Mobile Robotics

The details
Computer Science and Electronic Engineering (School of)
Colchester Campus
Undergraduate: Level 6
Thursday 03 October 2024
Friday 13 December 2024
26 April 2024


Requisites for this module



Key module for

BENGGH46 Computers with Electronics (Including Foundation Year),
BENGGH4P Computers with Electronics,
BENGGH4Q Computers with Electronics (Including Year Abroad),
BENGI1H6 Computers with Electronics (Including Placement Year),
BENGH615 Robotic Engineering,
BENGH616 Robotic Engineering (Including Year Abroad),
BENGH617 Robotic Engineering (Including Placement Year),
BENGH618 Robotic Engineering (Including Foundation Year),
BENGH730 Mechatronic Systems,
BENGH731 Mechatronic Systems (Including Year Abroad),
BENGH732 Mechatronic Systems (Including Placement Year),
BENGH733 Mechatronic Systems (Including Foundation Year),
BSC H737 Mechatronics,
BSC H738 Mechatronics (including Placement Year),
BSC H739 Mechatronics (including Year Abroad),
BSC H717 Robotics,
BSC H718 Robotics (including Placement Year),
BSC H719 Robotics (including Year Abroad)

Module description

This module provides a general understanding of AI robotics that has wide 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. They are generally characterised by real-time performance, autonomous operation and learning capabilities.

Module aims

The aim of this module is:

  • To be aware of the rich variety of AI robotic applications in the real world; understand performance needs of mobile robots; real time operation; autonomy; asynchronous events; computer architectures; and the use of different methods of data interpretation.

Module learning outcomes

By the end of this module, students will be expected to be able 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. Design, program and evaluate autonomous mobile robots and intelligent machines, from sensing to action.

Module information

Outline Syllabus

  1. Introduction to the course: review of AI robotic systems and embedded computing architectures.

  2. Application domain characteristics: the complex, unpredictable and dynamic natures of the world; timeliness, autonomy and intelligence.

  3. 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.

  4. Architectures for mobile robots & intelligent machines: comparison of reactive versus cognitive architecture; examination of hierarchical sensory-interactive and behaviour based approaches.

  5. Data interpretation & representation: local and global map building such as quadtree, occupancy grid, Veronoi diagram; representation of uncertainty; multi-sensor data fusion.

  6. Implementation issues: mapping models to hardware and software via modularization; configuration flexibility; multi/distributed processing; development tools and simulation environment.

Basic C/C++ programming skills are needed.

Learning and teaching methods

This module will be delivered via:

  • Lectures
  • Laboratories


This module does not appear to have a published bibliography for this year.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Simulated Robot Navigation     100% 
Exam  Main exam: In-Person, Open Book (Restricted), 120 minutes during Summer (Main Period) 
Exam  Reassessment Main exam: In-Person, Open Book (Restricted), 120 minutes during September (Reassessment Period) 

Exam format definitions

  • Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
  • In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
  • In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
  • In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework Exam
30% 70%


Coursework Exam
30% 70%
Module supervisor and teaching staff
Prof Huosheng Hu, email: hhu@essex.ac.uk.
Professor Huosheng Hu
School Office, email: csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770



External examiner

Dr Shadan Khan Khattak
Cardiff Metropolitan University
Senior Lecturer
Available via Moodle
No lecture recording information available for this module.


Further information

* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.

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