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Australian State/Territory : VIC
Field of Research : Control engineering
Research Topic : Intelligent Robotics
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  • Active Funded Activity

    Discovery Projects - Grant ID: DP240100938

    Funder
    Australian Research Council
    Funding Amount
    $509,205.00
    Summary
    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.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP240101140

    Funder
    Australian Research Council
    Funding Amount
    $518,252.00
    Summary
    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.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP230101107

    Funder
    Australian Research Council
    Funding Amount
    $470,740.00
    Summary
    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.
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