Postgraduate Course: Introduction to Vision and Robotics (INFR11126)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Only available to students of the Data Science, Technology and Innovation (DSTI) online distance learning programme.
This course introduces the basic concepts and methods in the field of robotics and computer vision. |
| Course description |
Robotics 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 course introduces the basic concepts and methods in these areas, and serves as an introduction to the more advanced robotics and vision modules.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | Only available to students of the Data Science, Technology and Innovation (DSTI) online distance learning programme |
Course Delivery Information
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| Academic year 2017/18, Not available to visiting students (SS1)
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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 )
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| Assessment (Further Info) |
Written Exam
70 %,
Coursework
30 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Coursework assignments (30%), Written exam (70%) |
| Feedback |
Students will get formative feedback from the course tutors while doing their coursework and summative feedback from their marked practicals, their exams and from live feedback during their coursework demonstrations. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
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| Main Exam Diet S1 (December) | | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- 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.
- 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.
- 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.
- 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.
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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, supplementary 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 materials 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 a lot of IVR some of AV |
Additional Information
| Graduate Attributes and Skills |
The activities of the course are designed to further develop intellectual skills in the areas of: laboratory, writing (lab reports and short essays), teamwork, critical analysis, programming and laboratory skills. |
| Special Arrangements |
Students will need to have high-speed internet access suitable for downloading and watching video content. |
| Keywords | Robotics,computer vision,image processing,artificial intelligence |
Contacts
| Course organiser | Dr Timothy Hospedales
Tel:
Email: |
Course secretary | Ms Katey Lee
Tel: (0131 6)50 2701
Email: |
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