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AI (Artificial Intelligence) enabled Robotics for autonomous sensing and inspection of utility and sewage pipes.

Project Description

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.

Funding Notes

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: View Website. Recently the UK Government made available the Doctoral Student Loans of up to £25,000 for UK and EU 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%.)

How good is research at Brunel University London in Computer Science and Informatics?

FTE Category A staff submitted: 30.50

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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