Personal statement
Dr Erfu Yang is a Senior Lecturer in Robotics and Autonomous Systems (RAS) Group within the Departnent of Design, Manufacuting and Engineering Management (DMEM) at the Univerisity of Strathclyde. In 2008, he received his Ph.D. degree in robotics from the University of Essex, Colchester, UK, within the School of Computer Science and Electronic Engineering.
Before joining the DMEM, he was a research fellow in the Cognitive Signal Image and Control Processing Research (COSIPRA) Laboratory at the University of Stirling, UK. Previously, as a research fellow he also worked in the Department of Mechanical and Control Systems Engineering (Tokyo Institute of Technology, Japan) and School of Engineering (University of Edinburgh, UK). His main research interests include robotics, autonomous systems, computer vision, image/signal processing, mechatronics, data analytics, manufacturing automation, multi-objective optimizations, and applications of machine learning and artificial intelligence including multi-agent reinforcement learning, fuzzy logic, neural networks, bio-inspired algorithms, and cognitive computation, etc. He has over 140 publications in these areas, including more than 70 journal papers and 10 book chapters.
Dr Yang has been awarded over 15 research grants as PI (principal investigator) or CI (co-investigator). He is the Fellow of the UK Higher Education Academy, Member of the IEEE Society of Robotics and Automation, Publicity Co-Chair of the IEEE UK and Ireland Industry Applications Chapter, Committee Member of the Chinese Automation and Computing Society in the UK (CACSUK), and the IET SCOTLAND Manufacturing Technical Network. Dr Yang has been a Scientific/Technical Programme Committee member or organizer for a series of international conferences and workshops. He is an associate editor for the Cognitive Computation journal published by Springer.
Professional activities
- Convener
- Examiner
- 10/8/2020
- Industry Engagement Online Event
- Speaker
- 4/8/2020
- Qualisys-UoS Online Research Meeting
- Presenter
- 16/7/2020
- International Green Gown Award for Student Engagement for Strathclyde VIP4SD programme
- Contributor
- 8/7/2020
- ISCF Healthy Ageing Social, Behavioural and Design Research Programme (SBDRP) Outline Call Webinar
- Participant
- 27/5/2020
- Online Close-up Presentation on AFRC R2I Robotics Project "Intelligent Transferring System with Flexible and Cooperative Robots for Advanced Composite Manufacturing"
- Speaker
- 18/5/2020
More professional activities
Projects
- Autonomous and Interactive Station Rover for Next-Generation Passenger Information System
- Yang, Erfu (Principal Investigator) Luo, Xichun (Co-investigator) Corney, Jonathan (Co-investigator) Alcantara de la Cruz, Daniel (Researcher)
- KTP project with TrainFX Limited. In total £190,122, funded by the Innovate UK.
- 01-Jan-2021 - 31-Jan-2023
- Autonomous Manufacturing of Composite Products with Multiple Flexible and Intelligent Robots
- Yang, Erfu (Principal Investigator) Bomphray, Iain (Co-investigator)
- The ambition of this project is to fundamentally investigate the novel adaptive control algorithms and smart path planning strategies for developing a feasible solution to the autonomous manufacturing of composite products through the use of a MAR system. A highly-efficient vision system is also to be investigated by utilising advanced machine learning algorithms to detect the tools and materials, which is more intelligent and can significantly reduce human’s work. The proposed adaptive control algorithms and smart path planning strategies consist of an intelligent MAR controller and in-process path planner that determines an optimal path in a collaborative manner of multiple robots in the flexible manufacturing environment has raised the significant challenges in both academic and industrial domains It is also proposed that the MAR will have obstacle avoidance capabilities to avoid collision with other machines and humans in the shared workfloor
- 01-Jan-2020 - 30-Jan-2023
- KTP - TrainFX Resubmission
- Yang, Erfu (Principal Investigator) Corney, Jonathan (Co-investigator) Luo, Xichun (Co-investigator)
- 07-Jan-2020 - 06-Jan-2022
- Development of Thickness Control Technology using AI Controls for Blown Film Air Ring
- Yang, Erfu (Principal Investigator)
- 01-Jan-2019 - 31-Jan-2021
- Smart Hardware-embedded Data Processors for Rapid 3D Ranging & Imaging
- Li, David (Principal Investigator) Zang, Zhenya (Researcher) Yang, Erfu (Co-investigator)
- 01-Jan-2019 - 31-Jan-2022
- Novel Path Planning Algorithms and Smart Navigation Strategies of Multiple Autonomous Robots for the Visual Inspection of Asset Integrity in Confined Space
- Yang, Erfu (Principal Investigator) Yan, Xiu (Co-investigator)
- Robotic and autonomous systems (RAS) have received the increasing interests both in onshore and offshore applications where harsh environment (e.g. confined space to deploy and access) has been a challenging issue particularly in the oil and gas industry for numerous reasons. Among them, health, safety and environmental concerns are the key drivers for the deployment of RAS technology. The inspection of key assets in harsh environment (e.g., in the oil and gas industry) is critical both for safety and business reasons. With the current need to deploy an engineer into these environments, safety is of utmost importance and as a result, much preparatory work and additional safety assessments must be performed prior to human entry. In addition, RASs have enabled machines with greater levels of flexibility and adaptability, allowing them to perform various tasks more efficiently than the human counterpart. Multiple Autonomous Robots (MARs) (e.g., Unmanned aerial vehicles, UAVs, climbing mobile robots, etc) within the realm of RASs in particular have emerged as highly agile systems that can be deployed in swarms to perform lightweight tasks quickly and efficiently. With the rising safety, time and cost concerns relating to the inspection of important industrial equipment and infrastructures, the use of small, lightweight MAR that can be deployed quickly to assess the internal and external conditions are highly desirable.
The ambition of this project is to fundamentally investigate the novel path planning algorithms and smart navigation strategies for developing a feasible solution to the autonomous visual inspection and assessment of internal and external surface conditions in confined spaces through the use of a MAR equipped with on-board cameras. The proposed path planning algorithms and smart navigation strategies consist of an intelligent MAR controller and coverage path planner that determines an optimal path to fully inspect the assets such as vessel surfaces in which confined space has raised the significant challenges in both academic and industrial domains . It is also proposed that the MAR will have obstacle avoidance capabilities to avoid collision with external obstructions and internal features such as weirs and vane packs etc. Toward this end, smart navigation strategies will be playing a key role.
- 01-Jan-2019 - 31-Jan-2023
More projects
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