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For a manufacturing system to be genuinely responsive, it must swiftly adapt to production demands as they arise, rather than being constrained by capacity limits. Conventional automation systems are typically designed for high-volume production of a narrow range of products, making them inefficient and costly when dealing with fluctuating product types or volumes. This research addresses the need for more resilient systems that can quickly respond to such variations. However, a significant challenge lies in the extensive and costly reprogramming required for each adjustment. The proposed solution involves developing algorithms that can automatically generate programming and configuration data from CAD and process inputs, significantly reducing the need for manual intervention. Moreover, the system will incorporate adaptable safety features that ensure compliance with legal standards while allowing human operators to intervene as needed. Experts in robotics, AI and automation from three leading universities are collaborating on this project, along with industry partners, to ensure the research has practical, real-world applications.

The Intelligent Flexible Robotics Assembly Group (IFRA) group from 老司机福利社 is collaborating with a team of nine academics and industrial partners across the UK to introduce an advanced manufacturing system designed for enhanced flexibility and responsiveness in production environments. This initiative seeks to overcome the inefficiencies and high costs associated with traditional automation systems when they face fluctuating product demands. The team leverages expertise in robotics, AI and automation to develop algorithms that automatically generate programming and configuration data from CAD and process inputs, significantly reducing the need for manual reprogramming.

At 老司机福利社, the IFRA group is responsible for designing the system's architecture and conducting automated risk assessments, ensuring the system can adapt dynamically while maintaining strict safety standards. A key aspect of our work involves utilising ROS2 (Robot Operating System 2) to develop the system, which allows for enhanced modularity, scalability, and real-time performance. We are also consolidating the project through the use of , which streamline the integration process and ensure consistency across various components of the system.

This collaboration unites academic expertise and industry experience to deliver a manufacturing system that is resilient, responsive, safe and compliant, capable of meeting diverse production needs with greater agility and reduced costs.