Module Details

CE801-7-AU-CO: Intelligent Systems And Robotics

Year: 2017/18
Department: Computer Science and Electronic Engineering
Essex credit: 15
ECTS credit: 7.5
Available to Study Abroad / Exchange Students: No
Full Year Module Available to Study Abroad / Exchange Students for a Single Term: No
Outside Option: No

Supervisor: Professor Hani Hagras
Teaching Staff: Professor Hani Hagras
Contact details: School Office, e-mail csee-schooloffice (non-Essex users should add to create full e-mail address), Telephone 01206 872770.

Module is taught during the following terms
Autumn Spring Summer

Module Description

This module gives an introduction to intelligent systems and robotics. It goes on to consider the essential ardware for sensing and manipulating the real world, and their properties and characteristics. The module then considers kinematics, especially in the context of manipulators. The programming of intelligent systems and real-world robots are explored in the context of localisation, mapping, and fuzzy control. The module finishes by discussing the recent advances in robotics, especially multi-robotic systems and robot learning.

Learning Outcomes

On completion of the course, students should be able to:

1. Demonstration an understanding of a range of intelligent systems and robots
2. Explain the characteristics of a range of sensors and actuators
3. Explain the basic principles of robot kinematics, localisation and mapping
4. Make use of the principles of fuzzy logic in controlling real-world devices
5. Perform simple programming of a robot


Introduction to intelligent systems and robotics:

. A brief history of robotics, types of robots
. Robot challenges (RoboCup, DARPA Grand Challenge)
. Potential applications of intelligent systems and robotics
Sensors and Actuators:
. Sonar, laser scanner, optical encoders
. DC motors

. Feedback control
. Fuzzy controllers

Localisation and mapping
. Triangulation
. Kalman filter

Behaviour based programming
. Robot behaviours
. Potential field approach
. Behaviour based architecture

Learning and Teaching Methods

lectures and lab sessions


30 per cent Coursework Mark, 70 per cent Exam Mark


Assignment 1: Type of assignment - Practical assignment and report. Mode of submission via FASER. Weighting (as percentage of module mark) 30%. Week assignment issued is week 4. Week assignment to be submitted is week 16.

Other information



  • Recommended Reading
  • BEKEY, G., Autonomous Robots, The MIT Press, 2005, ISBN-10: 0262025787,
  • ISBN-13: 978-0262025782
  • MURPHY, R., Introduction to AI Robotics, The MIT Press, 2000, ISBN 0-262-13383-0