CS 685
Autonomous Robotics
Time/Location: Tuesday 7:10-10pm,
Robinson B203
Instructor: Jana Kosecka
Office hours: 2-3pm Wednesday
Contact: Office 4444 Engineering Build., e-mail:
kosecka@gmu.edu, 3-1876
Course web page: http://www.cs.gmu.edu/~kosecka/cs685/
The course
covers basic principles of design and practice of intelligent
robotics systems. We will cover algorithms for the analysis of the
data obtained by vision and range sensors, basic principles of
modelling kinematics and dynamics and design of basic control
strategies.
Notion of robot's configuration space and geometric and topological
representations of the environment will be introduced and followed
by overview of basic motion planning techniques.
Issues of uncertainty modelling, state estimation, probabilistic
inference will be introduced and examined in the context of
localization and map making problems. We will study and formulate
interesting robotics tasks and show how they can be accomplished by
individual robot or cooperative robot teams (such as flocking,
foraging as well as robotic soccer).
The topics and techniques covered are relevant
for students interested in robotics, computer vision, artificial intelligence
as well as modeling and programming of complex distributed embedded systems
which interact with dynamically changing environments.
The course will comprise of lectures by
the instructor, homeworks and presentations of the selected research
publications by students. The grade will be based on homeworks and
final presentation of the project. The projects will involve
implementation of a systems in a mobile robot simulator and/or the actual
mobile robot.
Schedule, Homeworks, Handouts
Prerequisites:
CS 580 Artificial Intelligence
optional prerequisites - Computer Vision,
Analysis of Algorithms
Students taking the class should be comfortable with linear algebra, calculus
and probability
Recommended Textbooks:
R. Siegwart and I. Nourbakhsh:
Autonomous Mobile Robots, Second Edition, MIT Press, 2011, http://www.mobilerobots.org
S. Thrun, W. Burghart, D. Fox: Probabilistic Robotics,
http://robots.stanford.edu/probabilistic-robotics/
Additional Resources:
S. LaValle: Planning Algorithms, Cambridge
Press, http://planning.cs.uiuc.edu/
Grading:
Homeworks and Projects 65%
Exam 35%
The homeworks should be handed in on time. Late submissions are accepted
(as opposed to no submissions) but will incur late submission penalty.
Other recommended books:
G. Dudek and M. Jenkin: Computational
Principles of Mobile Robotics. Cambridge University Press 2000
R. Murphy: Introduction to AI
Robotics, MIT Press 2000
T. Dean and M. Wellman: Planning and Control. Morgan Kaufmann Publishers,
1991.
S. Russell and P. Norvig: Artificial Intelligence, Prentice Hall,
1995
R. Arkin: Behavior-Based Robotics, MIT Press, 1998
J-C. Latombe: Robot Motion Planning,Kluwer
Academic Publishers 1991.
Academic Integrity:
The integrity of the University community is affected by the
individual choices made by each of us. GMU has an Honor Code
with clear guidelines regarding academic integrity. Three
fundamental and rather simple principles to follow at all times
are that: (1) all work submitted be your own; (2) when using the
work or ideas of others, including fellow students, give full
credit through accurate citations; and (3) if you are uncertain
about the ground rules on a particular assignment, ask for
clarification. No grade is important enough to justify academic
misconduct. Plagiarism means using the exact words, opinions, or
factual information from another person without giving the
person credit. Writers give credit through accepted
documentation styles, such as parenthetical citation, footnotes,
or endnotes. Paraphrased material must also be cited, using MLA
or APA format. A simple listing of books or articles is not
sufficient. Plagiarism is the equivalent of intellectual robbery
and cannot be tolerated in the academic setting. If you have any
doubts about what constitutes plagiarism, please see me.
CS department Honor Code can be found here.