DSC8056 : Foundations of Autonomous Systems (Inactive)
- Inactive for Year: 2026/27
- Module Leader(s): Professor Wei Pan
- Lecturer: Professor Damian Giaouris
- Owning School: Engineering
- Teaching Location: Â鶹´«Ã½ 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 treats autonomy as a systems and control problem focusing on how sensing, modelling and feedback interact to enable reliable decision making in autonomous systems. Autonomy is introduced as a closed loop system structured around the sense-model-decide-act loop, integrating perception, probabilistic state estimation, modelling and control under uncertainty. The module develops the fundamental principles underpinning each stage, with particular emphasis on how uncertainty propagates through the system and how it can be managed through estimation and control.
The module covers the estimation and modelling tools that any autonomous system relies on (Kalman filtering, system identification, Gaussian processes for dynamics), alongside the optimal control methods (LQR, MPC) that connect models to actions. It also provides a conceptual introduction to safe autonomy, covering common failure modes and the motivation for safety constraints.
Students are expected to have working knowledge of Python and numerical computing. Those without this background are strongly advised to complete a Python refresher before the module begins.
Outline Of Syllabus
System architectures for autonomy: the sense-model-decide-act loop, modular vs end-to-end designs, layered architectures, real-time considerations
Sensing and perception: sensor modalities (LiDAR, camera, IMU, GNSS), sensor models and noise characteristics, multi-sensor fusion
Probabilistic state estimation: Bayesian filtering, Kalman filters (linear, extended, unscented), particle filters, observability, estimation convergence
Model learning and system identification: parametric and non-parametric methods, Gaussian process dynamics models, data-driven system identification, model validation, quantifying model uncertainty
Optimal control: linear quadratic regulation (LQR), model predictive control (MPC), constrained optimisation for control
Foundations of safe autonomy: common failure modes in autonomous systems, conceptual introduction to safety constraints
Revision and integration: Case studies from mobile robotics, autonomous vehicles and multi-robot systems used as integrative context
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 25 | 1:00 | 25:00 | in-person lectures |
| Guided Independent Study | Assessment preparation and completion | 8 | 2:00 | 16:00 | Formative assessment preparation |
| Guided Independent Study | Assessment preparation and completion | 1 | 40:00 | 40:00 | Exam preparation |
| Guided Independent Study | Assessment preparation and completion | 1 | 2:00 | 2:00 | 2 hour exam |
| Scheduled Learning And Teaching Activities | Practical | 5 | 3:00 | 15:00 | Computer Practicals |
| Structured Guided Learning | Structured research and reading activities | 11 | 2:00 | 22:00 | Guided reading of key papers and textbook chapters |
| Guided Independent Study | Independent study | 1 | 80:00 | 80:00 | Reviewing lecture notes, self-study, general reading |
| Total | 200:00 |
Teaching Rationale And Relationship
Lectures cover the core theory in sequence: sensing and estimation first, then model learning, then optimal control, then safety — following the natural pipeline of an autonomous system (Knowledge Outcomes 1-6). This ordering means students always see where each new topic fits in the bigger picture.
Practical lab sessions give students hands-on experience implementing the methods from lectures (Skill Outcomes 1-4, 6). Early labs are structured (e.g. implement a Kalman filter for a provided system), while later labs are more open-ended (e.g. design an MPC controller for a simulated vehicle). All labs use Python.
Guided reading is set each week to supplement the lectures with textbook material and selected papers. This helps students see how the methods are used in practice and develops their ability to read technical literature critically (Skill Outcome 5).
The independent study hours cover revision, deeper reading around topics of interest, and preparation for the exam.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Exams
| Description | Length | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|---|
| Written Examination | 120 | 1 | A | 100 | 2-hour In-Person Closed-Book Exam |
Zero Weighted Pass/Fail Assessments
| Description | When Set | Comment |
|---|---|---|
| Prob solv exercises | M | Tutorial sheets and Class Examples Released each week or after a topic is completed – expected to take 2 hours to complete each sheet – approx 8 sheets |
Assessment Rationale And Relationship
The exam tests whether students can work through estimation, model learning and control problems under time pressure, and whether they understand the theory well enough to apply it to unseen scenarios (Knowledge Outcomes 1-6).
Weekly problem sheets are released throughout the semester so students can practise and receive formative feedback before the summative assessments.
Alternative forms of assessment are available where required. These could include oral exams and digital exams
Reading Lists
Timetable
- Timetable Website: