Module Details

CE315-6-SP-CO: Mobile Robotics

Year: 2016/17
Department: Computer Science and Electronic Engineering
Essex credit: 15
ECTS credit: 7.5
Available to Study Abroad / Exchange Students: Yes
Full Year Module Available to Study Abroad / Exchange Students for a Single Term: No
Outside Option: No

Supervisor: Professor Huosheng Hu
Teaching Staff: Professor Huosheng Hu
Contact details: CSEE School Office, email: 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

Learning Outcomes

The aim of this module is to provide a general understanding of AI robotics that has widely potential applications in the real world. Various different approaches are reviewed together with associated design methodologies. Autonomous mobile robots are intelligent machines that have many embedded computers, sensors and actuators which interact intelligently in the real world. They are generally characterised by real-time performance, autonomous operation
and learning capabilities.

After completing this module, students will be expected to:

1. be aware of the rich variety of AI robotics applications in the real world;
2. understand the performance needs of mobile robots in terms of characteristics such as real-time operation, autonomy, asynchronous event handling, modularity, flexibility and robustness;
3. appreciate the advanced computer architectures that may be adopted to build intelligent machines in general, mobile robots in particular;
4. recognise different methods for data interpretation and representation, including sensor uncertainty, local and global map building, as well as multi-sensor data fusion
5. be able to design, program and evaluate autonomous mobile robots and intelligent machines, from sensing to action.

Outline Syllabus
. Introduction to the course: review of AI robotic systems and embedded computing architectures.
. Application domain characteristics: the complex, unpredictable and dynamic natures of the world; timeliness, autonomy and intelligence.
. Intelligent embedded machine characteristics: uncertainty such as sensor noise,
imprecision & sparseness of data; slow processing and small memory; field support such as user-interface and tools.
. Architectures for mobile robots & intelligent machines: comparison of reactive versus cognitive architecture; examination of hierarchical sensory-interactive and behaviour based approaches.
. Data interpretation & representation: local and global map building such as quadtree, occupancy grid, Veronoi diagram; representation of uncertainty; multi-sensor data fusion.
. Implementation issues: mapping models to hardware and software via modularization; configuration flexibility; multi/distributed processing; development tools and simulation environment.

Learning and Teaching Methods

Lectures and Laboratories


40 per cent Coursework Mark, 60 per cent Exam Mark


Assignment 1 - Simulation - weighting = 20%, submitted in wk 21, Assignment 2 - Real robot - weighting = 20%, submitted in wk 30.

Exam Duration and Period

2:00 during Summer Examination period.

Other information




  • Recommended reading
  • MURPHY R.R., Introduction to AI Robotics, the MIT Press, ISBN 0-262-13383-0, 2000
  • BEKEY, G.A., Autonomous Robots - From Biological Inspiration to Implementation and Control, the MIT Press, ISBN 0-262-02578-7, 2005
  • Various lecture notes and review papers will be distributed during the course.

External Examiner Information

  • Name: DR Pandelis Kourtessis
    Institution: University of Hertfordshire
    Academic Role: Reader