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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 and dynamics of robots; robot control, classical and modern control theories; motion planning; state estimation and signal processing; localization and mapping. 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 & control 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 at [Appleton Tower, 6.06, AT].

First Lecture: 18 Sep (Mon) 9:00-10:50

Practical times

Mondays: 15:00 - 17:00 [Appleton Tower, 3.01/3.02 Robotics lab] - first practical is on 25-Sep-2017

Thursdays: 11:00 - 13:00 [Appleton Tower 3.01/3.02 Robotics lab] - first practical is on 28-Sep-2017

Tutorial times

Mondays: 11:00 - 12:00 [Appleton Tower, 3.09; Week 3; Week 6 - Week 9] - first tutorial is on 2-Oct-2017

Thursdays: 15:00 - 16:00 [Appleton Tower, 5.04 - North Lab; Week 3; Week 6 - Week 9] - first tutorial is on 5-Oct-2017

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 which are applied to robotics.
  • Translate a subset of standard algorithms for control, motion planning and localization into practical implementations.
  • Implement and evaluate a working, full robotic system involving elements of control, planning, localization.

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. Zhibin (Alex) Li - zhibin[dot]li[at].ed.ac.uk

 

Demonstrators

Dr Vladimir Ivan - v.ivan[at]ed.ac.uk

Henrique Ferrolho - henrique.ferrolho[at]ed.ac.uk


Technical Support

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

Tony Shade - ashade[at]inf.ed.ac.uk

 

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

18-Sep-2017

Introduction & Transformations,
Intro to Practicals

Sethu Vijayakumar, Vladimir Ivan

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

 

1

21-Sep-2017

Overview of Robotics
 Zhibin Li Overview of different sub-fields in robotics, elements of robotics and their related techniques, as well as the state of the art robotic showcases.

 

2

25-Sep-2017

Kinematics

 Sethu Vijayakumar

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


 Kit handout

2

28-Sep-2017

System Identification & Filtering  Zhibin Li How to identify parameters of a system and estimate the state, basic filtering techniques will be covered.


 Kit handout

3

2-Oct-2017

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
2-Oct-2017 Tutorial: Introduction to Python
Zhibin Li


3

5-Oct-2017

State Estimation & Kalman Filter Zhibin Li
State estimation and the use of Kalman filter.


3
5-Oct-2017 Tutorial: Introduction to Python Zhibin Li

4

9-Oct-2017

Dynamics (contd),

Control,

SOC additional notes

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



4

12-Oct-2017

No Class

RAS CDT Annual Conference


Homework 1 assigned

5

16-Oct-2017

Localisation: fundamentals & grid localisation
Zhibin Li

Localisation and histogram filter for localisation.

 

5

19-Oct-2017

Localisation: particle filters
Zhibin Li Particle filters for localisation.


6

23-Oct-2017

Localization and Mapping
Zhibin Li Occupancy grid map and SLAM.

Major Milestone 1 

6
23-Oct-2017 Tutorial: Grid localisation Zhibin Li

6

26-Oct-2017

Path & Motion Planning I
Zhibin Li Motion planning concepts, potential fields, Rapidly exploring Random Tree (RRT), and extensions of RRT algorithms. Major Milestone 1

6
26-Oct-2017 Tutorial: Grid localisation Zhibin Li

7

30-Oct-2017

Path & Motion Planning II
Zhibin Li Probabilistic Roadmap (PRM), Dijkstra's algorithm and A* search algorithm.


7
30-Oct-2017 Tutorial: Particle filters Zhibin Li

7

2-Nov-2017

Digital System
Zhibin Li
Numerical simulation, digital control systems, digitization of controllers with an example of digital PID controller.

Homework 1
(
due Friday 3rd Nov by 4pm)

7
2-Nov-2017 Tutorial: Particle filters Zhibin Li

8

6-Nov-2017

Advanced Digital Controllers Zhibin Li LQR stabilizer and LQR tracking control; constrained control, introducing anti-windup etc.

 

8
6-Nov-2017 Tutorial: Numerical simulation
Zhibin Li

8

9-Nov-2017 Optimisation I
Zhibin Li

Concept of optimization; unconstrained optimization, least square optimization, Tikhonov regularisation; gradient-based optimization; and Lagrange Multiplier method.



8
9-Nov-2017 Tutorial: Numerical simulation Zhibin Li

9

13-Nov-2017

Optimisation II
Model Predictive Control

Zhibin Li

Constrained optimization: constrained linear least squares, Quadratic Programming (QP), and nonlinear optimisation, and model predictive control.

Major Milestone 2
9
13-Nov-2017 Tutorial: Control design
Zhibin Li

9
16-Nov-2017 No Class
Conference attendance
Major Milestone 2

10

20-Nov-2017

Machine Learning for Robot Control

Zhibin Li

Machine learning techniques, Deep Deterministic Policy Gradients (DDPG), and Recurrent Deterministic Policy Gradients (RDPG) in the control of robot locomotion.


Homework 1 feedback to be handed out

Homework 2 - Practical report
(due 4pm)

10 20-Nov-2017 Tutorial: Control design Zhibin Li

10
23-Nov-2017 Exam Q&A SV, ZL
Kit collection






  15-Dec-2017
Final Exam (09:30 to 11:30)
  Location: Godfrey Thomson Hall - Thomsons Land
 

 

 

 

 Recommended Texts

 
  • Franklin, Gene F., et al. Feedback control of dynamic systems. Vol. 3. Reading, MA: Addison-Wesley, 1994.
  • 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.
  • J. J. Craig, Introduction to Robotics: Mechanics and Control (3rd Edition), [pdf]: Use for first 3 chapters only.
  • Yoshihiko Nakamura, Advanced Robotics: Redundancy and Optimization.
  • J.M. Maciejowski, Predictive control : with constraints.
  • Ian Goodfellow, et al., Deep Learning.
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