Mobile Robotics

The details
Computer Science and Electronic Engineering (School of)
Colchester Campus
Undergraduate: Level 6
Monday 13 January 2020
Friday 20 March 2020
07 May 2019


Requisites for this module



Key module for

BENGH615 Robotic Engineering,
BENGH616 Robotic Engineering (Including Year Abroad),
BENGH617 Robotic Engineering (Including Placement Year),
BENGH730 Mechatronic Systems,
BENGH731 Mechatronic Systems (Including Year Abroad),
BENGH732 Mechatronic Systems (Including Placement Year)

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

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.

Module information

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.

Basic C/C++ programming skills are needed.

Learning and teaching methods

Lectures and Laboratories


This module does not appear to have any essential texts. To see non-essential items, please refer to the module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Weighting
Coursework Assignment 1- Simulation - Week 20 13/02/2020 50%
Coursework Assignment 2 - Real Robot 19/03/2020 50%
Exam 120 minutes during Summer (Main Period) (Main)

Overall assessment

Coursework Exam
40% 60%


Coursework Exam
40% 60%
Module supervisor and teaching staff
Professor Huosheng Hu
CSEE 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 Robert John Watson
University of Bath
Senior Lecturer
Available via Moodle
Of 82 hours, 22 (26.8%) hours available to students:
60 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).


Further information

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