CS31315 Mobile Robotics
Introductory description
The main aim of the module is to provide an understanding of the fundamental principles of mobile robotics and related concepts. The module introduces various mechanisms of mobility for different kinds of mobile robots, algorithms and data structures for safe navigation of the robot, and some techniques for equipping the robot with an intelligent vision system.
Module aims
The main aim of the module is to provide an understanding of the fundamental principles of mobile robotics and related concepts. The module introduces various mechanisms of mobility for different kinds of mobile robots, algorithms and data structures for safe navigation of the robot, and some techniques for equipping the robot with an intelligent vision system.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
 Introduction to mobile robots
 Sensors
 State Estimation
 Discrete Filter
 Linear Gaussian Filter
 Nonparametric Filters
 Mapping
 SLAM
 Motion Planning
 Markov Decision Process
Learning outcomes
By the end of the module, students should be able to:
  Demonstrate an understanding of the underlying principles of mobile robotics
  Demonstrate a knowledge of the applications of mobile robotics
  Apply these to analyse and solve realworld problems
Indicative reading list
(a) Sebastian Thrun, Wolfram Burgard, Dieter Fox, Probabilistic Robotics, MIT Press, 2005.
(b) Siegwest and Nourbakhsh, Introduction to Autonomous Mobile Robots, MIT Press, 2004.
(c) Dudek G and Jenkin M, Computational Principles of Mobile Robotics, Cambridge University Press, 2000.
(d) Craig JJ, Introduction to Robotics: Mechanics and Control (3rd ed), PrenticeHall, 2005.
(e) Gonzalez R and Woods RC, Digital Image Processing, PrenticeHall, 2002.
Subject specific skills
 A fair grasp of knowledge about the following mathematical tools is required: trigonometry, matrix algebra, vector spaces, and differential equations.
Transferable skills
To be confirmed
Study time
Type  Required 

Lectures  20 sessions of 1 hour (13%) 
Practical classes  5 sessions of 2 hours (7%) 
Private study  120 hours (80%) 
Total  150 hours 
Private study description
 Background reading
 Study lecture materials
 Team discussion and work for lab materials
 Revision
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Students can register for this module without taking any assessment.
Assessment group D1
Weighting  Study time  

Group lab report  20%  
Group report of about 3000 words plus individual report of about 1000 words 

Online Examination  80%  

Assessment group R
Weighting  Study time  

Online Examination  Resit  100%  
CS313 resit examination

Feedback on assessment
Written feedback for the lab report will be provided on Tabula
Prerequisites
 Ideally the student would find it useful to have completed CS130 Mathematics for Computer Scientists I, CS131 Mathematics for Computer Scientists II, ES107 Mathematics for Engineers, or a similar Mathematics module.
Courses
This module is Optional for:

UCSAG4G1 Undergraduate Discrete Mathematics
 Year 3 of G4G1 Discrete Mathematics
 Year 3 of G4G1 Discrete Mathematics
 Year 3 of UCSAG4G3 Undergraduate Discrete Mathematics
 Year 4 of UCSAG4G2 Undergraduate Discrete Mathematics with Intercalated Year
This module is Option list A for:
 Year 4 of UCSAG504 MEng Computer Science (with intercalated year)

UCSAG500 Undergraduate Computer Science
 Year 3 of G500 Computer Science
 Year 3 of G500 Computer Science

UCSAG502 Undergraduate Computer Science (with Intercalated Year)
 Year 4 of G502 Computer Science with Intercalated Year
 Year 4 of G502 Computer Science with Intercalated Year

UCSAG503 Undergraduate Computer Science MEng
 Year 3 of G500 Computer Science
 Year 3 of G503 Computer Science MEng
 Year 3 of G503 Computer Science MEng
 Year 3 of UCSAG406 Undergraduate Computer Systems Engineering
 Year 3 of UCSAG408 Undergraduate Computer Systems Engineering
 Year 4 of UCSAG407 Undergraduate Computer Systems Engineering (with Intercalated Year)
 Year 4 of UCSAG409 Undergraduate Computer Systems Engineering (with Intercalated Year)
This module is Option list B for:

USTAG302 Undergraduate Data Science
 Year 3 of G302 Data Science
 Year 3 of G302 Data Science
 Year 3 of USTAG304 Undergraduate Data Science (MSci)
 Year 4 of USTAG303 Undergraduate Data Science (with Intercalated Year)