Robotics project - ELE00176M
Module summary
This module will challenge students to work in a group on a complex robotic problem and produce a design and implementation to be tested in the York Robotics Laboratory. Using a variety of robotic parts and technologies, you will work together to build on your previous work on the Intelligent Robotics MSc to create an innovative team of robotic systems.
Module will run
| Occurrence | Teaching period |
|---|---|
| A | Summer Semester 2025-26 |
Module aims
Subject Content aims:
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To develop technical skills in the development of control software for robots
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To develop technical skills in the development of systems to integrate multiple robotic units
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To understand a complex problem formulation and provide a suitable design and implementation
Graduate Skills aims:
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To provide a context for the application of taught knowledge in an engineering setting
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To demonstrate the appreciation of scientific and engineering methods and techniques
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To demonstrate effective group working skills
Module learning outcomes
Subject content learning outcomes:
After successful completion of this module, students will be able to::
- Implement transferable group working skills such as communication and collaboration. Examined by group documentation and individual report.
- Achieve a professional standard of design, reasoning and implementation skills in a specialist area of robotic systems. Examined by individual report and a demonstration.
- Implement scientific and engineering skills that allow for the analysis of results from a developed system. Examined in the individual project report.
- Contribute in an original way to an established area of research or development, demonstrating a practical understanding of how established techniques of research and enquiry are used to create and interpret knowledge. Examined in the individual project report.
- Formulate a moderate sized problem, select and justify an appropriate approach in the context of wider research literature, and follow the approach systematically. Examined in the group documentation and individual report.
- Evaluate alternatives, selecting and justifying the approach taken at each point in the report, identifying parts of the project area that are feasible within the time (etc.) constraints of the project. Examined in the group documentation and individual report.
- Evaluate the latent issues of the subject area (for example, they might have to alter experimental work to take into account new findings). Examined in the group documentation and individual report.
- Prepare a report the structure and presentation of which is uncontentious, and that demonstrates the ability to critically reflect on their own performance and details the development and deployment of a robotic system.
- Accurately summarise work plans and demonstrate a solution in an oral presentation format.
Graduate skills learning outcomes:
After successful completion of this module, students will:
- Be able to express advanced technical concepts concisely and accurately and comment on their applications, limitations and implications
- Be able to select, adapt and apply a range of theoretical and practical techniques to solve advanced robotics problems
Indicative assessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 85 |
| Oral presentation/seminar/exam | 15 |
Special assessment rules
None
Additional assessment information
Groups of 4-5 students are tasked to develop a system in which two or more types of robot work together to complete a task in the TFTB York Robotics Laboratory.
Assessment includes a portfolio of tasks.
Indicative reassessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 100 |
Module feedback
'Feedback’ at a university level can be understood as any part of the learning process which is designed to guide your progress through your degree programme. We aim to help you reflect on your own learning and help you feel more clear about your progress through clarifying what is expected of you in both formative and summative assessments. A comprehensive guide to feedback and to forms of feedback is available in the Guide to Assessment Standards, Marking and Feedback.
The School of PET aims to provide some form of feedback on all formative and summative assessments that are carried out during the degree programme. In general, feedback on any written work/assignments undertaken will be sufficient so as to indicate the nature of the changes needed in order to improve the work. The School will endeavour to return all exam feedback within the timescale set out in the University's Policy on Assessment Feedback Turnaround Time. The School would normally expect to adhere to the times given, however, it is possible that exceptional circumstances may delay feedback. The School will endeavour to keep such delays to a minimum. Please note that any marks released are subject to ratification by the Board of Examiners and Senate. Meetings at the start/end of each term provide you with an opportunity to discuss and reflect with your supervisor on your overall performance to date.
Formative Feedback:
Students will have first and second supervisors who provide feedback on weekly progress reports by email.
Students are also expected to schedule regular meetings with their supervisors to receive feedback on their progressing design and build activities,.
Summative Feedback:
Feedback forms with a detailed breakdown of grades for each part of the assessment provided at the assessment of coursework which occurs both for an initial design presentation in Week 3 and at the end of term when the projects are assessed.
Indicative reading
Students are expected to use all the skills they have previously learned in the course but may want to refer to the following for supplementary information:
Modern Robotics (Mechanics,Planning and Control), 2017, K.M. Lynch and F. C. Park.
Introduction to Robotics, P.J. McKerrow Addison Wesley 1991.7
Fundamentals for control of robotic manipulators, Koivo, John Wiley, 1989.
Behaviour based robotics, R. C. Arkin, 1998, MIT press
Davies, E. Roy (2005). Machine Vision: Theory, Algorithms, Practicalities (3rd ed.). Amsterdam, Boston: Elsevier. ISBN 9780122060939