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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  G.08  1 George Square,  Thursdays LG34 Patersons Land map1, map2

First Lecture: 21 Sep (Mon) 9:00-10:50 @ G.08  1 George Square

Practical times

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.

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

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.

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

21-Sep-2015

Introduction & Transformations

Sethu Vijayakumar

Introduction; Notations, Transformations, Rotations (1h15mim), Primer for the Practicals (30min)

 

1

24-Sep-2015

Introduction slides 2x2

Image formation slides 2x2

Chris Williams

Introduction. Image formation -- pinhole camera, cameras with lenses, light and surfaces, colour.

 

2

28-Sep-2015

Kinematics

 Sethu Vijayakumar

Kinematic (Forward, Inverse), Jacobian, Operational Space, Null Space, Optimality Principles (2h)


 Kit handout

2

1-Oct-2015

RSS Vision 1+2View Geometry Lecture 2x2

Chris Williams

Single camera geometry; intrinsic and extrinsic parameters.

Two-view geometry: affine transformation, homography, epipolar geometry


 Kit handout

3

5-Oct-2015

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)

 

3

8-Oct-2015

RSS Vision Interest Points and Regions 2x2

RSS Vision Describing and Matching Local Features 2x2

Chris Williams

 Harris corners; invariance and equivariance; regions and choosing a scale; affine equivariance. Feature descriptors (SIFT etc).


4

12-Oct-2015


RSS Vision Describing and Matching Local Features 2x2 (ctd)
RSS Vision Recognition of Specific Objects 2x2
RSS Vision Edge Detection 2x2
 

Chris Williams

 Matching descriptors. RANSAC. Detecting objects.

Comparison of template matching and local features methods

 Linear filtering; smoothing and edge detection; Canny edge detector and beyond

 

4

15-Oct-2015

RSS System Identification and State Estimation
RSS Particle Filters

Subramanian Ramamoorthy

System identification and least squares method; 

State estimation - the Kalman filter, introduction to the particle filter

 

5

19-Oct-2015

Dynamics (contd),

Control,

SOC additional notes

Sethu Vijayakumar


Dynamics (cont'd)  (1h);  Control:  Intro to Optimal Control, HJB equations, LQR (1h)

 

5

22-Oct-2015

RSS Vision Segmentation 2x2

 

RSS Vision Shape Matching 2x2

Chris Williams

Image segmentation: k-means, hierarchical k-means, mean shift.
Interactive sgementation with graph cuts.
Shape matching: shape signatures, shape contexts, thin plate splines, shock graphs. Shape matching in clutter: template matching, deformable templates, contour segment networks.


Homework 1 assigned

6

26-Oct-2015

RSS Localisation and Mapping

RSS Motion and Sensor Models

Subramanian Ramamoorthy

Localisation and Mapping; 

Implementation issues - particle filters, etc.; Motion and sensor models for mobile robots


Major Milestone 1

6

29-Oct-2015

RSS Vision Object Class Recognition 2x2 

Chris Williams

Shape matching continued.

Object class recognition: Classification, visual bag of words; Detection, sliding window detectors. Neural networks.

Major Milestone 1

7

2-Nov-2015

RSS Motion planning

Subramanian Ramamoorthy

Path planning - combinatorial and sampling-based methods


7

5-Nov-2015


Object class recognition continued.

Chris Williams

Detection, sliding window detectors. Neural networks.


8

9-Nov-2015

RSS Robot System Architecture + Case Study

Subramanian Ramamoorthy

Sampling-based motion planning, continued;
Overview of mobile robot system architectures; 
Case study: planning dynamically dexterous behaviour

 

8

12-Nov-2015

No lecture

 


Homework 1 due (13th Nov by 4pm)

9

16-Nov-2015


 

 

 

Major Milestone 2

9

19-Nov-2015


 

 


Major Milestone 2

10

23-Nov-2015

Exam Q&A

SV, SR, CW

 


Homework 1 feedback to be handed out

10
26-Nov-2015


 


 11  3-Dec-2015
Thursday
 Competition   Final Practical Demo / Competition  

Final Competition

Homework 2 (Practical report) due 4pm

           

 

 

 

 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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