Postgraduate Course: Robotics: Science and Systems (INFR11092)
Course Outline
School | School of Informatics |
College | College of Science and Engineering |
Course type | Standard |
Availability | Available to all students |
Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Credits | 20 |
Home subject area | Informatics |
Other subject area | None |
Course website |
None |
Taught in Gaelic? | No |
Course description | This course will be a Masters degree level introduction to several core areas in robotics: kinematics, dynamics and control; motion planning; state estimation, localization and mapping; vision for robotics. Lectures on these topics will be complemented by a large practical that exercises knowledge of a cross section of these techniques in the construction of an integrated robot in the lab, motivated by a task such as robot navigation. Also, in addition to lectures on algorithms and lab sessions, we expect that there will be several lecture hours dedicated to discussion of implementation issues - how to go from the equations to code.
The aim of the course is to present a unified view of the field, culminating in a practical involving the development of an integrated robotic system that actually embodies key elements of the major algorithmic techniques. |
Entry Requirements (not applicable to Visiting Students)
Pre-requisites |
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Co-requisites | |
Prohibited Combinations | |
Other requirements | Multivariate Calculus, Linear Algebra and matrix manipulations, Basic notions of Statistics and concepts including expectation and conditional probability |
Additional Costs | None |
Information for Visiting Students
Pre-requisites | None |
Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
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Delivery period: 2012/13 Semester 1, Available to all students (SV1)
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Learn enabled: No |
Quota: None |
Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
Central | Lecture | | 1-11 | 09:00 - 10:50 | | | | | Central | Lecture | | 1-11 | | | | 09:00 - 10:50 | |
First Class |
First class information not currently available |
Exam Information |
Exam Diet |
Paper Name |
Hours:Minutes |
|
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Main Exam Diet S2 (April/May) | | 2:00 | | |
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Delivery period: 2012/13 Semester 1, Part-year visiting students only (VV1)
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Learn enabled: No |
Quota: None |
Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
Central | Lecture | | 1-11 | 09:00 - 10:50 | | | | | Central | Lecture | | 1-11 | | | | 09:00 - 10:50 | |
First Class |
First class information not currently available |
Exam Information |
Exam Diet |
Paper Name |
Hours:Minutes |
|
|
Main Exam Diet S1 (December) | | 2:00 | | |
Summary of Intended Learning Outcomes
- model the motion of robotic systems in terms of kinematics and dynamics
- analyse and evaluate a few major techniques for feedback control, motion planning and computer vision as applied to robotics
- translate a subset of standard algorithms for motion planning, localization and computer vision into practical implementations
- implement and evaluate a working, full robotic system involving elements of control, planning, localization and vision |
Assessment Information
Written Examination 50
Assessed Practicals 40
Assessed Assignments 10 |
Special Arrangements
None |
Additional Information
Academic description |
Not entered |
Syllabus |
The tentative coverage of topics is as follows:
- Kinematics - forward and inverse
- Dynamics
- Control
- Sensing - proprioception, etc.
- Motion planning - basics and sampling based methods
- Motion planning - planning under uncertainty, etc.
- State estimation, localization and mapping
- Implementing SLAM; Multi-modal sensor fusion
- Image acquisition
- Edge detection and segmentation
- Shape description and matching
- Two-view geometry
- Interest points and regions
- Recognition of specific objects
- Visual servoing and ego-motion estimation |
Transferable skills |
Not entered |
Reading list |
H. Choset, K.M. Lynch, S. Hutchinson, G. Kantor, Principles of Robot Motion: Theory, Algorithms, and Implementations.
S. Thrun, W. Burgard and D. Fox, Probabilistic Robotics.
D.A. Forsyth, J. Ponce, Computer Vision: A Modern Approach.
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Study Abroad |
Not entered |
Study Pattern |
Lectures: 30
Tutorials: 0
Timetabled Laboratories: 8
Non-timetabled assessed assignments: 42
Private Study/Other: 100 |
Keywords | Not entered |
Contacts
Course organiser | Dr Michael Rovatsos
Tel: (0131 6)51 3263
Email: mrovatso@inf.ed.ac.uk |
Course secretary | Miss Kate Weston
Tel: (0131 6)50 2701
Email: Kate.Weston@ed.ac.uk |
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© Copyright 2012 The University of Edinburgh - 31 August 2012 4:12 am
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