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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2022/2023

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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Advanced Robotics (INFR11213)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
Summary***This course replaces Robotics: Science and Systems (INFR11092)***

Robotics is about turning high level goals into plans for action, i.e. robots sense the environment and produce physical motions and interactions with the environment to achieve a goal. In this course, stu-dents will learn the major algorithmic techniques and practical skills in robotics that can be applied and transferred to many real-world applications, such as manipulation of surgical robotics and robot as-sembly in automobile and manufacturing.

The course assumes no prior knowledge of robotics, so begins with a high-level overview of the major areas in robotics and then introduces core topics: kinematics, dynamics and control; state estimation and signal processing; digital control systems; optimisation and optimal control; robot motion planning and basics of robot learning.

Building on these fundamentals, the course then focuses on the advanced control and task planning of articulated robotic systems, e.g. robotic manipulators. Students develop a lab practical in both simulation and on a real robot, so as to consolidate theoretical knowledge and develop practical skills.
Course description This is a fast-paced course that starts with the fundamentals and then proceeds to go in-depth with core elements in robotics. The focused topics cover: kinematics, dynamics and control; state estimation and signal processing; digital control systems; optimisation, robot motion planning; and robot learning.

The aim of the course is to present essentials in robotics, articulated robots in particular, culminating in a robotic lab practical. The lab involves the development of an integrated robotic system which embodies the major algorithmic techniques used in real-world robotic applications. 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.

Lectures on these topics will be complemented by labs that exercise knowledge of a cross section of these techniques, based on realistic tasks driven by real-world applications, such as dual-arm robot manipulation. The practical lab consists of 2 parts: individual-based simulation (80%), and group-based real robot demonstration (20%). The lab demonstration will be carried out on an advanced humanoid robot and students will work in groups to deploy their work on the real robot.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements General knowledge from common engineering background (such as mechanical engineering, electronic engineering, computer signs and etc):
- Mathematics, eg linear algebra, calculus
- Basics of Physics, eg Newton's law
- Some level of proficiency or exposure to programming skills, or basic knowledge of programming languages
Information for Visiting Students
Pre-requisitesGeneral knowledge from common engineering background (such as mechanical engineering, electronic engineering, computer signs and etc):
- Mathematics, eg linear algebra, calculus
- Basics of Physics, eg Newton's law
- Some level of proficiency or exposure to programming skills, or basic knowledge of programming languages
High Demand Course? Yes
Course Delivery Information
Academic year 2022/23, Available to all students (SV1) Quota:  45
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 26, Seminar/Tutorial Hours 5, Supervised Practical/Workshop/Studio Hours 7, Formative Assessment Hours 1, Summative Assessment Hours 1, Revision Session Hours 2, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 154 )
Assessment (Further Info) Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (Assessment) Coursework 1, 15%: homework to complete general questions in robotics; may involve implementing some of the methods.

Coursework 2, 10%: an individual report for the practical labs involving scientific writing, analysis of results and data.

Coursework 3, 25%: a group presentation/demonstration of robotic tasks to demonstrate implementation of core robotics algorithms and solutions using both physics simulation and real robots.
Feedback Mid-term lecture revision
Final revision lecture
In-term feedback via Piazza
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S1 (December)Advanced Robotics (INFR11213)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. analyse the fundamental principles and the essential concepts in canonical robotics topics, evaluate the suitability and applicability of the algorithms given a robotics problem. Identify, propose and develop robotic solutions to solve practical robotic tasks
  2. program and implement theoretical algorithms using common programming languages, and develop proficiency in debugging the code
  3. use common robotics-related software, and use simulation tools to successfully set up robotic tasks and environments
  4. write up and deliver a technical and scientific report, and demonstrate analytical and critical thinking to explain the positive and negative results of the tasks, and evaluate the performance by using quantifiable metrics
  5. increase awareness of health and safety issues while working with real robotic systems, acquire knowledge of basic safety procedures of operating robotic and / or electronic systems, learn practical skills in using physical emergency devices and implementing software safety measures
Reading List
Modern Robotics: Mechanics, Planning, and Control, Frank C. Park and Kevin M. Lynch
Introduction to Robotics, Fourth Edition, J. J. Craig, Pearson, 2017
Franklin, Gene F., et al. Feedback control of dynamic systems. Vol. 3. Reading, MA: Addison-Wesley, 1994.
Additional Information
Graduate Attributes and Skills - Develop interpersonal skills through teamwork with cohort student for the labs
- Practical skills of problem-solving and knowledge integration through applying knowledge to real world problems
- Communication skills in terms of verbal and written skills, through presentations of practical and reporting of results
- Work Independently and time management skills to deliver multiple objectives through reporting and live demos
- Cultural awareness and diversity through teamwork with international students
KeywordsRobot Forward / Inverse Kinematics,Robot Dynamics,System Modelling,Motion Planning
Contacts
Course organiserDr Zhibin Li
Tel:
Email: Zhibin.Li@ed.ac.uk
Course secretaryMs Lindsay Seal
Tel: (0131 6)50 2701
Email: lindsay.seal@ed.ac.uk
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