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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 & State Estimation  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

5-Oct-2017

State Estimation (contd): Kalman Filter Zhibin Li
Kalman filter for state estimation.


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
Digital System I
Zhibin Li

Localisation and histogram filter for localisation; numerical simulation and digital control systems.

Major Milestone 1

5

19-Oct-2017

Localisation: particle filters
Digital System II
Zhibin Li Particle filters for localisation; design and tuning of digital PID controller.


Major Milestone 1

6

23-Oct-2017

Localization and Mapping
Design of Advanced Controllers I
Zhibin Li Occupancy grid map and SLAM; design of discretized controllers, LQR stabilizer and LQR tracking control.

 

6

26-Oct-2017

Path & Motion Planning I
Zhibin Li Motion planning concepts, Potential Fields, principle and code demo of Rapidly exploring Random Tree (RRT), and extensions of RRT algorithms.

7

30-Oct-2017

Path & Motion Planning II
Design of Advanced Controllers II
Zhibin Li Probabilistic Roadmap (PRM), Dijkstra's algorithm and A* Search with code demos; constrained control, introducing anti-windup etc.


7

2-Nov-2017

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

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


6-Nov-2017

Optimisation II Zhibin Li Constrained optimization:  constrained linear least squares, Quadratic Programming (QP), and nonlinear optimisation.

 

8

9-Nov-2017 Model Predictive Control
Zhibin Li

Concept and examples of designing model predictive controllers.



9

13-Nov-2017

Trajectory Planning and Motion Planning

Zhibin Li

Trajectory Planning for fixed-base industrial manipulators, motion planning for fiex- and floating base robots.

Major Milestone 2

9

16-Nov-2017

Machine Learning for Robot Control Zhibin Li

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


Major Milestone 2

10

20-Nov-2017

Exam Q&A

SV, ZL

 

 


Homework 1 feedback to be handed out

Homework 2 - Practical report
(due 4pm)

10
23-Nov-2017

 


Kit collection







  Dec-2017
Final Exam
  Venue will be updated duly
 

 

 

 

 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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