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Many industrial manufacturing companies are driven by the desire for automation and smart manufacturing into the fourth industrial revolution – also referred to as Industry 4.0 – through automated and digital process cutting-edge technology such as the Industrial Internet of Things. Also, at the forefront of robotics is the idea of a robot capable of safe, collaborative working with operators to perform tasks across the process.
This research aims to study and develop an algorithm for human-robot learning control for collaborative output tasks. Such human-robot learning control needs to satisfy two cases:
• The desired output is directly available to the robot
• The robot infers the desired output from the human-achieved output
Therefore, the second challenge for this research is to develop a secure methodology to systematically digitalise a process that is typically very heavily operator dependent. It will be necessary to design robust, safe and secure hardware and software modules, which can be applied to the process to facilitate productivity improvements via the use of robotics. The overall objective is to develop an automated and repeatable digitalised process for the manufacture of safety critical components.
Developing a framework around capturing process data, part geometry and handling requirements, securely transferring this data to a service platform (i.e., a Cloud), and then performing data analytics to correlate part performance will be required. This will enable manufacturing companies to minimise downtime, reduce human – process errors, and decrease maintenance costs. Implementation of the framework will result in a more competitive market by providing more efficient solutions to the customer.
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