MSc Advanced Control and Systems Engineering / Course details

(1) Robotic manipulators

(1.1) Introduction      

(1.2) Actuators and sensors

(1.3) Kinematics and path planning

(1.4) Dynamics

(1.5) Multivariable and advanced control

 (2) Autonomous Systems

(2.1) Introduction to probabilities

(2.2) Introduction to autonomous systems

(2.3) Uncertainty propagation in autonomous systems

(2.4) Map-based localization

(2.5) Mapping

(2.6) Introduction to SLAM (Simultaneous Localization and Mapping)

(2.7) Reactive navigation

(2.8) Path-planning

(2.9)  Applications

The course unit aims to:

(1)    Provide students with understanding of modern autonomous systems and robotic manipulators.

(2)    Enable the students to model robots.

(3)    Enable the students to design linear and nonlinear controllers for robotic manipulators.

(4)    Provide students with an overview on main topics encountered in autonomous systems field such as localization techniques, mapping, SLAM, navigation, path-planning.

Students will be able to:

Knowledge and understanding

(1) Demonstrate understanding of robotic manipulators, their modelling and control.

(2) Demonstrate understanding of main topics encountered in Autonomous Systems field.

Intellectual skills

(1) Use Lagrange formulation to derive equations of motion.

(2) To understand the most important question in Autonomous Systems field: “How much information and support must be provided by human to ensure that the robot is able to achieve its goals”. 

Practical skills

(1) Model robot arms and design controllers

(2) Autonomy in mobile robots

Transferable skills and personal qualities

(1)    Apply modelling techniques to other problems.

(2)    Apply control techniques to other problems.

(3)    Apply robotic techniques to other control related problems

(1) Demonstrate understanding of robotic manipulators, their modelling and control.

(2) Demonstrate understanding of main topics encountered in Autonomous Systems field.

(1) Use Lagrange formulation to derive equations of motion.

(2) To understand the most important question in Autonomous Systems field: “How much information and support must be provided by human to ensure that the robot is able to achieve its goals”.  

(1) Model robot arms and design controllers

(2) Autonomy in mobile robots 

(1)    Apply modelling techniques to other problems.

(2)    Apply control techniques to other problems.

(3)    Apply robotic techniques to other control related problems

Unseen Written Examination:

Four questions, answer all questions

Length of examination: 3 hours

Calculators are permitted

The unseen written examination forms 70% of the unit assessment

Course Work:

Lab and Assignment 1: Autonomous Systems – Medium level autonomy

1.1 Motion-based Localisation

1.2 Reactive Navigation (Obstacle Avoidance)

1.3 Motion Control

Submission date: A report based in the lab must be submitted on Sunday, Week 7, Semester 2

Maximum mark for assignment 1 forms 10% of the overall unit mark

Lab and Assignment 2: Autonomous Systems – High level autonomy

2.1 Binary & Probabilistic Mapping

2.2 Mapping with Forgetting Factor

2.3 Map-based Localisation

2.4 Path-Planning

Submission date: A report based on the course work must be submitted on Sunday, Week 8, Semester 2

Maximum mark for assignment 2 forms 20% of the overall unit mark