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.
Where: Monday = LT270 OC (Old College), Thursday = F.21 7GSQ (7 George Square)
First Lecture: 20 Sep (Thu) 9:00-10:50 @ F.21 7GSQ (7 George Square)
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 (Primary contact)
Dr Subramanian Ramamoorthy - s.ramamoorthy[at]ed.ac.uk
Dr Vittorio Ferrari - vittorio.ferrari[at]ed.ac.uk
Paul Ardin - s8528002[at]sms.ed.ac.uk
Vladimir Ivan - v.ivan[at]ed.ac.uk
Robert McGregor - robertm[at]inf.ed.ac.uk
Garry Ellard - gde[at]inf.ed.ac.uk
Lecture time: 9:00 - 10:50 (with 10 min. break) on Mondays and Thursdays.
||Introduction; Notations, Transformations, Rotations (1h15mim), Primer for the Practicals (30min)
||24-Sep-2012||Notes. 2 Intro
Notes. 2 Image Formation
||Image acquisition: basic world-to-image geometry and color spaces (1h); Two-view geometry: setting, notion of point correspondences, transformation classes for planar objects: similarity, affine, homography (1h)|
||Sethu Vijayakumar||Kinematic (Forward, Inverse), Jacobian, Operational Space, Null Space, Optimality Principles (2h)
||1-Oct-2012||Notes. 4 Path Planning||Subramanian Ramamoorthy
||Introduction to path planning methods
||Practicals (Wed.): Robot is able to move around.
||4-Oct-2012||Notes. 5 Two View Geometry||Vittorio Ferrari||Two-view geometry: fundamental matrix (properties and estimation), invariance classes, invariants for planar configurations of points and lines
||Practicals (Thu.): Robot is able to move around.|
||8-Oct-2012||Notes. 6 Motion Planning||Subramanian Ramamoorthy||Sampling based path/motion planning
||Practicals (Wed.): Obstacle avoidance.
||11-Oct-2012||Notes. 7 State Estimation||Subramanian Ramamoorthy||State estimation||Practicals (Thu.): Obstacle avoidance.
||15-Oct-2012||Notes. 8 Interest Points||Vittorio Ferrari||Implementation issues for homography and fundamental matrix estimation (1h); Interest points and regions: general concept, plain Harris, scale-invariant Harris (1h)|
||18-Oct-2012||Notes. 9 Feature Matching||Vittorio Ferrari||Interest points and regions: affine-invariant IBR and MSER (1h); implementation issues (1h)||Assignment 1|
||22-Oct-2012||Notes.3 Kinematics (cont'd)
|Sethu Vijayakumar||Kinematic and multi-objective motion planning (1h), Dynamics: Point mass, PID, Newton Euler, Joint Space, Optimal Operational Space Control, Non-holonomic sytems (1h)
||25-Oct-2012||Notes. 11 Affine features
Notes. 11 Specific object recognition
|Vittorio Ferrari||Specific object recognition: global descriptors, interest point/region descriptors (SIFT, moments), matching interest points/regions, filtering mismatches with geometric consistency (local consistency tests, global consistency tests with RANSAC)
||29-Oct-2012||Notes.10 Dynamics (cont'd)
SOC Additional Notes
|Sethu Vijayakumar||Dynamics (cont'd) (1h); Control: Intro to Optimal Control, HJB equations, LQR (1h)
||Practicals (Wed.): Resource identification.
||1-Nov-2012||Notes. 13 SLAM||Subramanian Ramamoorthy||Localization and Mapping||Practicals (Thu.): Resource identification.
||5-Nov-2012||Notes. 14 Edge detection||Vittorio Ferrari||Specific object recognition: correspondence expansion, how to do it very fast for large-scale object/image retrieval (1h); implementation issues (1h);
Practicals (Wed.): Visual servoing.
||8-Nov-2012||Notes. 15 Image segmentation||Vittorio Ferrari||Edge detection and segmentation: simple thresholding, convolutions, canny, graph-cut, grab-cut
||Practicals (Thu.): Visual servoing.|
||12-Nov-2012||Notes. 16 Motion Synthesis||Subramanian Ramamoorthy||Motion synthesis in dynamic environments||Practicals (Wed.): Homing.|
||15-Nov-2012||Guest Lecture Info Sheet
Notes. 17 Compliant Motion Control
|Wyatt Newman||Guest Lecture by Wyatt Newman (Venue: IF 4.31)
Impedance and Force Control
Practicals (Thu.): Homing.
11:00-12:00 and 13:00-14:00
||Guest Lecture: Wyatt Newman (Non-Examinable) (Venue: IF 4.31)
Compliant Motion Control: Applications and Implementations
||19-Nov-2012||Notes. 18 Shape matching||Vittorio Ferrari||Shape matching: global descriptors, shape signatures, shape contexts, etc.
||22-Nov-2012||Notes. 19 Object categorization||Vittorio Ferrari||Object categorization taster: problem definition and challenges, two simple models (generalized hough transforms, sliding-windows), learning parameters from training data, part-based models, the need for weak supervision. THIS LECTURE WILL NOT BE PART OF THE EXAM.
||26-Nov-2012||Final Demo: Practice|
||29-Nov-2012||Final Practical Demo / Competition||Competition|
- Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G., Robotics: Modelling, Planning and Control
- 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.