Structure and Synthesis of Robot Motion
Semester 2 2011/2012
Course organiser: Michael Rovatsos
To contact: email@example.com
Office: 2.12 IF
Phone: 651 3263
Lecturer: Subramanian Ramamoorthy
Tutor: Benjamin Rosman
Lecture times: Mondays and Thursdays 10 - 10:50 am, AT 2.12
Lecture topics and handouts
The course mark will be computed using the following weighting:
- Three Homeworks - 10%, 10%, 20% respectively
- Final Exam - 60%
In addition, there will be assignments for tutorial sessions (not marked) aimed at unpacking the material and giving students practice with the taught concepts.
This course will introduce students to state of the art methods and some current research themes centered on the question of how
intelligent robots should make decisions about their motion and behaviour.
To a computer scientist, the problem of motion synthesis is of interest because it calls for systematic treatment of various kinds of unknowns in decision making.
The course will be structured in terms of six major themes, typically two lectures each - each theme addressing a key type of unknown in a model of decision making within robots.
The six themes for this semester are:
- Making sense of sensorimotor systems
- Motion synthesis under uncertainty in state/actions
- Motion synthesis with strategic considerations
- Motion synthesis in groups and formations
- Decentralized decision making and motion synthesis
- Information incompleteness and asymmetry
These topical lectures will be preceded by a couple of lectures reviewing basic concepts, to set the scene. Lectures will be complemented by
four tutorial sessions and three homework assignments, which should be treated as an essential and integral part of the course.
Background and Pre-requisites
This is a 'second course' in robotics.
It is assumed that all students have completed an introductory robotics course, or possess equivalent working knowledge. It is expected that the student has sufficient
mathematical maturity in order to be able to read the robotics literature (i.e., facility with concepts from multivariate calculus, linear algebra, probability and
On the practical side, assignments and tutorials will require programming, typically in some combination of Matlab and Webots. Students are expected to enter this course with sufficient programming skill, or the capacity to learn what is required on the fly. However, this is not a 'programming course' - most of our classroom discussion will focus on algorithmic and conceptual ideas.
- H. Choset et al., Principles of Robot Motion, MIT Press, 2005.
- S. LaValle, Planning Algorithms, Cambridge University Press, 2006. (Online version)
- F. Bullo, Distributed Control of Robotic Networks, Princeton University Press, 2009. (Online version)
Other specific references will be assigned along with corresponding lectures.
Last update: 10 January 2012.