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

Course Outline
School	School of Informatics	College	College of Science and Engineering
Credit level (Normal year taken)	SCQF Level 10 (Year 4 Undergraduate)	Availability	Available to all students
SCQF Credits	10	ECTS Credits	5
Summary	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.
Course description	* 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

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 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.

Information for Visiting Students
Pre-requisites	None
High Demand Course?	Yes

Course Delivery Information

Academic year 2017/18, Available to all students (SV1)		Quota: None
Course Start	Semester 1
Timetable	Timetable
Learning and Teaching activities (Further Info)	Total Hours: 100 ( Lecture Hours 16, Supervised Practical/Workshop/Studio Hours 6, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 74 )
Assessment (Further Info)	Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (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%. You should expect to spend approximately 40 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 S2 (April/May)			2:00

Academic year 2017/18, Part-year visiting students only (VV1)		Quota: None
Course Start	Semester 1
Timetable	Timetable
Learning and Teaching activities (Further Info)	Total Hours: 100 ( Lecture Hours 16, Supervised Practical/Workshop/Studio Hours 6, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 74 )
Assessment (Further Info)	Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (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%. You should expect to spend approximately 40 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: 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. Identify and describe limitations of each architecture, particularly when applied to real robots interacting with the real world, rather than simulations. In written answers, describe and assess attempts to use robots to model biological systems. Write reports (in the form of journal papers) that explain in detail the implementation and evaluation of a robot performing a navigation task.

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 * Roland Siegwart, Illah R. Nourbakhsh and David Scaramuzza: Autonomous Mobile Robots. MIT Press 2011.