovators who can use the benefits of automation to position Australian manufacturers as world-class agile producers of high-value advanced composite structures using high-rate, error-free processes.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