Advanced Mobile Robot Vision for Smart Manufacturing

Apply and key information  

This project is funded by:

    • Department for the Economy (DfE)

Summary

Smart Manufacturing, involving robotics, automation and data analytics, has become a global focus. An Autonomous Mobile Robot (AMR) is a type of robot that follows a defined path within a smart manufacturing facility to transport materials or goods. They are used to automate material handling processes.

Visual navigation for AMRs involves using cameras or other vision sensors to guide the robotic vehicle through its environment. This method offers flexibility compared to other methods like magnetic tape or laser guidance, as it allows for dynamic route changes and adaptation to changing environments.

This project will focus on fundamental challenges in visual navigation for AMRs including:

* Accurate Image Processing: Ensuring reliable image processing and feature extraction in varying lighting conditions and complex environments.
* Real-time Processing: Processing visual data in real-time to enable timely decision-making and control.
* Obstacle Detection and Avoidance: Accurately detecting and avoiding obstacles, both static and dynamic, to prevent collisions.
* Localization and Mapping: Precisely determining the AGV's position and creating accurate maps of the environment.
* Robustness to Environmental Changes: Adapting to changes in lighting conditions, object placement, and other environmental factors.

Overcoming these challenges requires advanced computer vision, robust sensor fusion and deep learning enabling AMRs to be more flexible in their path planning by recognising visual cues in the environment, such as lines on the floor or certain landmarks in the warehouse. The object detection and recognition capabilities of the AMR can further enhance navigation. This is very useful in environments where the AMRs are to interact with dynamic objects or avoid obstacles.

The Cognitive Robotic laboratory contains a range of robotics systems including mobile robots along with 2D/3D vision sensors that can be utilised in this project. This project is suitable for graduates from both computing and engineering disciplines.

https://www.ulster.ac.uk/research/topic/computer-science/intelligent-systems-research-centre/teams/cognitive-robotics

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
  • 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

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

This project is funded by:

  • Department for the Economy (DfE)

Department for the Economy (DFE) Scholarship – UK/ROI Awards

These scholarships will cover tuition fees and provide a maintenance allowance of £19,237 (tbc) per annum for three years (subject to satisfactory academic performance).  A Research Training Support Grant (RTSG) of £900 per annum is also available.

To be eligible for these scholarships, applicants must meet the following criteria:

  • Be a UK National, or
  • Have settled status, or
  • Have pre-settled status, or
  • Have indefinite leave to remain or enter, or
  • be an Irish National

Applicants should also meet the residency criteria which requires that they have lived in the EEA, Switzerland, the UK or Gibraltar for at least the three years preceding the start date of the research degree programme.

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

Alatise, M. B., & Hancke, G. P. (2020). A review on challenges of autonomous mobile robot and sensor fusion methods. IEEE Access, 8, 39830-39846.

Cebollada, S., Payá, L., Flores, M., Peidró, A., & Reinoso, O. (2021). A state-of-the-art review on mobile robotics tasks using artificial intelligence and visual data. Expert Systems with Applications, 167, 114195.

Chen, C., Wang, B., Lu, C. X., Trigoni, N., & Markham, A. (2023). Deep learning for visual localization and mapping: A survey. IEEE Transactions on Neural Networks and Learning Systems.

The Doctoral College at Ulster University

Key dates

Submission deadline
Thursday 9 January 2025
04:00PM

Interview Date
24 January 2025

Preferred student start date
31 March 2025

Applying

Apply Online  

Contact supervisor

Dr Dermot Kerr

Other supervisors