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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2012/2013
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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Robotics: Science and Systems (INFR11092)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Course typeStandard AvailabilityAvailable to all students
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) Credits20
Home subject areaInformatics Other subject areaNone
Course website http://www.inf.ed.ac.uk/teaching/courses/rss/ Taught in Gaelic?No
Course descriptionThis 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 Co-requisites
Prohibited Combinations Students MUST NOT also be taking Introduction to Vision and Robotics (INFR09019) OR Intelligent Autonomous Robotics (Level 10) (INFR10005)
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-requisitesNone
Displayed in Visiting Students Prospectus?Yes
Course Delivery Information
Delivery period: 2012/13 Semester 1, Available to all students (SV1) Learn enabled:  No Quota:  None
Location Activity Description Weeks Monday Tuesday Wednesday Thursday Friday
CentralLecture1-11 09:00 - 10:50
CentralLecture1-11 09:00 - 10:50
First Class Week 1, Thursday, 09:00 - 10:50, Zone: Central. F.21 7 George Square
Exam Information
Exam Diet Paper Name Hours:Minutes
Main Exam Diet S2 (April/May)2:00
Delivery period: 2012/13 Semester 1, Part-year visiting students only (VV1) Learn enabled:  No Quota:  None
Location Activity Description Weeks Monday Tuesday Wednesday Thursday Friday
CentralLecture1-11 09:00 - 10:50
CentralLecture1-11 09:00 - 10:50
First Class Week 1, Thursday, 09:00 - 10:50, Zone: Central. F.21 7 George Square
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.
Study Abroad Not entered
Study Pattern Lectures: 30
Tutorials: 0
Timetabled Laboratories: 8
Non-timetabled assessed assignments: 42
Private Study/Other: 100
KeywordsNot entered
Contacts
Course organiserDr Michael Rovatsos
Tel: (0131 6)51 3263
Email: mrovatso@inf.ed.ac.uk
Course secretaryMiss Kate Weston
Tel: (0131 6)50 2701
Email: Kate.Weston@ed.ac.uk
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