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)
Prerequisites 
It is RECOMMENDED that students have passed
Introduction to Vision and Robotics (INFR09019)

Corequisites  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
Prerequisites  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   111  10:00  10:50      Central  Lecture   111     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 preexisting opensource 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 subproblems 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 subproblems. 
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  NewtonEuler and EulerLagrange 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 HamiltonJacobiBellman 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, HumanComputer 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
Nontimetabled 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 

