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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:  David Hume Tower Room 4.01 (50 George Square)

First Lecture: 16 Sep (Mon) 9:00-10:50 @  David Hume Tower Room 4.01

Practical times

Wednesdays 13:00 - 15:00 (AT 3.01)

Thursdays 16:00 - 18:00 (AT 3.01)

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

Course Lecturers

Professor Sethu Vijayakumar - sethu.vijayakumar[at]ed.ac.uk (Primary contact)

Dr Subramanian Ramamoorthy - s.ramamoorthy[at]ed.ac.uk

Dr Bastiaan Boom - bboom[at]inf.ed.ac.uk

Demonstrators

Paul Ardin - s8528002[at]sms.ed.ac.uk (Wednesday practical)

Andreea Radulescu - a.radulescu[at]sms.ed.ac.uk (Thursday practical)

Technical Support

Robert McGregor - robertm[at]inf.ed.ac.uk

Garry Ellard - gde[at]inf.ed.ac.uk

 

Lecture plan

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

Week

Date

Lecture notes

Lecturer

Lecture topic

Milestones

1

16-Sep-2013

Introduction

Sethu Vijayakumar

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

 

1

19-Sep-2013

Path Planning

Subramanian Ramamoorthy

Introduction to path planning methods; motion planning in c-space

 

2

23-Sep-2013

Kinematics

Sethu Vijayakumar

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

 

2

26-Sep-2013

Motion Planning

Subramanian Ramamoorthy

Sampling based motion planning, compositional methods

 

3

30-Sep-2013

Kinematics (cont'd)
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)

Practicals (Wed.): Robot is able to move around.

3

3-Oct-2013

Dynamics (cont'd)
Control
SOC Additional Notes

Sethu Vijayakumar

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

Practicals (Thu.): Robot is able to move around.

4

7-Oct-2013

State Estimation

The Matrix Cookbook

Subramanian Ramamoorthy

State estimation

Practicals (Wed.): Obstacle avoidance.

4

10-Oct-2013

Localization and Mapping

Subramanian Ramamoorthy

Particle filter, Localization, Mapping, SLAM

 Practicals (Thu.): Obstacle avoidance.

5

14-Oct-2013

Introduction to Vision
Image Formation

Bastiaan Boom

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)

 

5

17-Oct-2013

Two View Geometry

Bastiaan Boom

Two-view geometry: fundamental matrix (properties and estimation), invariance classes, invariants for planar configurations of points and lines

Homework 1 assigned

6

21-Oct-2013

Interest Points

Bastiaan Boom

Implementation issues for homography and fundamental matrix estimation  (1h); Interest points and regions: general concept, plain Harris, scale-invariant Harris (1h)

 

6

24-Oct-2013

Feature Matching

Bastiaan Boom

Interest points and regions: affine-invariant IBR and MSER (1h); implementation issues (1h)


7

28-Oct-2013

Affine features
Specific object recognition

Bastiaan Boom

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)

Practicals (Wed.): Homing.

7

31-Oct-2013

Motion Synthesis

Subramanian Ramamoorthy

Motion synthesis in dynamic environments

Practicals (Thu.): Homing.

8

4-Nov-2013

Edge detection

Bastiaan Boom

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

7-Nov-2013

Image segmentation

Bastiaan Boom

Edge detection and segmentation: simple thresholding, convolutions, canny, graph-cut, grab-cut

Practicals (Thu.): Visual servoing.

Homework 1 due (7th Nov)

9

11-Nov-2013

Shape matching

Bastiaan Boom

Shape matching: global descriptors, shape signatures, shape contexts, etc.


Practicals (Wed.): Resource identification.

 

9

14-Nov-2013

Object categorization

Part-based models: Star models and Open Challenges will not be part of exam

Bastiaan Boom

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.

Practicals (Thu.): Resource identification.

10

18-Nov-2013

 

 

 

Homework 1 feedback to be handed out

Practicals (Wed): Resource Pickup

10

21-Nov-2013

 Exam Q & A

 SV, SR, BB

 

Practicals (Thu): Resource Pickup
11
25-Nov-2013


Final Demo: Practice [Please complete your HW2 Write Up by now!]


 11  28-Nov-2013  Competition   Final Practical Demo / Competition  

Competition

Homework 2 (practical report) due

           

 

 

 

 Recommended Texts

 
  • 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.
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