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Instructor Location and Time Office Hours |
Amarda Shehu , Room #4452 ENG, amarda\AT\gmu.edu Innovation Hall #136, M 4:30pm - 7:10 pm M 2:30-4:30 pm |
This course covers topics from artificial intelligence, algorithms, and databases. It presents algorithms that model and simulate physical and biological systems. The course will focus on motion-planning algorithms for robotic systems in the presence of obstacles. Simple deterministic and sampling-based approaches to motion planning will be covered. Advanced planning methods including planning with kinematics and dynamic constraints will also be presented. Selected topics will include sensor-based motion planning, manipulation planning, assembly planning, planning under uncertainty, and robotics-inspired methods for the modeling and characterization of biological molecules as special articulated chains.
Material will be disseminated in the form of lectures. Students will be tested on the comprehension of the basic material through homework programming projects and a midterm exam. In addition to the basic material, special topics will be covered. Extra credit in the 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 of a homework. No programming is involved in the exam, only pseudocode. No late homeworks or project deliverables will be accepted. A final research project will replace the final exam.
Minimum Grade of B- in CS 583.
| Jan. 22 | Course Overview, Bug Algorithms | Chapter 2 | Hw1 Out |
| Jan. 29 | Configuration Space | Chapter 3 | |
| Feb. 05 | Forward Kinematics | Chapter 3 | Hw1 Due, Hw2 Out |
| Feb. 12 | Inverse Kinematics, Potential Fields | Chapters 3-4 | |
| Feb. 19 | Deterministic Roadmap Planners | Chapters 5-6 | Hw2 Due |
| Mar. 05 | Exam |
| Sampling-based and Probabilistic Motion Planning | ||
| Mar. 19 | Probabilistic Roadmap | Chapter 7 | Hw3 Out |
| Mar. 26 | (Probabilistic) Tree Approaches | Chapter 7 | |
| Advanced Motion Planning | ||
| Apr. 02 | Multiple Robots, Manipulation Planning | Chapter 7.5 | Hw3 Due |
| Apr. 09 | Planning with Kinodynamic Constraints | Chapter 10 | Paper Selection |
| Apr. 16 | Paper presentations |
| Localization and Mapping | ||
| Apr. 23 | Representation of Uncertainty, Bayesian Methods | Chapter 9 | Project Topic Selection, Exam |
| Apr. 30 | Kalman Filtering, Mapping, and SLAM | Chapter8 | Short Progress Update |
| May 14 | Final Project Presentations | In place of final exam |
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, schedule updates, and other course materials will be
available at URL
http://www.cs.gmu.edu/~ashehu/?q=CS689_Spring2018