MEC8092 : Applied AI and Robotics (Inactive)
- Inactive for Year: 2025/26
- Module Leader(s): Dr Zhuang Shao
- 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 | |
Aims
The module equips students with in-depth knowledge and understanding of key applications in robotics. Concepts are mathematically analysed with subsequent engineering solutions firstly developed via simulation and finally implemented and characterised on 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 wheeled 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.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 24:00 | 24:00 | Practical application of material learned towards a goal orientated project |
| Scheduled Learning And Teaching Activities | Lecture | 9 | 2:00 | 18:00 | Lectures |
| Guided Independent Study | Assessment preparation and completion | 1 | 1:00 | 1:00 | Computer-based examination |
| Guided Independent Study | Directed research and reading | 9 | 5:00 | 45:00 | Focussed mini-assignments to support module assessment |
| Structured Guided Learning | Academic skills activities | 9 | 5:00 | 45:00 | Computer-based tutorials and trial exams |
| Guided Independent Study | Directed research and reading | 8 | 5:00 | 40:00 | Recommended reading for required knowledge |
| Scheduled Learning And Teaching Activities | Small group teaching | 9 | 3:00 | 27:00 | Teaching (tutorials) to support independent study and reinforce skills practice including lab exercises |
| 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 an in-person tutorial class covering tutorial problems, simulation work and hands-on activities.
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. A blend of simulated and hands on activities allows students to learn the required knowledge and skills and apply this to real work scenarios.
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.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Exams
| Description | Length | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|---|
| Digital Examination | 60 | 2 | A | 40 | NUMBAS based digital exam |
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Design/Creative proj | 2 | M | 60 | Assignment based on a mini project to develop an engineering solution to a given problem specification. |
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 practice of NUMBAS exam |
Assessment Rationale And Relationship
The assignment assesses the student’s ability to apply the knowledge and skills developed during the module towards creating and evaluating a AI and robotic solutions to given problem specifications. A computer-based exam assesses students on specific technical knowledge of the module material under time constrained conditions. An open book approach is adopted to encourage students to make the necessary notes in preparation for the exam, this gives students the opportunity to reflect on areas of strength and weaknesses in their knowledge of the subject.
Students are given a range of NUMBAS based 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. The students are also required to do mini-assignments and lab work during the module, advice and feedback is available to any student needing help with these. The module assignment is based on these activities. Thus all assessment in this module is based on formative practice of the material.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- MEC8092's Timetable