EEE8166 : Applied AI and Robotics
EEE8166 : Applied AI and Robotics
- Offered for Year: 2026/27
- Module Leader(s): Dr Zhuang Shao
- Lecturer: Dr John Hedley, Professor Nick Wright
- Owning School: Engineering
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 2 Credit Value: | 20 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Pre-requisite
Modules you must have done previously to study this module
Pre Requisite Comment
N/A
Co-Requisite
Modules you need to take at the same time
Co Requisite Comment
N/A
Aims
The module equips students with in-depth knowledge and understanding of key applications in robotics and applied AI. Concepts are analysed with subsequent engineering solutions firstly developed via simulation and then designed for hardware. The module aims to equip students with the necessary skills to effectively develop AI and robotic solutions towards tomorrow’s technological needs in industry.
Outline Of Syllabus
Mobile robotics and AI – This aspect of the module looks to analyse autonomous mobile robotics. The kinematics to drive such systems are mathematically derived and then developing these robots towards autonomy is explored through application of suitable sensing, data processing and control algorithms. Application of artificial intelligence to these autonomous robotic solutions is introduced.
Walking robots/reinforcement learning – This aspect of the module looks to analyse statically unstable robotic system and the application of reinforcement learning to establish dynamic stability and control of these systems.
Computer vision – This aspect of the module looks at how vision-based sensors are utilised in robotics and the use of AI for data analysis and interpretation; and explores how AI robotics, equipped with computer vision algorithms, processes and interprets vast amounts of visual data to enable real-world sophisticated decision-making.
Learning Outcomes
Intended Knowledge Outcomes
By the end of the module students will be able to:
1. Evaluate controller hardware and create software solutions to controller applications. (M4)
2. Mathematically analyse robot kinematics. (M3)
3. Analyse sensor data and evaluate data processing algorithms. (M2)
4. Create and evaluate autonomous control schemes. (M2, M6)
5. Create and evaluate AI solutions to smart technology. (M4, M8)
6. Evaluate design options for an autonomous robotic solution to meet an engineering specification. (M1, M4, M5, M6, M13)
7. Self-evaluate on knowledge and skills acquired and areas for further development. (M18)
Intended Skill Outcomes
By the end of the module students will be able to:
1. Evaluate virtual environments to create simulated solutions to engineering problems. (M3)
2. Design and evaluate hardware system level solutions to complex engineering problems. (M1, M4, M5, M6, M13)
3. Presenting technical details to engineering solutions. (M17)
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 20 | 1:00 | 20:00 | Lectures |
| Guided Independent Study | Assessment preparation and completion | 1 | 50:00 | 50:00 | Revision for exams and completion of exams |
| Scheduled Learning And Teaching Activities | Lecture | 3 | 2:00 | 6:00 | Lab sessions on Computer vision in computer cluster. |
| Guided Independent Study | Assessment preparation and completion | 1 | 2:00 | 2:00 | Formative examination |
| Guided Independent Study | Directed research and reading | 8 | 5:00 | 40:00 | Recommended reading for required knowledge. |
| Structured Guided Learning | Academic skills activities | 9 | 5:00 | 45:00 | Tutorial examples and trial exams |
| Scheduled Learning And Teaching Activities | Practical | 3 | 3:00 | 9:00 | Lectures/Lab/Tutorial sessions on mobile robotics in computer cluster |
| Guided Independent Study | Independent study | 1 | 28:00 | 28:00 | Reviewing lecture notes; general reading |
| Total | 200:00 |
Teaching Rationale And Relationship
The module is divided into discrete topics with each topic addressing a particular aspect of AI understanding and robotics engineering. Teaching on each topic consists of a series of lectures covering all the required material for that topic followed by tutorial problems to develop understanding.
Recommended reading links give students a deeper and broader understanding of the subject.
The timetabled sessions give students the opportunity to access help for any of the module material whilst a discussion board allows for additional queries to be addressed outside of timetabled sessions.
Opportunities are provided throughout the module for students to practice examples of the assessments and receive feedback of their performance. Students are encouraged to monitor their learning as the module progresses.
Reading Lists
Assessment Methods
The format of resits will be determined by the Board of Examiners
Exams
| Description | Length | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|---|
| Digital Examination | 90 | 2 | M | 100 | Computer based exam |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Digital Examination | 2 | M | Formative practise of NUMBAS questions |
Assessment Rationale And Relationship
The exam assesses students on specific technical knowledge of the module material under time constrained conditions. The assessment will cover M1, M2, M3, M4 and M6.
Students are given a range of tutorial questions during the teaching aspect of the module to practice on, these give immediate feedback on marking and advice on how to answer the question and help students prepare for their module assessment [M1, M2, M3, M4, M6, M7, M12, M13]
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- EEE8166's Timetable
Past Exam Papers
- Exam Papers Online : www.ncl.ac.uk/exam.papers/
- EEE8166's past Exam Papers
General Notes
N/A
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Disclaimer
The information contained within the Module Catalogue relates to the 2026 academic year.
In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described.
Modules may be amended on an annual basis to take account of changing staff expertise, developments in the discipline, the requirements of external bodies and partners, staffing changes, and student feedback. Module information for the 2027/28 entry will be published here in early-April 2027. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.