Undergraduate Course: Intelligent Autonomous Robotics (Level 10) (INFR10005)
|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
|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.
* The problem of designing intelligent autonomous systems.
* Reactive control of behaviour.
* The subsumption architecture.
* Sensor fusion.
* 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)
|| Students MUST have passed:
Introduction to Vision and Robotics (INFR09019)
|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
|High Demand Course?
Course Delivery Information
|Not being delivered|
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.
|* 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.
|Course organiser||Dr Barbara Webb
Tel: (0131 6)51 3453
|Course secretary||Mr Gregor Hall
Tel: (0131 6)50 5194