Publications

Cobot-assisted capacitive sensing for in-process, micrometre-scale thickness estimation of liquid films

Othman Uqba, Ameur Mohamed Adlan Ait, Walker Charlie, Khedr Mohamed, Yang Erfu, Torres Ophelie

Next Research Vol 14 (2026)

https://doi.org/10.1016/j.nexres.2026.102570

Human-robot collaboration in laboratory automation: A critical review

Cameron-Robson Tye, Yang Erfu, Donlon Lynn, Willis Oliver

2026 31st International Conference on Automation and Computing (ICAC) 2026 31st International Conference on Automation and Computing (ICAC) (2026)

https://doi.org/10.1109/icac70325.2026.11712329

Intelligent tidal lane setting algorithm based on LSTM-GASVR and multi-objective optimization

Cheng Xin, Zhou Jingmei, Chen Jing, Chang Hui, Yang Erfu

2026 31st International Conference on Automation and Computing (ICAC) 2026 31st International Conference on Automation and Computing (ICAC) (2026)

https://doi.org/10.1109/icac70325.2026.11712528

Advances in sensing, control and path planning for robotic systems

Szczepański Rafał, Yang Erfu

Sensors Vol 26 (2026)

https://doi.org/10.3390/s26196253

Socio-technical systems perspective on Internet of Things adoption in agriculture : A systematic literature review of developing economies

Jibrin Baffa Abdulkadir, Yang Erfu, Mehnen Jorn

Journal of Economy and Technology (2026)

https://doi.org/10.1016/j.ject.2026.09.003

Robotic teleoperation in hazardous environments : A review of feedback architectures, stability, and learning-based adaptation

Khedr Mohamed, Le Quang Dan, Cameron-Robson Tye, Morsi Nour, Yang Erfu, Luo Chaomin

Complex Engineering Systems Vol 6 (2026)

https://doi.org/10.20517/ces.2026.07

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Professional Activities

The 30th International Conference on Automation and Computing

Chair

29/8/2026

Examiner

Examiner

16/6/2026

DMEM Research Seminar on Connecting Researchers, Systems, and Communities: IEEE SMC and AI in One Health

Organiser

5/6/2026

Examiner

Examiner

5/6/2026

International Journal on Interactive Design and Manufacturing (Journal)

Guest editor

1/5/2026

MDPI UK Editors' Forum (Event)

Editor

29/4/2026

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Projects

Robust Perception for Improving Robot Navigation in Highly Dynamic Manufacturing Environments

Yang, Erfu (Principal Investigator) Mehnen, Jorn (Co-investigator) Weston, Sam (Post Grad Student)

01-Jan-2026 - 30-Jan-2029

EPSRC Centre for Doctoral Training in Materials 4.0 | Irvine, Anthony

Huang, Jianglin (Principal Investigator) Yang, Erfu (Co-investigator) Irvine, Anthony (Research Co-investigator)

01-Jan-2026 - 01-Jan-2030

AI Surrogate Modelling to Enhance the Digital Twin of Titanium Cogging

Huang, Jianglin (Principal Investigator) Yang, Erfu (Co-investigator) Rahimi, Salaheddin (Co-investigator)

The "AI Surrogate Modelling to Enhance the Digital Twin of Titanium Cogging" is a fully funded PhD project at the University of Strathclyde, linked with the EPSRC Centre for Doctoral Training (CDT) in Materials 4.0 and the Henry Royce Institute.
The research unites physics-based modelling, machine learning, and experimental validation. It is designed to tackle the computationally intensive nature of physical simulations in heavy manufacturing.
Key aspects of this research include:
1) Digital Twin Advancement: The project develops rapid predictive models to simulate the hot forging and cogging of titanium.
2) The FutureForge Facility: The research applies these surrogate models directly to real-world data generated by the FutureForge platform, which is a pioneering hot forging research facility located at Strathclyde's Advanced Forming Research Centre (AFRC).
3) Optimization and Speed: Machine learning (surrogate) models act as fast, mathematical stand-ins for heavy Finite Element Analysis (FEA), enabling real-time decision support, microstructural prediction, and process optimization without standard computational delays.

01-Jan-2026 - 30-Jan-2030

ML-Driven Extra Estimation and Tool Life Extension for Resource-Efficient CNC Manufacturing (MEXTOOL)

Yang, Erfu (Principal Investigator) Mehnen, Jorn (Co-investigator) Xie, Wenkun (Co-investigator)

01-Jan-2026 - 31-Jan-2027

Inclusive Network Pathways for Human-Robot-AI Healthcare Research and Knowledge Exchange Awarded £3450.00

Yang, Erfu (Principal Investigator) McGeown, William (Co-investigator) Buchanan, Sarah (Fellow) Ait Ameur, Mohamed Adlan (Post Grad Student)

This project will build CONNECT-HRC-AI Healthcare as an inclusive, visible and sustainable RKEI culture network for human-robot-AI healthcare research and knowledge exchange at Strathclyde. The purpose is to use conference engagement as an evidence-gathering and external-learning stage that informs a wider programme to connect existing expertise, make contributions visible, and create practical collaboration pathways. The project responds directly to the RKEI culture challenge identified at Strathclyde: relevant activity already exists, but people, capabilities, opportunities and everyday contributions are often fragmented, unevenly visible and difficult to connect.
The network will build on initial collaborative work between DMEM and the Department of Psychology, using existing MICA/social robotics and AI-in-the-loop healthcare research as a concrete anchor (Ait Ameur et al., 2025). However, the purpose of the network is to broaden this collaboration beyond a single doctoral project, laboratory, or discipline. Human-robot-AI healthcare brings together engineering and robotics, computer science and AI, psychology and cognitive science, health and wellbeing research, ethics, design, data governance, technical support, professional services and knowledge exchange, clinicians, formal care partners, and lived-experience or community voices. It therefore provides a strong test case for the kind of inclusive, visible, and sustainable collaborative research culture that the RKEI Network aims to enable.
The project will build on existing Strathclyde activity rather than create a disconnected new group. It will follow a structured mapping approach inspired by existing institutional network analysis: identify relevant people, themes, projects, facilities, datasets, methods and gaps; validate this map through expert-led workshops; and convert it into collaboration pathways, pitch ideas and future grant routes. Participants will be selected through a combination of targeted invitations across faculties and roles, open expressions of interest, doctoral/researcher networks, KE contacts and existing health, cognitive science, digital, design and robotics links.

01-Jan-2026 - 14-Jan-2026

Cobot-enabled Nondestructive Quality Control in Production Lines with Advanced Vision and Machine Learning for Smart Manufacturing

Yang, Erfu (Principal Investigator) Millar, Richard (Co-investigator) Cameron-Robson, Tye (Researcher)

The overall research aim of this project is to fundamentally investigate the novel Cobot-enabled nondestructive quality control strategies by enabling holistic control and interactions of collaborative robots in complex human-robot collaborative manufacturing environments such that safety and interaction of nearby humans/operators can be maintained while the in-process inspection and quality control are being done. It is also critical to understand how to ensure the efficiency and flexibility of the manufacturing process can be achieved when process variations or environmental changes occur in advanced manufacturing systems. It is important that the Cobot-enabled nondestructive quality control is able to work collaboratively in a safe manner around a designated area of production lines, such as automated egg packaging stations. In a busy manufacturing environment, there can be no errors leading to production down.

01-Jan-2025 - 30-Jan-2028

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