Use of Advanced Robotics to augment Railway Maintenance and Operations

Reference number: SCEBE-20-023-CHSF

Background

It is recognised that advanced robotics will have an increasingly important part to play in the competitive future of Railway Maintenance and operations, and this work builds on the previous successful published work at GCU. Maintenance is known as a high cost area within the railway industry however the diverse range of requirements and non-standard, non-repetitive handling contexts required means that smart systems able to cope with the challenges of dull dirty and dangerous tasks that rail can present are needed. Adapting classical robotics to deal with the unique challenges that rail represents, including smart vision, flexible and tactile handling systems, in terms of safety and task repeatability is needed. There are excellent links with Fanuc Robotics, Siemens, Hitachi and Abellio-Scotrail and this proposal builds on those connections.

Aim and scope of work

This project seeks to address the increasing interest in Robotics by use of a low risk approach to robotisation via simulation (Using Fanuc Roboguide – an industry leading software package- Dr Colin Harrison) in conjunction with Computer Vision techniques for task handling (Dr Mario Mata), use of PTC Creo (Dr Colin Harrison) for design together with the internationally recognised expertise in rail of Professor Babakalli Alkali. The aim is to devise a smart robotic maintenance system, incorporating force measurement, advanced handling and vision technology, as well as using robotic simulation to quantify the benefits in a low risk approach for railway maintenance applications.

Person Specifications 

The successful applicant will hold a minimum of a Bachelor’s degree in a relevant subject (UK 1.1 or 2.1 classification) or equivalent. Good understanding and prior experience of working and thinking independently as an engineer, including some CAD such as PTC Creo v5 or similar CAD experience, use of MATLAB including toolboxes, and general programming skills will be an advantage. Please note this is a self-funded opportunity. 

How to Apply

To apply, please use the relevant link below:

· As a full-time student: https://evision.prod.gcu.tribalsits.com/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=D27ENGXXXFT&code2=0006

· As a part-time student: https://evision.prod.gcu.tribalsits.com/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=D27ENGXXXPT&code2=0006