CS 685
Autonomous Robotics

Time/Location: Wednesday 4:30-7:10pm,  Innovation Hall 136
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.


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/


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.