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Instructor Location and Time Office Hours |
Amarda Shehu , Room #4452 ENG, amarda\AT\gmu.edu Art and Design Building L008, MW, 12:00pm - 1:15 pm MW, 11:00 - 11:59 am |
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 two exams 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 exams. No late homeworks or project deliverables will be accepted. A final research project will provide students with an opportunity to implement a published algorithm and present. An in-class presentation will demonstrate project progress in the form of a conference presentation. 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. 29 | Introduction and Course Overview |
| Basic Motion-Planning Algorithms and Foundations |
| Aug. 31 | Bug0, Bug1 | ||
| Sep. 07 | Bug1, Bug2 | Hw1 Out | |
| Sep. 12 | Configuration Spaces | ||
| Sep. 14 | Forward Kinematics | ||
| Sep. 19 | Inverse Kinematics | ||
| Sep. 21 | Inverse Kinematics contd | ||
| Sep. 26 | Potential Fields | Hw1 Due | |
| Sep. 28 | Potential Fields contd | Hw2 Out | |
| Oct. 03 | Deterministic Roadmap Planners | ||
| Oct. 05 | Deterministic Roadmap Planners contd | ||
| Oct. 10 | Exam 1 |
| Sampling-based and Probabilistic Motion Planning | ||
| Oct. 12 | Probabilistic Roadmap | ||
| Oct. 17 | Biased Sampling | Hw2 Due | |
| Oct. 19 | Random Trees | Hw3 Out | |
| Oct. 24 | Planning with Kinematic Constraints | ||
| Oct. 26 | Wheeled Systems | ||
| Oct. 31 | Planning with Kinodynamic Constraints |
| Advanced Motion Planning | ||
| Nov. 02 | Multi-Robot Planning | Hw3 Due | |
| Nov. 07 | Multi-Robot Planning contd | Project Topics | |
| Nov. 09 | Manipulation Planning | ||
| Nov. 14 | Paper Presentations |
| Probabilistic Robotics | ||
| Nov. 16 | Perception | ||
| Nov. 21 | Range Sensing | ||
| Nov. 28 | Uncertainty, Bayesian Methods | ||
| Nov. 30 | Recursive Bayesian Filtering | Project Preliminary Report | |
| Dec. 05 | Kalman Filtering, SLAM | ||
| Dec. 07 | Exam 2 | ||
| Dec. 19 | Project Demos and Presentations | Report due before class |
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_Fall2016