Postgraduate Course: Structure and Synthesis of Robot Motion (INFR11065)
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 11 (Postgraduate) |
Credits | 10 |
| Home subject area | Informatics |
Other subject area | None |
| Course website |
http://www.inf.ed.ac.uk/teaching/courses/ssrm |
Taught in Gaelic? | No |
| Course description | 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 (not applicable to Visiting Students)
| Pre-requisites |
It is RECOMMENDED that students have passed
Introduction to Vision and Robotics (INFR09019)
|
Co-requisites | It is RECOMMENDED that students also take
Intelligent Autonomous Robotics (Level 11) (INFR11070) OR
Intelligent Autonomous Robotics (Level 10) (INFR10005)
|
| Prohibited Combinations | |
Other requirements | For Informatics PG and final year MInf students only, or by special permission of the School.
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. |
| Additional Costs | None |
Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
|
| Delivery period: 2011/12 Semester 2, Available to all students (SV1)
|
WebCT enabled: No |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| Central | Lecture | | 1-11 | 10:00 - 10:50 | | | | | | Central | Lecture | | 1-11 | | | | 10:00 - 10:50 | |
| First Class |
Week 1, Monday, 10:00 - 10:50, Zone: Central. AT 2.12 |
| Exam Information |
| Exam Diet |
Paper Name |
Hours:Minutes |
|
|
| Main Exam Diet S2 (April/May) | | 2:00 | | |
Summary of Intended Learning Outcomes
1 - 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. Learning outcomes: 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.
2 - Conceptual foundations - Learning outcomes: 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.
3 - 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. Learning outcomes: 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.
4 - Breadth of thinking - Learning outcome: 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. |
Assessment Information
Written Examination 60
Assessed Assignments 40
Oral Presentations 0
Assessment
There will be two homework assignments (each requiring approximately 6 hours of work), intended to flesh out concepts covered in the lectures. Then, there will be one project (requiring approximately 20 hours of work) wherein the student will be asked to solve a concrete problem. This problem will be a simplified version of interesting questions arising in the current research literature. So, in their report, the student will be asked to comment on their work in the context of the current state of the art.
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 |
1. Introduction: Variety of robotic motions; new applications - animation, sensor networks, computational biology, etc.
2. Mathematical Preliminaries
a. Basic notions from manifold theory and topology
b. Differential equations and vector fields
c. Some notions from probability and stochastic processes
3. Structure: Describing and Modeling Motion
a. Kinematics - forward and inverse (definition of geometric transformations, computations in the serial, parallel and cyclic configurations).
b. Dynamics - Newton-Euler and Euler-Lagrange formalisms, incorporating friction, impacts, and other effects.
c. Configuration and phase spaces
d. Continuum and collective motion
4. Synthesis: Planning and Control
a. Overview of task encoding and motion strategies in robotics and biology - what are the major issues? This includes an introduction to classical notions of stability, controllability, observability, robustness, etc. and their relevance to more complex system and task descriptions.
b. Control Theory
i. Optimal control and Hamilton-Jacobi-Bellman equation
ii. Model predictive control and adaptive control
iii. Geometric and nonlinear control - nonholonomy, underactuation and constraints
c. Motion Planning
i. Classical and combinatorial algorithms $ú outline of the major ideas
ii. Sampling based algorithms: PRM, RRT, etc.
iv. Motion primitives and symbolic abstractions: how to define, learn (from data and/or active exploration) and use in a hierarchy
5. Case Studies - Grand Challenges
a. Humanoid robotics - dynamic manipulation, locomotion, etc.
b. Distributed and reconfigurable robotics - motion planning and control of shape
c. Computational biology - motion of biomolecules
Relevant QAA Computing Curriculum Sections: Artificial Intelligence, Human-Computer Interaction (HCI), Intelligent Information Systems Technologies, Simulation and Modelling, Theoretical Computing |
| Transferable skills |
Not entered |
| Reading list |
* H. Choset et al., Principles of Robot Motion, MIT Press, 2005.
* S.M. LaValle, Planning Algorithms, Cambridge University Press, 2006.
* S.P. Novikov et al., Modern Geometric Structures and Fields, American Mathematical Society, 2006.
* R. M. Murray et al., A Mathematical Introduction to Robotic Manipulation, CRC Press, 1994.
* M.T. Mason, Mechanics of Robotic Manipulation, MIT Press, 2001. |
| Study Abroad |
Not entered |
| Study Pattern |
Lectures 20
Tutorials 0
Timetabled Laboratories 0
Non-timetabled assessed assignments 32
Private Study/Other 48
Total 100 |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Michael Rovatsos
Tel: (0131 6)51 3263
Email: mrovatso@inf.ed.ac.uk |
Course secretary | Miss Kate Weston
Tel: (0131 6)50 2701
Email: Kate.Weston@ed.ac.uk |
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© Copyright 2011 The University of Edinburgh - 16 January 2012 6:17 am
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