AI (Artificial Intelligence) enabled Robotics for autonomous sensing and inspection of utility and sewage pipes.
Traditional manual maintenance of pipelines which requires digging of ground to get access to pipelines is ineffective because of safety issues, high cost and limited human vision. Also, trials and errors method is repeated to find the exact inspection spot which is a slow process and causes unnecessary disruption of service, traffic and businesses adjacent to it. Thus, serious faults often missed in the pipeline which causes failures such as leaks in the system. Thus, this project aims to design and develop AI (Artificial Intelligence) enabled autonomous multiple co-operative robots integrated with intelligent sensing system for the non-disruptive inspection of utility and sewage system.
Eligibility criteria: A B.Sc. or M.Sc. in Robotics or Electrical or Electronic or Mechanical engineering or related fields. Strong programming skills Knowledge and experience in (or a willingness to quickly learn about) machine vision and machine learning algorithms. Experimental, modelling and/or analytical experience in Robotics. Knowledge and experience of working on mechanical CAD software such as Solidworks or equivalent. Ability to work within a technical team. A wish to publish academic research in the highest quality journals. Good communication skills and ability to deliver on challenging tasks.
How to apply
If you are interested in applying for the above PhD topic please follow the steps below:
- Contact the supervisor by email or phone to discuss your interest and find out if you woold be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
- Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
- Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.
This is a self funded topic
Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.
Meet the Supervisor(s)
- PhD Napier University
- Research-engineer degree Belarusian State University of Informatics and Radio-electronics, Minsk, Belarus
- MSc (distinction) Belarusian State University of Informatics and Radio-electronics, Minsk, Belarus
- 2000-present Lecturer Brunel University London
- 2003-2011 Business Fellow London Technology Network, LTN Link between research activities at Brunel University London and industry
- 1997-2000 PhD student Napier University
- 1994-1997 Research Assistant Belarusian State University of Informatics and Radio-electronics
Md Nazmul Huda
- Dr M Nazmul Huda
received his BSc (Hons) degree in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology, Bangladesh in 2008, his MSc by Research degree in Computing Science from Staffordshire University, UK in 2011 and his Ph.D. degree in Robotics and Control from Bournemouth University, UK in 2016. At present, he is a Senior Lecturer in Electronic and Electrical Engineering at Brunel University London and supervising several PhD students in robotics, artificial intelligence and renewable energy. Before joining at Brunel University London, he has held several academic/research positions at Coventry University, Cranfield University, Bournemouth University, Staffordshire University and Bangladesh. He has more than ten years of experience in performing research and leading research projects in robotics, control and machine learning funded by various funding bodies including EPSRC and Innovate UK. He has filed a patent and published papers on flagship journals and conferences. He is a member of IET, IEEE, IEEE RAS and EPSRC associate peer review college. He has been nominated as a regular reviewer for EPSRC grants applications. He has been collaborating with internal and external academic and industrial partners and actively developing research proposals as a PI and Co-PI for internal and external funding calls including Horizon 2020, Wellcome Trust and High-Volume Transport. He also serves as a reviewer for many flagship journals and conferences in robotics, control and artificial intelligence including IEEE ICRA, IEEE IROS, IEEE SSRR, IEEE/ASME Transactions on Mechatronics, IEEE Robotics and Automation Letters (RA-L) etc.