Undergraduate Course: Intelligent Autonomous Robotics (Level 10) (INFR10005)

Course Outline
School	School of Informatics	College	College of Science and Engineering
Course type	Standard	Availability	Available to all students
Credit level (Normal year taken)	SCQF Level 10 (Year 4 Undergraduate)	Credits	10
Home subject area	Informatics	Other subject area	None
Course website	http://www.inf.ed.ac.uk/teaching/courses/iar/	Taught in Gaelic?	No
Course description	This course explored the fundamental problems involved in producing real world intelligent behaviour in robots, covering the different information processing methods and control architectures that have been developed and are currently in use, including probabilistic methods and approaches inspired by biological systems. The course is structured around a practical task to develop navigation algorithms on a real robot platform.

Entry Requirements (not applicable to Visiting Students)
Pre-requisites	Students MUST have passed: Introduction to Vision and Robotics (INFR09019)	Co-requisites
Prohibited Combinations	Students MUST NOT also be taking Intelligent Autonomous Robotics (Level 11) (INFR11070) OR Robotics: Science and Systems (INFR11092)	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. A good grounding in mathematics and some knowledge of first-order differential equations will be useful.
Additional Costs	None

Information for Visiting Students
Pre-requisites	None
Displayed in Visiting Students Prospectus?	Yes

Course Delivery Information

Delivery period: 2014/15 Semester 1, Available to all students (SV1)			Learn enabled: No	Quota: None
Web Timetable	Web Timetable
Course Start Date	15/09/2014
Breakdown of Learning and Teaching activities (Further Info)	Total Hours: 100 ( Lecture Hours 20, Supervised Practical/Workshop/Studio Hours 10, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 66 )
Additional Notes
Breakdown of Assessment Methods (Further Info)	Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Exam Information
Exam Diet	Paper Name	Hours & Minutes
Main Exam Diet S2 (April/May)		2:00

Delivery period: 2014/15 Semester 1, Part-year visiting students only (VV1)			Learn enabled: No	Quota: None
Web Timetable	Web Timetable
Course Start Date	15/09/2014
Breakdown of Learning and Teaching activities (Further Info)	Total Hours: 100 ( Lecture Hours 20, Supervised Practical/Workshop/Studio Hours 10, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 66 )
Additional Notes
Breakdown of Assessment Methods (Further Info)	Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Exam Information
Exam Diet	Paper Name	Hours & Minutes
Main Exam Diet S1 (December)		2:00

Summary of Intended Learning Outcomes
1 - Demonstrate familiarity with current robot control architectures by ability to choose the most appropriate method for a given robot task, to specify the components and interactions involved, and to design and programme an algorithm that solves the task. 2 - Identify and describe limitations of each architecture, particularly when applied to real robots interacting with the real world, rather than simulations. 3 - In written answers, describe and assess attempts to use robots to model biological systems. 4 - Write reports (in the form of journal papers) that explain in detail the implementation and evaluation of a robot performing a navigation task.

Assessment Information
Written Examination 50 Assessed Assignments 50 Oral Presentations 0 Assessment The coursework, carried out in groups of 2 or 3, requires you to program a robot to perform a specified task, and to present the results in written reports. The first two reports are worth 10% each, and the final report 30%. 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.

Special Arrangements
None

Additional Information
Academic description	Not entered
Syllabus	* The problem of designing intelligent autonomous systems. * Reactive control of behaviour. * The subsumption architecture. * Sensor fusion. * Control. * Planning. * Evolutionary and collective robotics. * Robots as biological models. * Simple navigation: gradient following, potential fields, landmarks. * Navigation with maps: localisation and learning maps. Relevant QAA Computing Curriculum Sections: Artificial Intelligence, Intelligent Information Systems Technologies
Transferable skills	Not entered
Reading list	* Valentino Braitenberg: Vehicles. MIT Press 1984 * Ronald C Arkin: Behavior-based Robotics, MIT press, 1998 * Robin R. Murphy: Introduction to AI Robotics, MIT Press, 2000 Introduction to Autonomous Mobile Records, R.Siegwart and I.Nourbakhsh
Study Abroad	Not entered
Study Pattern	Lectures 20 Tutorials 0 Timetabled Laboratories 10 Non-timetabled assessed assignments 40 Private Study/Other 30 Total 100
Keywords	Not entered