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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Robotics: Science and Systems (INFR11092)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThis course will be a Masters degree level introduction to several core areas in robotics: kinematics, dynamics and control; 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 on an integrated mobile 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 the key elements of the major algorithmic techniques.
Course description The main coverage of topics is as follows:
- Kinematics - forward and inverse
- Dynamics - equation of motions and the state space representation
- Control - classical and modern control theories & techniques
- Sensing - single processing, filtering
- Motion planning - the basics and sampling based methods
- State estimation, localization and mapping
- SLAM; Multi-modal sensor fusion
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Students MUST NOT also be taking Introduction to Vision and Robotics (INFR09019) OR Intelligent Autonomous Robotics (Level 10) (INFR10005)
Other requirements This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser (lecturer).

Knowledge of multivariate calculus, linear algebra and matrix manipulations, basic notions of statistics and probability theory. General programming competence is required and the course will use Python and other in a Linux environment, and use GIT for version control.
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2020/21, Available to all students (SV1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 30, Supervised Practical/Workshop/Studio Hours 8, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 156 )
Assessment (Further Info) Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (Assessment) You should expect to spend approximately 42 hours on the coursework for this course.

If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year.
Feedback Not entered
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S1 (December)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Model the motion of robotic systems in terms of kinematics and dynamics
  2. Analyse and evaluate a few major techniques for feedback control, motion planning as applied to robotics
  3. Translate a subset of standard algorithms for motion planning, localization and feedback controllers into practical implementations
  4. Implement and evaluate a working, full robotic system involving elements of control, planning, localization
Reading List
1. Franklin, Gene F., et al. Feedback control of dynamic systems. Vol. 3. Reading, MA: Addison-Wesley, 1994.
2. Peter Corke, Robotics, Vision and Control, Springer-Verlag.
3. Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G., Robotics: Modelling, Planning and Control, Springer Verlag.
4. H. Choset, K.M. Lynch, S. Hutchinson, G. Kantor, Principles of Robot Motion: Theory, Algorithms, and Implementations.
5. S. Thrun, W. Burgard and D. Fox, Probabilistic Robotics.
6. J. J. Craig, Introduction to Robotics: Mechanics and Control (3rd Edition): Use for first 3 chapters only.
7. Yoshihiko Nakamura, Advanced Robotics: Redundancy and Optimization.
8. J.M. Maciejowski, Predictive control: with constraints.
9. Ian Goodfellow, et al., Deep Learning.
Additional Information
Course URL
Graduate Attributes and Skills Not entered
Course organiserDr Zhibin Li
Course secretaryMs Lindsay Seal
Tel: (0131 6)50 2701
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