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.
Course descriptor
When and Where?
When: 9:00 - 10:50 (with 10 min. break) on Mondays and Thursdays.
Where: Mondays [50 George Square: G.06] and Thursdays at [David Hume Tower: LG.11]
First Lecture: 19 Sep (Mon) 9:00-10:50
Practical times
Monday 11.00 - 13.00 (Forrest Hill G.A11) - first practical on xx Sep (Mon)
Thursdays 11.00 - 13.00 (Forrest Hill G.A11) - first practical on xx Sep (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.
Assessment
Written Examination 50
Assessed Practicals 40
Assessed Assignments 10
Late Coursework & Extension Requests
Academic Misconduct
Course Lecturers
Professor Sethu Vijayakumar - sethu.vijayakumar[at]ed.ac.uk
Professor Bob Fisher - rbf[at]inf.ed.ac.uk
Dr. Michael Herrmann - michael.herrmann[at]ed.ac.uk
Dr. Zhibin (Alex) Li - zhibin[dot]li[at].ed.ac.uk
Demonstrators
Dr Vladimir Ivan - v.ivan[at]ed.ac.uk
Wolfgang Merkt - wolfgang.merkt[at]ed.ac.uk
Technical Support
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.
| Week | Date | Lecture notes | Lecturer | Lecture topic | Milestones |
|---|
| 1 | 19-Sep-2016 | Introduction & Transformations | Sethu Vijayakumar, Vladimir Ivan | Introduction; Notations, Transformations, Rotations (1h15mim), Primer for the Practicals (30min) | |
|---|
| 1 | 22-Sep-2016 | Vision Introduction 22/9 Notes | Bob Fisher | What is vision, cameras and image formation | | | 2 | 26-Sep-2016 | Kinematics | Sethu Vijayakumar | Kinematic (Forward, Inverse), Jacobian, Operational Space, Null Space, Optimality Principles (2h) |
Kit handout
| | 2 | 29-Sep-2016 | Vision Image Processing 29/9 Notes | Bob Fisher | Locating and describing image structures |
Kit handout
| | 3 | 3-Oct-2016 | Introduction to System Identification & State Estimation | Zhibin Li | How to identify parameters of a system and estimate the state, some basic filtering techniques will be covered as well. | | | 3 | 6-Oct-2016 | Visual Recognition 6/10 Notes | Bob Fisher | Simple probabilistic object recognition |
| | 4 | 10-Oct-2016 |
Dynamics
| 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) |
| | 4 | 13-Oct-2016 | No Class
| | RAS CDT Annual Conference |
Homework 1 assigned
| | 5 | 17-Oct-2016 | Dynamics (contd), Control, SOC additional notes | Sethu Vijayakumar | Dynamics (cont'd) (1h); Control: Intro to Optimal Control, HJB equations, LQR (1h) | Major Milestone 1 | | 5 | 20-Oct-2016 | Visual Matching 20/10 Notes | Bob Fisher | Edge detection, point features and template matching |
Major Milestone 1
| | 6 | 24-Oct-2016 | Low level robot vision (M.H.) | Michael Herrmann | Line detection, spatial clustering (M.H.) | | | 6 | 27-Oct-2016 | State Estimation (contd): Kalman Filter (Z.L.)
Robot vision I: Optical flow (M.H.) | Zhibin Li
Michael Herrmann | Kalman filter for state estimation (Z.L.)
Optical flow (M.H.) |
| | 7 | 31-Oct-2016 | Robot vision II: Shape from motion (M.H.)
Localisation: fundamentals & grid localisation (Z.L.) | Michael Herrmann
Zhibin Li | Mutli-view geometry, shape from texture (M.H.)
Basics about localisation and histogram filter for localisation (Z.L.) |
| | 7 | 3-Nov-2016 | Localisation: particle filters (Z.L.)
Robot vision III: Stereo (M.H.) | Zhibin Li
Michael Herrmann | Particle filters for localisation (Z.L.)
3D reconstruction (M.H.) |
Homework 1
(due Friday 4th Nov by 4pm)
| | 8 | 7-Nov-2016 | Robot vision IV: Visual Servoing (M.H.)
Localization and Mapping (Z.L.) | Michael Herrmann
Zhibin Li | Visual servoing (M.H.)
Occupancy grid map and SLAM (Z.L.) | | | 8 | 10-Nov-2016 | Path & Motion Planning I (Z.L.)
Robot Vision V: Vision for robots (M.H.) | Zhibin Li
Michael Herrmann | Motion planning concepts, Potential Fields, principle and code demo of Rapidly exploring Random Tree (RRT), and extensions of RRT algorithms. Vision-based control & manipulation (M.H.) |
| | 9 | 14-Nov-2016 | Object recognition (M.H.)
Path & Motion Planning II (Z.L.) | Michael Herrmann
Zhibin Li | Objectness, object recognition for robots (M.H.)
Probabilistic Roadmap (PRM), Dijkstra's algorithm and A* Search with code demos (Z.L.) |
Major Milestone 2
| | 9 | 17-Nov-2016 | Sensor Fusion
Deep control, HRI (M.H.) |
Michael Herrmann
| Sensor fusion, Deep neural networks forcontrol,
Human-robot interaction (M.H.) |
Major Milestone 2
| | 10 | 21-Nov-2016 | Exam Q&A | SV, BF, MH, ZL | |
Homework 1 feedback to be handed out
Homework 2 - Practical report
(due 4pm) | | 10 | 24-Nov-2016 | | | |
Kit collection
| | 11 | 28-Nov-2016
Monday | Exam Briefing and Q&A | MH, ZL | |
| | | 16-Dec-2016
Friday | Final Exam | | Exam is at 14:30-16:30 in G.21 Paterson's Land | |
|
Recommended Texts
- 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.
