Robotics: Science and Systems (R:SS) Course Webpage
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; visual geometry, recognition of textured objects, shape matching and object categorization.
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. NOTE: This is a 20 pt course, as opposed to the standard 10 pt courses since this covers two introductory topics: robotics and vision and a practical element.
When and Where?
When: 9:00 - 10:50 (with 10 min. break) on Mondays and Thursdays.
First Lecture: 21 Sep (Mon) 9:00-10:50 @ G.08 1 George Square
Monday 11.00 - 13.00 (Forrest Hill G.A11) - first practical on 28 Sep (Mon)
Thursdays 11.00 - 13.00 (Forrest Hill G.A11) - first practical on 1 Oct (Thu)
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.
Written Examination 50
Assessed Practicals 40
Assessed Assignments 10
Professor Sethu Vijayakumar - sethu.vijayakumar[at]ed.ac.uk
Dr Subramanian Ramamoorthy - s.ramamoorthy[at]ed.ac.uk
Professor Chris Williams - ckiw[at]inf.ed.ac.uk; Office hour Tues 9.00-9.45 Appleton Tower foyer in weeks 2-7.
Dr Vladimir Ivan - v.ivan[at]ed.ac.uk
Wolfgang Merkt - wolfgang.merkt[at]ed.ac.uk
Garry Ellard - gde[at]inf.ed.ac.uk
Tony Shade - ashade[at]inf.ed.ac.uk
Vision demo code
Available from https://github.com/svepe/rss-demos
Lecture plan (provisional)
Lecture time: 9:00 - 10:50 (with 10 min. break) on Mondays and Thursdays.
- Peter Corke, Robotics, Vision and Control, Springer-Verlag.
- Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G., Robotics: Modelling, Planning and Control, Springer Verlag.
- 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, 2nd edition, Pearson 2012.
- R. Szeliski. Computer Vision: Algorithms and Applications, Springer, 2011
- J. J. Craig, Introduction to Robotics: Mechanics and Control (3rd Edition), [pdf]: Use for first 3 chapters only.