Dr John Oyekan

Department of Automatic Control and Systems Engineering

Lecturer in Digital Manufacturing

John Oyekan profile photo
j.oyekan@sheffield.ac.uk
+44 114 222 5232

Full contact details

Dr John Oyekan
Department of Automatic Control and Systems Engineering
Amy Johnson Building
Portobello Street
Sheffield
S1 4DW
Profile

John Oyekan is a Lecturer in Digital Manufacturing in the Department of Automatic Control and Systems Engineering at the University of Sheffield. He received a Ph.D degree in Computer Science and Electronic Engineering from the University of Essex as well as a MSc Robotics and Embedded Systems from same. Prior to the University of Sheffield, he was an Engineer at the Manufacturing Technology Centre in Coventry were he developed software architectures and algorithms for Autonomous Systems and a Research Fellow at Cranfield University carrying out research in Manufacturing Informatics.

He has over 3 years experience working in industry and his work is characterised by both fundamental and applied research in collaboration with partners in the automotive, aerospace and manufacturing sectors. He has over 30 publications in the areas of swarm robotics, manufacturing informatics, bio-inspired algorithms and sensing.

Research interests

ARTIFICIAL INTELLIGENCE ALGORITHMS FOR MANUFACTURING SYSTEMS:
This theme investigates various AI algorithms to improve the efficiency of manufacturing systems. One such example we are looking at is the development of algorithms to enable human and robots to work together in close proximity with each other. This means that in the future safety cages that are currently used to protect humans from industrial robots could be removed enabling humans and robots to share the same workspace like work buddies. They will be able to understand each other as when humans are working alongside each other on various tasks. Another example is the use AI to augment humans during repetitive tasks. We have conducted some work that enables a system to non-intrusively check the manual work a human is performing and feedback to the human if a mistake is made. This eliminates the need of a testing station in manual assembly lines thereby saving costs. 


DIGITAL TWINS FOR ZERO PROTOTYPING OF FACTORY LAYOUTS:
The increasing complexity of manufacturing due to varying and bespoke customer demands calls for a factory that is able to be quickly reconfigured and build. But how do we ensure that the factory layout is right the first time and hence provide savings in cost? We investigate the use of virtual reality tools and simulation to develop virtual models of factories, simulate them and understand material as well as human flows before actual construction. The virtual models enable various hypotheses to be safely and cheaply tested. 


REMOTE COLLABORATIVE TASK PLATFORM ENABLED BY GAMING TECHNOLOGIES:
The increasing global spread of manufacturing enterprises calls for a real-time platform that enables geographically dispersed engineering teams to engage in collaborative problem solving and knowledge sharing. We investigate the use of new ICT gaming devices to develop a collaborative platform that: (a) enables remote sharing of physical engineering contexts, (b) allows synchronous exchange of human activity within the shared contexts, (c) provides intuitive augmentation of task environments with knowledge generated from the collaborating sites and (d) provides just in time context based information to users. the platform is built using low-cost ICT gaming interface devices such as the Microsoft Kinect, unity3d environment as well as oculus rift, and has been demonstrated using a real use case from the automotive industry.
 

NATURE-INSPIRED ALGORITHMS FOR ROBOTS IN UNKNOWN ENVIRONMENTS:
Animals are able to minimise the cost associated with food search in relation to food intake. They are able to efficiently search and locate sparsely distributed food in large environments outside their perception range and still have enough energy to survive and reproduce. They are masters of energy optimisation. Their environment span from a couple of square miles up to hundreds of square miles. By studying the process by which they solve this task, we are able to write algorithms for unmanned aerial robots capable of navigation and exploration in the natural environment. Possible uses of our robots include autonomous visual structural inspection tasks as well as autonomous search and rescue.

Publications

Books

Journal articles

Chapters

  • Oyekan JO (2016) Introduction, Tracking and Mapping of Spatiotemporal Quantities Using Unicellular Swarm Intelligence (pp. 1-10). Springer International Publishing RIS download Bibtex download
  • Oyekan JO (2016) Behaviour Based Coverage Controller, Tracking and Mapping of Spatiotemporal Quantities Using Unicellular Swarm Intelligence (pp. 129-161). Springer International Publishing RIS download Bibtex download
  • Oyekan JO (2016) Literature Review, Tracking and Mapping of Spatiotemporal Quantities Using Unicellular Swarm Intelligence (pp. 11-66). Springer International Publishing RIS download Bibtex download
  • Oyekan JO (2016) Investigative Process, Tracking and Mapping of Spatiotemporal Quantities Using Unicellular Swarm Intelligence (pp. 67-76). Springer International Publishing RIS download Bibtex download
  • Oyekan JO (2016) Improvements and Towards Real World Applications, Tracking and Mapping of Spatiotemporal Quantities Using Unicellular Swarm Intelligence (pp. 163-185). Springer International Publishing RIS download Bibtex download
  • Oyekan JO (2016) Developing and Implementing a Source Finding Controller, Tracking and Mapping of Spatiotemporal Quantities Using Unicellular Swarm Intelligence (pp. 77-109). Springer International Publishing RIS download Bibtex download
  • Oyekan JO (2016) Relationship Between the Berg–Brown Model and the Keller–Segel Model, Tracking and Mapping of Spatiotemporal Quantities Using Unicellular Swarm Intelligence (pp. 111-128). Springer International Publishing RIS download Bibtex download
  • Oyekan JO (2016) Conclusion, Tracking and Mapping of Spatiotemporal Quantities Using Unicellular Swarm Intelligence (pp. 187-194). Springer International Publishing RIS download Bibtex download
  • Oyekan J, Lu B, Li B, Gu D & Hu H (2010) A Behavior Based Control System for Surveillance UAVs, Advanced Information and Knowledge Processing (pp. 209-228). Springer London RIS download Bibtex download

Conference proceedings papers

Teaching activities
  • Machine Learning