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Robotics: Science and Systems (R:SS) Course Webpage 2019/2020

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. Particularly, in order to bridge the lectures on algorithms and lab sessions, the course also provides tutorials dedicated to the practice of programming and the implementation of algorithms - 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, G.16 Seminar Room - Doorway 4 (Medical School); Thursdays, Elliot Room (Minto House)

First Lecture: 16 Sep (Mon) 9:00-10:50 @G.16 Seminar Room - Doorway 4 (Medical School)

Practical times

Mondays: 15:00 - 17:00 [Appleton Tower, 3.01/3.02 Robotics lab] - first practical is on 23-Sep-2019

Thursdays: 15:00 - 17:00 [Appleton Tower 3.01/3.02 Robotics lab] - first practical is on 26-Sep-2019

Practical Signup Sheet: Link

Alternative Link

Tutorial times

Mondays: 11:00 - 12:00 [Appleton Tower, 3.09 workroom; Week 2 - Week 9] - first tutorial is on 23-Sep-2019

Thursdays: 11:00 - 12:00 [Appleton Tower, 3.09 workroom; Week 2 - Week 9] - first tutorial is on 26-Sep-2019

Tutorial Signup Sheet: Link

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, optimisation and localization.


Written Examination 50
Assessed Practicals 40
Assessed Assignments 10 (Coursework submission: submit via DICE by "submit rss cw1 <filename.pdf>")

Late Coursework & Extension Requests
Academic Misconduct

Course Lecturers

Dr. Zhibin (Alex) Li - zhibin[dot]li[at]



Chris McGreavy - c.mcgreavy[at]

Eleftherios Triantafyllidis - Eleftherios.Triantafyllidis[at]

Technical Support

Garry Ellard - gde[at]

Maryam Dar - mdar[at]


Jack Wilkinson - jack.wilkinson[at]

Theodoros Stouraitis - theodoros.stouraitis[at] 


Lecture plan (provisional)

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



Lecture notes


Lecture topic




Introduction & Overview of Robotics,
Intro to Practicals

Zhibin Li

Introduction and overview of different sub-fields in robotics, elements of robotics and their related techniques, as well as the state of the art robotics (1h 30min). Primer for the Practicals (20min)




Introduction & Overview of Robotics (ctd),
Coordinate Transformations
 Zhibin Li Notations, Transformations, Representation of Rotations




System Identification & Filtering

 Zhibin Li

How to identify parameters of a system and estimate the state, basic filtering techniques will be covered.

 Kit handout



Localisation: fundamentals & grid localisation  Zhibin Li Localisation and histogram filter for localisation.

 Kit handout



Localisation: particle filters

 Zhibin Li

Particle filters for localisation.




Localization and Mapping

Zhibin Li Occupancy grid map and SLAM.



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



Path & Motion Planning II Zhibin Li

Probabilistic Roadmap (PRM), Dijkstra's algorithm and A* search algorithm.




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



Kinematics Zhibin Li Kinematic (Forward, Inverse), Jacobian, Operational Space, Null Space, Optimality Principles

Homework 1 assigned



Kinematics (cont.)
Zhibin Li Kinematic and multi-objective motion planning. Dynamics: Point mass, PID, Newton Euler, Joint Space, Optimal Operational Space Control.
Major Milestone 1



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

Major Milestone 1



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




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



4-Nov-2019 Optimisation II
Model Predictive Control
Zhibin Li

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



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 due on Friday 8 Nov 4pm

11-Nov-2019 Robotics case study



Revision lecture


 10 18-Nov-2019       Major Milestone 2
 10  21-Nov-2019       Major Milestone 2

Homework 1 feedback to be handed out


 11  29-Nov-2019      
 Homework 2 - Practical report
(due 4pm, 29 Nov)

Kit collection
Final exam
  Location: Godfrey Thomson Hall - Thomsons Land
Date: Thursday, 19th December 2019
Time: 9:30 a.m. to 11:30 a.m.
Duration: 2:00




 Recommended Textbooks

  • 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): 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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