Dr John Oyekan

BEng (Hons), MIET, FHEA, CEng, FRS

  • Reader in Autonomous Manufacturing Systems and Robotics
Publications
A portrait style photograph of John Oyekan

Background

John Oyekan is a Chartered Engineer (CEng) and Reader in the Wolfson School at Loughborough University. His research focuses on building human-centred AI and cognitive architectures for autonomous manufacturing and automation, bridging the gap between optimisation algorithms, robotics, digital twins, intelligent systems design and industrial deployment.

Prior to joining Loughborough, he was a Senior Lecturer and Lecturer in Digital Manufacturing at the Universities of York and Sheffield. He holds a PhD in Computer Science and Electronic Engineering, as well as an MSc in Robotics and Embedded Systems, both from the University of Essex.

Earlier in his career, he worked as an Engineer at Visteon Ford, ZF TRW and at the Manufacturing Technology Centre (MTC) in Coventry, where he developed intelligent computational architectures and algorithms for autonomous systems. He also held a Research Fellowship at Cranfield University, conducting research in manufacturing informatics. His experience spans academia, industry and innovation ecosystems, including startups and government-funded catapults.

His work is characterised by a strong integration of fundamental research and industrial application, with collaborations across the automotive, aerospace and advanced manufacturing sectors. He is particularly interested in translating blue-sky research into deployable solutions that address real-world industrial, economic and societal challenges.

Dr Oyekan has a sustained record of publications in various areas including swarm robotics, manufacturing informatics, bio-inspired algorithms and intelligent sensing systems. He has also worked across various disciplines including chemistry, psychology and biology.

Qualifications and Awards

  • EPSRC PhD bursary, University of Essex, 2008 – 2012
  • ZF TRW MSc industrial scholarship, 2007 – 2008
  • Rodney Brooks Prize for best MSc thesis on Underactuated Control of Unmanned Vehicles, 2008. 
  • 3 year Under-graduate Scholarships, Coventry University 2002- 2006

John's research focuses on the development of autonomous manufacturing systems and robotics particularly the cognitive architecture of self-optimising, self-adaptive and self-reconfiguring manufacturing systems. He particularly focuses on the application of Digital Twins, Optimisation Algorithms and Robotics/Automation.

John works with other disciplines making use of multi and cross disciplinary approaches and has developed algorithms that:

  1. Enable Automation and Robots to be rapidly and flexibly retasked to new manufacturing task using a variety of AI concepts including symbolic and emergent AI approaches as well as Language models. I often make use of inspiration and concepts from cognitive psychology to achieve this.
  2. Enable a swarm of agents to constantly adapt to changing variations in the environment. An example is the use of a swarm of agents to make morphological aware decisions when collectively transporting a load through an environment.
  3. Enable the rapid digitisation of physical spaces in order to apply optimisation algorithms to find novel cost-effective ways to boost productivity and through-put on a manufacturing floor.

Grants and Contracts