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
Micromanipulation system. Many frontier areas such as micromanufacturing, microsurgery, biotechnology, and nanotechnology require high precision micromanipulation systems. This project aims to investigate fundamental issues in micromanipulation systems using an ARC-LIEF funded research facility, and establish methodologies for modelling and analysis, together with their experimental verification to evaluate the influence of various parameters in such systems. The findings will be utilised to e ....Micromanipulation system. Many frontier areas such as micromanufacturing, microsurgery, biotechnology, and nanotechnology require high precision micromanipulation systems. This project aims to investigate fundamental issues in micromanipulation systems using an ARC-LIEF funded research facility, and establish methodologies for modelling and analysis, together with their experimental verification to evaluate the influence of various parameters in such systems. The findings will be utilised to establish sensory-based control techniques to solve problems associated with predictability, control, and efficiency for future advancement of such novel systems. The outcomes will include acquiring new knowledge in micromanipulation systems for potential utilization of the innovative concepts in the frontier areas.Read moreRead less
Haptic exploration and manipulation of micro/nano scale environment. The proposed research is novel and innovative in character and it has potential benefits in many frontier areas utilising micro/nano manipulation systems. These include micromanufacturing and instrumentation, microbiology, microsurgery and nanotechnology. The outcomes of this project will add to the growth of world-class Australian engineering science, and consolidate Australia's position in innovative technologies and internat ....Haptic exploration and manipulation of micro/nano scale environment. The proposed research is novel and innovative in character and it has potential benefits in many frontier areas utilising micro/nano manipulation systems. These include micromanufacturing and instrumentation, microbiology, microsurgery and nanotechnology. The outcomes of this project will add to the growth of world-class Australian engineering science, and consolidate Australia's position in innovative technologies and international R&D. This highly challenging project will provide training for postdoctorate researchers, postgraduate and honours students. These researchers will gain expertise in many areas including micro/nano manipulation, sensing and control, system design and analysis, virtual reality and experimental techniques.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
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
Special Research Initiatives - Grant ID: SR0354703
Funder
Australian Research Council
Funding Amount
$20,000.00
Summary
Robotics Research Network (RRN). The RRN brings together all the best robotics research groups in Australia with the aim of fostering and coordinating cooperative research. The RRN integrates researchers from fields including machine perception, sensing, control, artificial intelligence and mechatronics. The RRN includes representation from twelve Universities, CSIRO and involvement of four ARC Centres. Programmes are proposed to share research facilities, to support training of research personn ....Robotics Research Network (RRN). The RRN brings together all the best robotics research groups in Australia with the aim of fostering and coordinating cooperative research. The RRN integrates researchers from fields including machine perception, sensing, control, artificial intelligence and mechatronics. The RRN includes representation from twelve Universities, CSIRO and involvement of four ARC Centres. Programmes are proposed to share research facilities, to support training of research personnel and promote cooperation in international research programmes. Robotics is already having a substantial impact in industries such as mining and agriculture. Robotics will, in future, offer benefits in areas such as health care, building systems, and defence.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