Reconfigurable robotics for responsive manufacture - R3M
Lead Research Organisation:
CRANFIELD UNIVERSITY
Department Name: Faculty of Engineering & Applied Science
Abstract
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.
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.
Publications
Asif S
(2023)
Managing Delays for Realtime Error Correction and Compensation of an Industrial Robot in an Open Network
in Machines
Asif S
(2025)
Rapid and automated configuration of robot manufacturing cells
in Robotics and Computer-Integrated Manufacturing
Shrinah A
(2024)
On the Design of Human-Robot Collaboration Gestures
Xu Z
(2023)
Vortex and Core Detection using Computer Vision and Machine Learning Methods
in European Journal of Computational Mechanics
| Description | We have developed new functionality in ROS 2 allowing seamless realtime reconfiguration of robotics cells. We have also developed automated robot programme generation software linked a flexible sensing environment. These three capabilities have been integrated across number of demonstrators to realise a true reconfigurable cell. |
| Exploitation Route | Much of the fundamental software in RoS 2 is already in the public domain and in use. We are seeking follow on funding to take the overall concept forward. |
| Sectors | Digital/Communication/Information Technologies (including Software) Manufacturing including Industrial Biotechology |
| Description | The work on ROS2 has resulted in Cranfield being invited to join th ROS2 consortium which issn international idutsrila and acidic group furthering the development and application of ROS 2. |
| First Year Of Impact | 2021 |
| Sector | Digital/Communication/Information Technologies (including Software) |
| Impact Types | Economic |
| Description | Researcher in Residence (Visiting Researcher Fellowship) with AMRC Sheffield |
| Organisation | University of Sheffield |
| Department | Advanced Manufacturing Research Centre (AMRC) |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | As a Researcher in Residence (April 2024 - April 2026), my contributions to this collaboration have been focused on robot calibration on the network and Factory 2050 cloud infrastructure, with the goal of pushing these advancements towards industrial standards. My work integrates real-time robotic calibration, digital twin technologies, and AI-driven automation, ensuring that robotic systems achieve higher precision, repeatability, and adaptability for advanced manufacturing applications. A key aspect of my contribution has been the development and deployment of a networked calibration framework, allowing robots across Factory 2050 and connected industrial facilities to self-calibrate in real-time. This involves leveraging cloud-based digital twins, AI-driven sensor fusion, and machine learning algorithms to continuously refine robotic accuracy. The integration of real-time data analytics and predictive maintenance ensures that robotic systems can adapt dynamically to changes in the manufacturing environment, reducing downtime and improving efficiency. Beyond technical development, I have led efforts to standardize robotic calibration protocols, working closely with industrial partners, research institutions, and policy-makers to ensure that these innovations align with international manufacturing and automation standards. This includes engaging with standardization bodies and contributing to guidelines for networked robotic calibration, positioning Cranfield University as a leader in AI-driven robotic standardization and industrial automation. Additionally, I have facilitated collaborations with key stakeholders, including AMRC Factory 2050, Airbus, and the ROS-Industrial Consortium, ensuring that our research translates into industry-wide best practices. By securing funding for joint research initiatives and organizing knowledge-sharing workshops, I am actively driving the adoption of cloud-based robotic calibration as a standardized industrial solution, further reinforcing Cranfield's leadership in intelligent automation and digital manufacturing. |
| Collaborator Contribution | AMRC Factory 2050: The Advanced Manufacturing Research Centre (AMRC) at Factory 2050 has provided state-of-the-art testbeds, infrastructure, and industrial integration support for the deployment and validation of cloud-based robotic calibration frameworks. Their expertise in robotic automation, metrology, and digital manufacturing ecosystems has facilitated real-world testing and refinement of AI-driven calibration methodologies, ensuring seamless integration with Industry 4.0 principles. |
| Impact | IEEE ICASCS Special Session Chair (2025) AI & Robotics |
| Start Year | 2024 |
| Title | R3M_Project GitHub Repository |
| Description | Establishment of an open source repository for new applications in ROS2. |
| Type Of Technology | Webtool/Application |
| Year Produced | 2022 |
| Impact | Significant interaction with the ROS2 online community |
| URL | https://github.com/IFRA-Cranfield/ros2_RobotSimulation |
