Completed Engineering Materials & Manufacturing

Reconfigurable robotics for responsive manufacture - R3M

In plain English

AI plain-English summary

A factory robot that can be told what to do without a human programmer writing new code for every product change would transform how quickly manufacturing lines can switch between different jobs. Conventional automation cells are built for high-volume production of a single product type. When demand fluctuates or product types vary, the fixed setup becomes inefficient and costly. Physically repositioning robots is relatively straightforward, but the real bottleneck is the time-consuming and expensive reprogramming needed for each change. This project tackles that barrier by developing algorithms that automatically generate programme and configuration data directly from CAD and process data, eliminating the need for significant human input. If successful, the system would allow manufacturers to respond rapidly to shifts in demand—switching production between different products in hours rather than weeks. The research also addresses safety, automatically configuring systems to be legally compliant while still allowing human operators to intervene actively. This could make supply chains more resilient, reduce waste from overproduction, and lower the cost of manufacturing small batches of specialised goods. The work brings together experts in robotics, AI, and control from three universities, with input from end users to ensure real-world relevance.

View original technical description
To be truly responsive, a manufacturing system should be able to rapidly adapt to what the production need is at a specific time, depending on demand rather than on capacity. Conventional automation cells tend be fixed and specifically designed to manufacture a single or limited number of products in large volumes. However, where product volumes or types are highly variable this approach is inefficient and costly. More resilient approaches that can rapidly adapt to variations in product quantity and type are of great interest and a significant quantity of work has been carried out to realise this concept. However, whilst physical reconfiguration, ie. the positioning of robots and process systems, is relatively easy to achieve, the major barrier is the need for time consuming and costly reprogramming to support each change. The research we propose will take a more holistic view of the reconfiguration process and develop new algorithms that can automatically generate programme and configuration data from CAD and process data eliminating the need for significant human input. Furthermore the system will also consider safety and how to automatically configure the safety system so that it is safe and legally compliant but also implement a flexible framework that allows the active intervention of human operators. Within the research we bring together experts in Robotics, AI and Control and Automation from three leading Universities to work together and develop game changing approaches to resilience in manufacturing. We will also engage with a number of end users and suppliers to ensure that the developed science has real world relevance and is aligned with realistic industrial challenges.

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Researchers

Ashutosh Tiwari (Co-Investigator)Lloyd Tinkler (Co-Investigator)Niels Lohse (Co-Investigator)Pedro Ferreira (Co-Investigator)Philip Webb (Principal Investigator)Seemal Asif (Co-Investigator)

Related Research

Grants with similar aims, by meaning.

Industrial Robots-as-a-Service (IRaaS) - Resilient and responsive manufacturing systems enabled by rapidly deployable mobile robots
Responsive Additive Manufacture to Overcome Natural and Attack-based disruption (RAMONA)
Self-Resilient Reconfigurable Assembly Systems with In-process Quality Improvement
Nimble Artificial Intelligence driven robotic solutions for efficient and self-determined handling and assembly operations
Applied Off-site and On-site Collective Multi-Robot Autonomous Building Manufacturing

Original classification

Research Grant

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