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Instructor Location and Time Office Hours |
Amarda Shehu , Room #4422 ENG, amarda\AT\gmu.edu Art and Design Building #2026, TR, 1:30pm - 2:45 pm TR, 12:30 - 1:30 pm |
Course Summary:This course covers topics from artificial intelligence, algorithms, and robotics for the design and practice of intelligent robotics systems. The main emphasis will be on planning algorithms for single and multi-robot systems in the presence of kinematic and dynamic constraints. Integration of sensory data will also be discussed. Selected topics will include manipulation planning, assembly planning, and planning under uncertainty.
Target audience: Junior- and senior-level students interested in artificial intelligence in general and robotics in particular. The course will allow students to implement sophisticated robotic algorithms. For samples of past student projects, visit this page .
Format:Material will be disseminated through class lectures. Homework programming projects and a midterm exam will test comprehension of the basic material. Homeworks will allow students to plugin their implementations to provided platforms so emphasis is on algorithmic design rather than graphical rendering. Extra credit in homeworks will allow students that are interested in advanced topics and research to demonstrate their abilities. Extra credit will not account for more than 10% of the total grade. No programming is involved in the exam No late homeworks or project deliverables will be accepted. A final research project will replace the final exam, with presentation of selected paper for implementation and project progress in class. Check out samples of past student projects .
Prerequisites:CS 262, CS310, and Math 203. Students taking the class should be comfortable with linear algebra, calculus, and probability. Computer Vision and Analysis of Algorithms are desirable but not imperative.
Textbook(s):The course will combine topics from 1) "Principles of Robot Motion" by Howie Choset et al. and 2) Planning Algorithms (available online) by Steven M. Lavalle, Cambridge University Press, 1st Edition (2006). Students are encouraged to purchase "Principles of Robot Motion."
Outcomes:
| Date | Topic | Lectures | Assignments |
|---|---|---|---|
| Aug. 27, 29 | Introduction and Course Overview |
| Basic Motion-Planning Algorithms and Foundations |
| Sep. 03, 05 | Bug Algorithms, Configuration Spaces | Hw1 Out | |
| Sep. 10, 12 | Forward and Inverse Kinematics | ||
| Sep. 17, 19 | Potential Fields, Roadmaps/Cell Decompositions |
| Sampling-based and Probabilisic Motion Planning | ||
| Sep. 24, 26 | Roadmap Approaches | Hw1 Due, Hw2 Out | |
| Oct. 01, 03 | Tree Approaches | ||
| Oct. 08, 10 | Guest Lecture | ||
| Oct. 17 | Exam | Hw2 Due, Hw3 Out |
| Advanced Motion Planning | ||
| Oct. 22, 24 | Multiple Robots, Manipulation Planning | ||
| Oct. 29, 31 | Computational Biology, Dynamics/Physics Game Engines | ||
| Nov. 05, 07 | High-level Tasks/AI/Discrete Planning | Hw3 Due, Project Out | |
| Nov. 12 | Dynamic Environment/Uncertainty |
| Localization and Mapping | ||
| Nov. 21, 26 | Kalman Filtering, Bayesian Methods, Mapping and SLAM | ||
| Dec. 03, 05 | Project Presentations | Project Write-ups due on Dec. 17 |
The class enforces the GMU Honor Code. Violations of academic honesty will not be tolerated.
If a disability or other condition affects your academic performance, document it with the Office of Disability Services.
Latest lectures and other course materials will be available at
URL
http://www.cs.gmu.edu/~ashehu/?q=CS485_Fall2013