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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.

Course descriptor

When and Where?

When: 9:00 - 10:50 (with 10 min. break) on Mondays and Thursdays.

Where: Mondays and Thursdays

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 - mherrman[at]inf.ed.ac.uk

Dr. Alex (Zhibin) Li - zli33[at]nf.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

 

 

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)


Homework 1 assigned

4

13-Oct-2016

No Class

RAS CDT Annual Conference



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




 

6

27-Oct-2016






7

31-Oct-2016





7

3-Nov-2016





Homework 1
(
due Friday 4th Nov by 4pm)

8

7-Nov-2016




 

8

10-Nov-2016



 



9

14-Nov-2016


 

 

 


Major Milestone 2

9

17-Nov-2016


 

 


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

 
 

 



           

 

 

 

 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.
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