Professor Niels Lohse Dipl.-Ing.(FH), MSc, PhD, MIEEE

Department of Mechanical Engineering
Professor of Manufacturing Automation and Robotics

Contact details

Telephone: +44 (0) 121 414 4279
Email: n.lohse@bham.ac.uk

Address: Department of Mechanical Engineering
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

The aim of Professor Lohse's research is to improve the productivity and responsiveness of production systems while improving human wellbeing and overall sustainability.

Niels' research interests are in the field of intelligent automation, robotics, and artificial intelligence for assembly, disassembly, and joining including human-robot and robot-robot collaboration, measurement and perception, manufacturing system modelling, distributed control, self-organising systems, diagnostics, and decision-support systems.

Starting a PhD in 2001, Niels gained extensive experience from leading and working in national and international research projects funded by EPSRC, InnovateUK, Research England, the European Commission and directly by industrial partners.

Professor Lohse values interdisciplinary collaboration with other experts in engineering, science, and humanities both national and internationally and has worked cross multiple sectors including but not limited to aerospace, automotive, energy, medical, construction, recycling/remanufacturing, and domestic appliances.

To accelerate innovation and deliver impact, the research projects are typically co-design and co-delivered with partners from academia and industry including small and medium size enterprises (SMEs) as well as large enterprises as end users, system integrators and technology providers.

The quality of the published work Niels has contributed to has been recognised by several awards including an outstanding and highly recommended paper awards in Assembly Automation as well as best papers at conferences (IEEE IECON2013, ICMR2021, TAROS2023).

Professor Lohse is open for supervising PhD projects related to my area of interest.

Qualifications

PhD in Manufacturing Engineering and Operations Management: University of Nottingham, 2006
MSc in Technology Management: University of Portsmouth, 2001
Dipl.-Ing. (Meng) in Mechanical Engineering specialising in Engineering Design: University of Applied Science Hamburg, Germany, 2000

Biography

After completing school in 1992, Niels successfully completed a three-year Apprenticeship as Industrial Mechanic at a shipyard in 1995 before entering higher education. This gave a strong foundation and sense for materials and industrial manufacturing processes. It also raised awareness of working in an industrial shopfloor environment.

Whilst completing their PhD, Niels was accepted a position as Lecturer in Advanced Manufacturing Technology at the University of Nottingham in 2006. This provided the opportunity to write, contribute, and work on several European Commission funded projects focusing on the design, operation and reconfiguration of automation systems. Many of these projects investigated and developed concepts, technologies and models that became to be know as Cyber-Physical Systems and later Industry 4.0. The key focus was on making it easier to change dedicated automation systems in response to changing production requirements. This gave the opportunity to work with many leading industrial and academic experts from across Europe driving the vision for smart systems with embedded intelligence and distributed control that allow systems to evolve over time.

At the beginning of 2014, Niels joined Loughborough University as a Senior Lecturer in Intelligent Automation as part of the Intelligent Automation Centre (IAC). This provided the opportunity to extend work on Cyber Physical Systems while at the same time exploring new opportunities in intelligent robotics and looking at human-robot collaboration working closely with several large aerospace companies in the UK.

In 2017, Niels was privileged to take over the leadership of the IAC, allowing guidance of the direction of research with our industrial partners and my academic colleagues. This was shortly followed by promotion to Reader in Manufacturing Automation and Robotics in 2018.

During time as director of IAC, they successfully moved premisses and built a very healthy research profile including the Made Smarter Innovation – Research Centre for Smart, Collaborative Robotics. Niels has represented Loughborough University on the Programme Board of the Manufacturing Technology Centre (MTC) since 2017, was elected to the University Senate (2020 to 2023), joined the EPSRC Strategic Advisory Team for Manufacturing and the Circular Economy in 2022, and was promoted to full Professor in Manufacturing Automation and Robotics in 2022.

In 2024, after over 10 years at Loughborough University, Professor Lohse accepted a new challenge joining the University of Birmingham to help build a Robotics Institute and as part of the new EPSRC Manufacturing Research Hub in Robotics, Automation & Smart Machine Enabled Sustainable Circular Manufacturing & Materials (RESCu-M2).

Postgraduate supervision

Professor Lohse is open to discuss potential PhD projects including but not limited to the following topic areas:

Human-robot collaborative lifting – investigating ergonomic and safe collaboration for medium to high weight components.

Robot swarms for collaborative manipulation and transport – investigating robot-robot mobile manipulation for the handling, manipulation and transport of large bulky objects.

Real-time perception in complex industrial workspaces – investigating multi-modal sensor networks with edge intelligence and reconstruction of workplaces to track people (work analysis, posture, safety, etc.) and objects (components, assemblies, tools, etc.) as well as robots to learn their behaviour.

Learning of dexterous manipulation skills for non-rigid components – investigating how robots learn (in simulation and the real world) manipulation tasks for non-rigid objects like cables, fabrics, fibres, etc.

Research

Professor Lohse's research is inspired by several fundamental research questions:

How do we enable humans and robotic systems to work more effectively together in hybrid teams that can achieve more collectively entirely human or robot systems?

How do we overcome existing barriers in automation and robotics for the manipulation, assembly, and disassembly of complex parts in complex environments that are difficult to model (e.g. due to non-rigid interactions, random variations, human behaviour)?

How do we enable automation and robotic systems to be come more adaptable, responsive and easier to changeover in response to changing requirements, objectives and tasks?

How do we increase labour productivity while improving environmental, social, and economy sustainability in industrial processes?

How do we unlock the potential of artificial learning systems for increased safety, autonomy, and resilience in manufacturing environments?

What are our ethical principles and corresponding regulatory frameworks that should govern the future of human and robotic work?

Publications

Recent publications

Article

Buerkle, A, Al-Yacoub, A, Eaton, W, Zimmer, M, Bamber, T, Ferreira, P, Hubbard, EM & Lohse, N 2023, 'An Incremental Learning Approach to Detect Muscular Fatigue in Human- Robot Collaboration', IEEE Transactions on Human-Machine Systems, vol. 53, no. 3, pp. 520-528. https://doi.org/10.1109/THMS.2023.3259139

Lupi, F, Mabkhot, MM, Boffa, E, Ferreira, P, Antonelli, D, Maffei, A, Lohse, N & Lanzetta, M 2023, 'Automatic definition of engineer archetypes: A text mining approach', Computers in Industry, vol. 152, 103996. https://doi.org/10.1016/j.compind.2023.103996

Pottier, C, Petzing, J, Eghtedari, F, Lohse, N & Kinnell, P 2023, 'Developing digital twins of multi-camera metrology systems in Blender', Measurement Science and Technology, vol. 34, no. 7, 075001. https://doi.org/10.1088/1361-6501/acc59e

Bahraini, MS, Mahmoodabadi, MJ & Lohse, N 2023, 'Robust Adaptive Fuzzy Fractional Control for Nonlinear Chaotic Systems with Uncertainties', Fractal and Fractional, vol. 7, no. 6, 484. https://doi.org/10.3390/fractalfract7060484

Buerkle, A, Matharu, H, Al-Yacoub, A, Lohse, N, Bamber, T & Ferreira, P 2022, 'An adaptive human sensor framework for human–robot collaboration', International Journal of Advanced Manufacturing Technology, vol. 119, no. 1-2, pp. 1233-1248. https://doi.org/10.1007/s00170-021-08299-2

Zimmer, M, Al-Yacoub, A, Ferreira, P, Hubbard, EM & Lohse, N 2022, 'Experimental study to investigate mental workload of local vs remote operator in human-machine interaction', Production and Manufacturing Research, vol. 10, no. 1, pp. 410-427. https://doi.org/10.1080/21693277.2022.2090458

Lupi, F, Mabkhot, MM, Finžgar, M, Minetola, P, Stadnicka, D, Maffei, A, Litwin, P, Boffa, E, Ferreira, P, Podržaj, P, Chelli, R, Lohse, N & Lanzetta, M 2022, 'Toward a sustainable educational engineer archetype through Industry 4.0', Computers in Industry, vol. 134, 103543. https://doi.org/10.1016/j.compind.2021.103543

Micheler, S, Goh, YM & Lohse, N 2021, 'A transformation of human operation approach to inform system design for automation', Journal of Intelligent Manufacturing, vol. 32, no. 1, pp. 201-220. https://doi.org/10.1007/s10845-020-01568-z

Al-Yacoub, A, Flanagan, M, Buerkle, A, Bamber, T, Ferreira, P, Hubbard, EM & Lohse, N 2021, 'Data-driven modelling of human-human co-manipulation using force and muscle surface electromyogram activities', Electronics (Switzerland), vol. 10, no. 13, 1509. https://doi.org/10.3390/electronics10131509

Buerkle, A, Eaton, W, Lohse, N, Bamber, T & Ferreira, P 2021, 'EEG based arm movement intention recognition towards enhanced safety in symbiotic Human-Robot Collaboration', Robotics and Computer-Integrated Manufacturing, vol. 70, 102137. https://doi.org/10.1016/j.rcim.2021.102137

Buerkle, A, Bamber, T, Lohse, N & Ferreira, P 2021, 'Feasibility of Detecting Potential Emergencies in Symbiotic Human-Robot Collaboration with a mobile EEG', Robotics and Computer-Integrated Manufacturing, vol. 72, 102179. https://doi.org/10.1016/j.rcim.2021.102179

Al-Yacoub, A, Zhao, YC, Eaton, W, Goh, YM & Lohse, N 2021, 'Improving human robot collaboration through Force/Torque based learning for object manipulation', Robotics and Computer-Integrated Manufacturing, vol. 69, 102111. https://doi.org/10.1016/j.rcim.2020.102111

Conference contribution

Mabkhot, MM, Lohse, N & Ferreira, P 2023, An adapation Framework for Industry 4.0 Responsive Production Systems. in H Dorksen, S Scanzio, J Jasperneite, L Wisniewski, KF Man, T Sauter, L Seno, H Trsek & V Vyatkin (eds), 2023 IEEE 21st International Conference on Industrial Informatics (INDIN)., 10218127, IEEE International Conference on Industrial Informatics (INDIN), IEEE, 21st IEEE International Conference on Industrial Informatics, INDIN 2023, Lemgo, Germany, 17/07/23. https://doi.org/10.1109/INDIN51400.2023.10218127

Flanagan, M, Lohse, N & Ferreira, P 2023, Mobile Robots for Collaborative Manipulation over Uneven Ground Using Decentralised Impedance Control. in F Iida, P Maiolino, A Abdulali & M Wang (eds), Towards Autonomous Robotic Systems: 24th Annual Conference, TAROS 2023, Cambridge, UK, September 13–15, 2023, Proceedings. 1 edn, Lecture Notes in Computer Science, vol. 14136 LNAI, Springer, pp. 343-355, Proceedings of the 24th Annual Conference Towards Autonomous Robotic Systems, TAROS 2023, Cambridge, United Kingdom, 13/09/23. https://doi.org/10.1007/978-3-031-43360-3_28

Review article

Buerkle, A, Eaton, W, Al-Yacoub, A, Zimmer, M, Kinnell, P, Henshaw, M, Coombes, M, Chen, WH & Lohse, N 2023, 'Towards industrial robots as a service (IRaaS): Flexibility, usability, safety and business models', Robotics and Computer-Integrated Manufacturing, vol. 81, 102484. https://doi.org/10.1016/j.rcim.2022.102484

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