Postgraduate Course: Robotics: Science and Systems (INFR11092)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | 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. |
| Course description |
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
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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 | 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).
Multivariate Calculus, Linear Algebra and matrix manipulations, Basic notions of Statistics and concepts including expectation and conditional probability. General programming competence is assumed. The course will use C++ in a Linux environment, GIT and OpenCV. |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2017/18, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 30,
Supervised Practical/Workshop/Studio Hours 8,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
156 )
|
| Assessment (Further Info) |
Written Exam
50 %,
Coursework
50 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
You should expect to spend approximately 42 hours on the coursework for this course.
If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year. |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S1 (December) | | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- 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
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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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Contacts
| Course organiser | Dr Zhibin Li
Tel:
Email: |
Course secretary | Ms Katey Lee
Tel: (0131 6)50 2701
Email: |
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