Summary

Smart Manufacturing, involving robotics, automation and data analytics, has become a global focus. Smart manufacturing facilitates enhanced productivity, cost reductions, predictive maintenance and automated data. Companies that adopt smart manufacturing principles can further improve on lean manufacturing strategies and compete in a global market, even in high wage societies. The focus of this project is to develop intelligent approaches for autonomous reconfigurable robotic applications. We want robots to work seamlessly alongside each other and humans in adaptive and flexible manufacturing environments. These environments will be capable of completing a range of tasks to contribute to the assembly of “low-volume high-value” products in line with Industry 4.0 principles. Process understanding involves identifying the required steps in a manufacturing process and employing appropriate resources. As robots are considered resources, then an optimal configuration is required for efficiency.

The first research challenge is to identify the appropriate robotic systems, based on availability, which are needed to complete the task. In a dynamic manufacturing environment we also require the ability to allocate additional resources as they become available. Robots use their sensors to perceive information from the environment which is needed to complete a task. Robots use their end-effectors to interact with objects and their environment to complete a task.

The second research challenge is to integrate robot sensory data with task understanding to make decisions on how to interact with the objects and environment. Smart manufacturing requires an environment to be adaptive, with robots rapidly changing between actions and tasks. Often actions require different end effectors of which there are three basic types: grippers, process tools and sensors. It may be necessary to change the end effector type for a specific task. It is envisaged that this would be completed, when necessary, by the other robots rather than requiring human involvement.

The third research challenge is to develop a reconfigurable and adaptive manufacturing environment consisting of state of the art robotics manipulators and dexterous hands with advanced tactile and vision sensors; these facilities are available in the Cognitive Robotics Lab at Ulster.

This project aligns with the Lean Manufacturing philosophy of minimising the 8 wastes of Lean, for example; overproduction, potential of people, unnecessary or excess motion, defects, waiting etc. The goals will be achieved through the use of state-of-the art distributed task allocation approaches, tactile and vision sensing, combined with stateof- the art machine learning techniques. Robot control and manipulation will be achieved via Robot Operating System (ROS). Research in Cognitive Robotics at the ISRC ranges from investigating robotics as a science, to applications of robotics such as industrial robotics, smart manufacturing, assistive robotics and computer vision. The Cognitive Robotic laboratory contains a range of robotics systems including manipulators and grippers along with 2D/3D vision sensors and tactile sensors that can be utilised in this project.

This project is suitable for graduates from both computing and engineering disciplines.


Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

  • Experience using research methods or other approaches relevant to the subject domain
  • Research proposal of 1500 words detailing aims, objectives, milestones and methodology of the project
  • A demonstrable interest in the research area associated with the studentship

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • First Class Honours (1st) Degree
  • Masters at 70%
  • For VCRS Awards, Masters at 75%
  • Experience using research methods or other approaches relevant to the subject domain
  • Work experience relevant to the proposed project
  • Publications - peer-reviewed
  • Experience of presentation of research findings

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).

This scholarship will cover full-time PhD tuition fees and provide the recipient with £16,062 (tbc) maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Vice-Chancellor’s Research Bursary (VCRB)

Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).

This scholarship will cover full-time PhD tuition fees and provide the recipient with £8,000 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Vice-Chancellor’s Research Fees Bursary (VCRFB)

Fees only award (PhD fees + RTSG for 3 years).

This scholarship will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £16,062 (tbc) per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

  • Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
  • Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
  • Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
  • Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living


Recommended reading

  1. Lepora, Nathan & Ward-Cherrier, Benjamin. (2016). Tactile Quality Control With Biomimetic Active Touch. IEEE Robotics and Automation Letters. 1. 1-1. 10.1109/LRA.2016.2524071.
  2. Su Z, Fishel JA, Yamamoto T and Loeb GE (2012) Use of tactile feedback to control exploratory movements to characterize object compliance. Front. Neurorobot. 6:7. doi: 10.3389/fnbot.2012.00007
  3. Friedl WA and Roa MA (2021) Experimental Evaluation of Tactile Sensors for Compliant Robotic Hands. Front. Robot. AI 8:704416. doi:10.3389/frobt.2021.704416
  4. Büchi, Giacomo, Cugno, Monica and Castagnoli, Rebecca, (2020), Smart factory performance and Industry 4.0, Technological Forecasting and Social Change, 150, issue C. DOI: 10.1016/j.techfore.2019.119790
  5. Kolberg, Dennis & Zühlke, Detlef. (2015). Lean Automation enabled by Industry 4.0 Technologies. IFAC-PapersOnLine. 48. 1870-1875.  10.1016/j.ifacol.2015.06.359.
  6. Ghobakhloo, Morteza. (2019). Industry 4.0, Digitization, and Opportunities for Sustainability. Journal of Cleaner Production. 252. 119869. 10.1016/j.jclepro.2019.119869.
  7. Culot, Giovanna & Nassimbeni, Guido & Orzes, Guido & Sartor, Marco. (2020). Behind the definition of Industry 4.0: Analysis and open questions. International Journal of Production Economics. 226. 107617. 10.1016/j.ijpe.2020.107617.

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