Master’s Degree Programme in Robotics and Autonomous Systems (RAS)

(To be offered in 2023/2024)

Compulsory Courses

This course aims at teaching students basic knowledges and more recent advances on mobile robotics, such as robot sensing, localization, mapping, and motion planning.

Introduction to sensors and actuators, especially flexible devices. These sensors and actuators have wide applications in wearable electronics, Internet of Things, robotics, and mobile health, etc. This course prepares students to work professionally in academic researching or companies focusing on advanced technology.

Project Report is a task of the capstone project focusing on combining existing academic theories or advanced technologies with an evaluation of a case study or academic project. Students will be asked to select a topic, or an engineering problem that arouses their interests, conduct literature review and research on the topic, design the experimental or testing methodology, acquire a portfolio of findings or results, create a final product or a written report demonstrating their learning acquisition or conclusions and finally give an oral presentation on the capstone project to a panel who collectively evaluate the quality of the task.

Required Elective Courses

4 required electives chosen from Group 1 (12 credits)


2 required electives chosen from Group 2 (6 credits)

Total Credits 30 credits

List of Required Elective Courses (3 credits for each course)


This is a project oriented course of Embedded System for postgraduate students. It emphasizes general concepts and design techniques of embedded system. Topics include overview of embedded system, real-time system, hardware and software co-design, and components selection.


Analyzes kinematic characteristics of planar and spatial manipulators. Differential kinematics and statics. Dynamics. Trajectory planning. Introduction to feedback control of physical system behavior. State-space and functional descriptions of linear and nonlinear systems. Feedback, stability, and robustness. Design of PID controllers and compensators. Interaction control. Actuators and sensors. Robot control architecture.

This course introduces the fundamentals of intelligent system technologies and their engineering applications. It will present the principles of knowledge-based systems, fuzzy logic, artificial neural networks, evolutionary computing, and explore how intelligent machines and automation could benefit from application of these technologies. It will also discuss the representation of knowledge, knowledge acquisition, decision making mechanism, learning and machine learning, as well as its applications in various engineering domains.

This course aims to introduce students to the fundamental concepts involved in the design and operation of aerial robots, visiting the topics of rotorcraft modelling, navigation guidance and control, as well as motion planning. In particular we will derive dynamic models of robotic quadrotors, discuss the available sensors to be installed onboard and sensor fusion techniques rooted on the concepts of Kalman Filtering for linear and nonlinear systems, present methods for aerial robot control with stability analysis, and basic algorithms for efficient robot motion in unstructured environments. Finally, some applications are presented and future uses of aerial robots are discussed.


Any specialized topic in Robotics chosen by staff member who has experience in that particular field, but the topic is not covered by the other postgraduate courses in the MSc. programme.


This course will give students an in-depth perspective on the most advanced techniques and algorithms currently used in wheeled mobile, aerial, space, and ocean autonomous robots, working in unstructured environments. These techniques will include recent results from machine vision, robotic image and video processing, sensor-based control for autonomous robots, sensor fusion for robot pose estimation, advanced nonlinear motion estimation and control, simultaneous localization and mapping, learning and deep neural networks, perception and compliance of human behaviour, and advanced robotic trajectory tracking and path planning. Case studies of successful algorithms employed in single and multiple autonomous robots will be presented and discussed.


List of Required Elective Courses (3 credits for each course)

This course is designed for students who are new to the world of data science. After the introduction of some basic arithmetic, variables, and data structures in Python, students will start to learn how to collect and extract data from real datasets. Some data analytical skills using the control flows and Python packages (e.g., NumPy, SciPy, Pandas, etc.) will be introduced. To address the needs of big data processing, some distributed computing frameworks (e.g., Spark) and visualization tools with Python will be discussed. Students may apply some basic learning algorithms with Python packages (e.g., scikit-learn) to extract knowledge from data.
The course will start from the very beginning of the ML basis. First, the basic concepts such as liner algebra; probability and information theory, and numerical methods will be introduced. Next machine learning overview, inductive learning, and representation learning will be introduced. Basic deep learning processes are designed as artificial neural network; Bayesian Networks and learning; Deep learning and deep neural networks; convolution neural network. Throughout the course, practical methodology of using tools such as Tensorflow or Karas etc. will be be emphasized.
This course introduces students to advanced topics in Internet of Things. The detailed contents may change from year to year depending on current developments and teacher specialization.
This course provides the concepts and methods of prognostics and health management (PHM) of engineering system, which describes PHM techniques and their applications in engineering systems. A variety of tools and techniques for developing health management and monitoring of components and systems will be discussed. Topics related to sensor signal acquisition, data pre-processing techniques, various signals processing methods for feature extraction, machine learning methods and data driven prognostics models. After successfully completing this course, students will have a good understanding of system health monitoring, optimum sensor placement for health assessment, and current challenges and opportunities in the PHM field.