Structure and Synthesis of Robot Motion (P02896)

? Credit Points : 10 ? SCQF Level : 11 ? Acronym : INF-P-SSRM

The goal of this course is to provide the student with the analytical and mathematical foundations required to design algorithms for synthesis or predictive modeling of motion in a variety of scientific and engineering domains - including autonomous robotics, sensor networks or swarms, computer animation and computational biology. One primary goal is to bridge the gap between introductory courses and the current state of research.

Entry Requirements

? Pre-requisites : Introduction to Vision and Robotics or equivalent knowledge; familiarity with basic mathematical concepts (at the advanced undergraduate level) from linear algebra, differential equations and probability. This course can be taken in conjunction with Intelligent Autonomous Robotics, if all of the other prerequisites have been met. Also, UG4 students may take this course (provided they have successfully completed Introduction to Vision and Robotics) but they will be performing and assessed at the MSc. level.

Subject Areas

Home subject area

Artificial Intelligence, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : Postgraduate

? Delivery Period : Semester 2 (Blocks 3-4)

? Contact Teaching Time : 2 hour(s) per week for 10 weeks

First Class Information

Date	Start	End	Room	Area	Additional Information
12/01/2009	10:00	10:50	Room 1.B01, Forrest Hill	Central

All of the following classes

Type	Day	Start	End	Area
Lecture	Monday	10:00	10:50	Central
Lecture	Thursday	10:00	10:50	Central

Summary of Intended Learning Outcomes

- Way of thinking. The course presents a sophisticated and mathematically mature view of motion strategies. In particular, a number of alternate representations and corresponding mathematical techniques will be introduced: Students will be able to describe a number of paradigmatic techniques for modeling motion systems, and make sound judgements about which of them is appropriate for a specific problem at hand.

- Conceptual Foundations. Students will possess a sufficiently deep understanding of the conceptual foundations of this area in order to be able to gainfully utilize and contribute to the research literature in this area.

- Practical Ability. For each of the major conceptual threads, the course will also include coverage of algorithm design issues to enable transfer of these ideas to practice. Students will be able to implement motion algorithms, and in conjunction with pre-existing open-source tools, demonstrate a solution to a concrete problem in a realistic application setting.

- Breadth of Thinking. Given a complex problem domain (e.g., rehabilitation robotics or computational structural biology), students will be able to (i) identify sub-problems for which motion algorithms are relevant, (ii) identify interfaces with closely related areas, e.g., machine learning and (iii) implement and evaluate solutions to these sub-problems.