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AER8014 : Applied AI and Autonomous Flight
- Inactive for Year: 2026/27
- Module Leader(s): Professor Wei Pan
- Lecturer: Dr Hamed Rezaee
- Owning School: Engineering
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 1 Credit Value: | 20 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Aims
This module develops advanced knowledge and practical capability in applied artificial intelligence and autonomous flight systems for aerospace applications.
Students will examine the principles of autonomous flight for fixed-wing and multi-copter UAVs, including kinematics, sensing, data processing, control algorithms, computer vision and AI-enabled decision-making. The module explores how autonomous systems can be developed through simulation and translated into hardware-oriented design solutions.
Through analytical study, simulation-based activities and a design portfolio, students will develop the ability to evaluate AI and autonomous flight technologies, design system-level solutions, justify hardware and software choices, and consider the safety and responsible deployment of autonomous aerospace systems.
The module aims to enable students to:
1. Analyse UAV kinematics and autonomous flight behaviour.
2. Evaluate sensing, data processing, computer vision and AI algorithms for autonomous systems.
3. Create and evaluate autonomous control schemes using simulation-based methods.
4. Design and justify system-level UAV solutions integrating hardware, software, sensors, AI and control.
5. Evaluate responsible AI, safety and operational considerations in autonomous flight applications.
6. Communicate autonomous flight design and analysis effectively through technical documentation.
Outline Of Syllabus
The module will typically cover:
1. Principles of autonomous flight for fixed-wing and multi-copter UAVs.
2. Mathematical modelling and analysis of UAV kinematics.
3. Sensing technologies for autonomous flight systems.
4. Data processing methods for navigation, perception and decision-making.
5. Control algorithms for autonomous flight and dynamic stability.
6. Reinforcement learning for control of statically unstable flight systems.
7. Computer vision methods for UAV perception and environmental interpretation.
8. AI-enabled decision-making for autonomous aerospace systems.
9. Simulation environments for developing and evaluating autonomous flight solutions.
10. Hardware-oriented design considerations, including controller hardware, sensors, software integration, safety and responsible deployment.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 5:00 | 5:00 | Collation of design portfolio |
| Guided Independent Study | Assessment preparation and completion | 1 | 20:00 | 20:00 | Preparation of design portfolio |
| Guided Independent Study | Assessment preparation and completion | 1 | 2:00 | 2:00 | Completion of written exam |
| Scheduled Learning And Teaching Activities | Lecture | 20 | 1:00 | 20:00 | Lectures |
| Guided Independent Study | Assessment preparation and completion | 1 | 30:00 | 30:00 | Revision for written exam |
| 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 | 2:00 | 6:00 | Lab sessions on Computer vision in computer cluster |
| Scheduled Learning And Teaching Activities | Practical | 3 | 3:00 | 9:00 | Lectures/Lab/Tutorial sessions on autonomous flight in computer cluster |
| Guided Independent Study | Independent study | 1 | 23:00 | 23:00 | Reviewing lecture notes; general reading |
| Total | 200:00 |
Teaching Rationale And Relationship
The module uses lectures, tutorials, simulation-based activities and design-focused portfolio work to support Masters-level learning in applied AI and autonomous flight. Lectures develop advanced knowledge of UAV kinematics, sensing, computer vision, AI, reinforcement learning and autonomous control, supporting M1 and M2.
Tutorials and guided problem-solving activities develop students’ ability to analyse autonomous flight behaviour, evaluate algorithms and interpret system performance. Simulation-based activities support M3 and M6 by requiring students to develop and evaluate autonomous solutions in virtual environments. Design-focused work supports M5 and M13 by requiring students to justify hardware, software, sensing and control choices within defined engineering constraints.
Responsible AI, safety and deployment considerations support M8, while portfolio-based communication and reflection support M17 and M18.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Exams
| Description | Length | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|---|
| Written Examination | 90 | 1 | A | 75 | written examination |
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Design/Creative proj | 1 | M | 25 | Simulation based design task and portfolio - max 1000 words |
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 |
|---|---|---|---|
| Computer assessment | 1 | M | One Canvas-based formative assessment |
Assessment Rationale And Relationship
The assessment strategy combines a substantial written examination with an applied design task and portfolio to evidence both advanced technical understanding and practical systems integration.
The written examination, weighted at 75%, assesses students’ individual ability to apply and critically analyse autonomous flight, UAV kinematics, sensing, data processing, AI, computer vision and control concepts under time-constrained conditions. It provides primary evidence for M1 and M2, and can also support M3 and M6 where students are required to evaluate algorithms, simulated behaviours, control approaches and integrated autonomous system scenarios.
The design task and portfolio, weighted at 25%, provides applied evidence of students’ ability to develop and evaluate an autonomous UAV solution. Students analyse requirements, justify hardware, sensing, software, AI or control choices, evaluate simulation or design outputs, and reflect on assumptions, limitations and responsible deployment considerations. This supports M3, M5, M6, M8 and M13 by requiring students to create and evaluate a solution within defined engineering constraints.
The portfolio also provides evidence for M17 through structured technical communication of design decisions, system architecture, analysis, results and justified conclusions. Where students include reflection on their learning, development needs and future improvement, it can also support M18.
Overall, the assessment balance ensures robust individual assessment of advanced technical knowledge while retaining applied evidence of design, systems integration, technology selection, responsible AI considerations and professional communication.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- AER8014's Timetable