School of Computer Science

Module 06-30244 (2022)

Intelligent Robotics (Extended)

Level 4/M

Mohan Sridharan Masoumeh Mansouri Semester 1 20 credits
Co-ordinator: Mohan Sridharan
Reviewer: Masoumeh Mansouri

The Module Description is a strict subset of this Syllabus Page.


Artificial Intelligence is concerned with the design and use of computer systems to understand and mimic human-level decision making. These systems represent, reason with, and learn from, different descriptions of knowledge and uncertainty. In this module we will address these issues in the context of intelligent mobile robots. The lectures will teach theories of perception, estimation, prediction, decision- making, learning and control, all from the perspective of robotics. In the laboratory sessions students will implement some of these theories on simulated or real robots to see how theory can be applied in practice.

Learning Outcomes

On successful completion of this module, the student should be able to:

  • Demonstrate an understanding of algorithms for perception, reasoning and control
  • Implement some of these algorithms to generate intelligent behaviour on a robot
  • Describe and analyse the performance of algorithms, system components and complete robot systems using appropriate methods
  • Design, execute and write up appropriate experiments and use their results to inform robot design
  • The student should demonstrate the capacity to independently study, understand, and critically evaluate advanced materials or research articles in the subject areas covered by this module.


  • There is a limited number of places determined by the number of robot platforms available. This will be determined each September in combination with Intelligent Robotics (LH). Students who have either module as COMPULSORY have, by definition, priority. The Module leader will then assess and allocate any remaining spaces to students who have chosen either module as an OPTION.
  • This module expects students to be proficient in linear algebra, calculus, probability theory, and statistics. A high level of proficiency is also expected in object oriented programming and in using (e.g., installing and debugging software packages, programming in) the Linux operating system. This module is time-intensive.

Taught with

Cannot be taken with


  • Main Assessments: 1.5 hour examination (50%) and continuous assessment (50%)
  • Supplementary Assessments: 1.5 hour examination (100%)

Programmes containing this module