A Digital Twin-Driven Approach to Predictive Safety and Control Optimisation in Human-Robot Collaborative Manufacturing

Apply and key information

This project is funded by:

Department for the Economy (DfE)

Summary

This research proposes a novel digital twin-driven framework to enhance safety and optimise control in human-robot collaborative manufacturing. By integrating real-time data from multi-modal sensors with a high-fidelity digital twin of the workspace, the project aims to create a predictive, adaptive system that monitors and dynamically adjusts robot behaviour based on human interactions and environmental factors. This digital twin will serve as a virtual platform for real-time scenario analysis, predictive modelling, and testing, enabling the anticipation of safety risks and the refinement of cobot control strategies to improve both operational efficiency and workplace safety.

The research will leverage advanced hybrid of model-based and data-driven techniques, such as deep reinforcement learning and distributed control, to enable networked swarm robotics and intelligent path planning in shared workspaces. The digital twin will facilitate robust control design by simulating interactions and responding to dynamic changes, ensuring resilience against faults and external disturbances.

The researcher will work at the Multi-Agent Robotic Centre (MARC) using newly acquired collaborative robots (cobots) and autonomous mobile robots (AMRs), supported by a Digital Twin-capable High-Performance Computer for computationally intensive learning algorithms. There will be opportunities for the researcher to work with the supervising team’s existing and new collaborative partners (academia and industrial). The research will also have opportunities to attend and present their research outcomes in local and international conferences as well as publications in international peer-reviewed high impact journals.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

Experience using research methods or other approaches relevant to the subject domain
A comprehensive and articulate personal statement

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

This project is funded by:

Department for the Economy (DfE)

These scholarships will cover tuition fees and provide a maintenance allowance of £19,237 (tbc) per annum for three years (subject to satisfactory academic performance). A Research Training Support Grant (RTSG) of £900 per annum is also available.

To be eligible for these scholarships, applicants must meet the following criteria:

Be a UK National, or

Have settled status, or

Have pre-settled status, or

Have indefinite leave to remain or enter, or

be an Irish National

Applicants should also meet the residency criteria which requires that they have lived in the EEA, Switzerland, the UK or Gibraltar for at least the three years preceding the start date of the research degree programme.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies.