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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Introduction to Vision and Robotics (INFR09019)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 9 (Year 3 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryRobotics and Vision applies AI techniques to the problems of making devices capable of interacting with the physical world. This includes moving around in the world (mobile robotics), moving things in the world (manipulation robotics), acquiring information by direct sensing of the world (e.g. machine vision) and, importantly, closing the loop by using sensing to control movement. Applying AI in this context poses certain problems, and sets certain limitations, which have important effects on the general software and hardware architectures. For example, a robot with legs must be able to correct detected imbalances before it falls over, and a robot which has to look left and right before crossing the road must be able to identify approaching hazards before it gets run over. These constraints become much more serious if the robot is required to carry both its own power supply and its own brain along with it. This module introduces the basic concepts and methods in these areas, and serves as an introduction to the more advanced robotics and vision modules.
Course description The issues addressed will include the following:

* Applications of robotics and vision; the nature of the problems to be solved; historical overview and current state of the art.
* Robot actuators and sensors. Parallels to biological systems.
* Robot control: Open-loop, feed-forward and feedback; PID (proportional integral differential) control.
* Image formation, transduction and simple processing; thresholding, filtering and classification methods for extracting object information from an image.
* Active vision and attention.
* Sensors for self monitoring.
* General approaches and architectures. Classical vs. behaviour-based robotics. Wider issues and implications of robot research.

The course also involves hands-on practicals in which vision and robot systems will be programmed.

Relevant QAA Computing Curriculum Sections: Artificial Intelligence; Computer Vision and Image Processing
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Students MUST NOT also be taking Robotics: Science and Systems (INFR11092)
Other requirements This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser (lecturer).

This course assumes experience of AI knowledge and representation issues (equivalent to first and second year courses in Informatics); enough school algebra and geometry to understand the optics of image formation with lenses; enough school physics to understand Newton's Laws of Motion; the general mechanical intuitions required in such tasks as bicycle maintenance; enough electrical knowledge to understand how electric batteries make electric motors work. You are expected to be familiar with these mathematical methods: Bayes rule, Multivariate Gaussian Distribution, Covariance matrices, Convolution, the Jacobean (relating derivatives of a vector valued function to its vector valued inputs).
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2018/19, Available to all students (SV1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 16, Supervised Practical/Workshop/Studio Hours 10, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 70 )
Assessment (Further Info) Written Exam 70 %, Coursework 30 %, Practical Exam 0 %
Additional Information (Assessment) Reports on practical projects.

The coursework is designed to be done in teams of 2, where the workload
is shared, with the breakdown of:

* Assignment : 30 hours per person
Feedback Not entered
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S1 (December)2:00
Resit Exam Diet (August)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Students will be able to recall and explain the essential facts, concepts and principles in robotics and computer vision, demonstrated through written answers in examination conditions.
  2. Students will be able to describe and evaluate the strengths and weaknesses of some specific sensor and motor hardware; and some specific software methods for sensory processing and motor control, demonstrated through written answers in examination conditions.
  3. Students will be able to employ hardware (e.g. cameras, robots) and software (e.g. Matlab,robot simulator) tools to solve a practical problem of sensory-motor control, and will show a working system in a practical class.
  4. Students will, in writing a joint report, identify problem criteria and context, discuss design and development, test, analyse and evaluate the behaviour of the sensory-motor control system they have developed.
Reading List
Russell & Norvig Chapters 24 & 25 in Artificial Intelligence: A modern approach, Prentice Hall, 1995, ISBN: 0130803022 - Highly Recommended
Robin R. Murphy, Introduction to AI Robotics, MIT Press, 2000, ISBN: 0262133830, Recommended, suppementary for Robotics
Solomon and Breckon, Fundamentals of Digital Image Processing, Wiley-Blackwell, 2010, ISBN 978-0470844731, Highly Recommended
Ulrich Nehmzoe, Mobile Robotics: A Practical Introduction, 2nd Edition, Recommended
W. Burger, M Burge: principles of Digital Image Processing, Springer 2009, ISBN: 978-848001909, Covers some of IVR, AV matreials but maybe less than 50%, also on-line free inside the University
RC Gonzalez, RE Woods, SL Eddins: Digital Image Processing Using MATLAB, 2nd Edition, Prentice Hall 2009, ISBN: 9780982085400, Excellent but expensive, covers alot of IVR some of AV
E. Alpaydin, Introduction to Machine Learning, The MIT PRess, October 2004, ISBN: 0262012111, Recommended. Chapters are a deeper exploration of the Bayesin Classification topic
Additional Information
Course URL
Graduate Attributes and Skills Not entered
KeywordsNot entered
Course organiserDr Timothy Hospedales
Tel: (0131 6)50 4450
Course secretaryMiss Lisa Branney
Tel: (0131 6)51 7607
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