CS 685
Intelligent Robotic Systems

Time/Location: Thursday 4:30-7:10pm
Instructor: Jana Kosecka
Office hours: Wednesday 2-4pm
Contact: Office 417 STII, e-mail: kosecka@gmu.edu, 3-1876
Course web page: http://www.cs.gmu.edu/~kosecka/cs685.html


  Simulator software
       Homeworks, Lecture notes      
       1. Introduction, History, Overview  Applications. Geometric transformations, Rigid Body Motion 

           Rigid Body Motion notes (.pdf) , Homework 1 (.pdf) (due September 17th)
       2. Kinematic chains in 2D, 3D, Inverse Kinematics, Jacobians, Mobile robot kinematics.
          Homework 2 (.pdf) (due September 23rd) (.mat file)
       3. Modelling and controlling dynamical systems (notes.pdf) , Perception. Homework 3 (.pdf) 
           Due October 7th.
       4. Computer Vision, Image Formation (.pdf), Image filtering (.pdf), Image features (.pdf)
       5.  Canny edge detection (.pdf), line fitting (.pdf), feature correspondences (.pdf)
            Homework 4 (.pdf) Image (.jpg)
       6. Two view geometry, camera calibration (.pdf), Motion Planning (read chapter 6)
       7. Motion planning, obstacle avoidance, robotic behaviors/architectures (slides .pdf)
            Homework 5 (.pdf), due October 28th
       9. Localization, Markov based Localization, Kalman filtering. (practice questions.pdf, notes.pdf)
      10. Review. Take home final (.pdf)
      11.  MC Localization (.html)
      12. Dynamic programming and MDP's
      13. Reinforcement leaning
      14. Project Presentations in class December 9, final project report due Monday December 13th.

      Additional resources

      sample homework solutions (goTo.m,  diffDrive.m)

      Kalman Filter material repository.
      Introduction to the Kalman filter (notes).  


 Project Ideas and additional readings
 Sample in class exams  (.pdf)

Examples of previous projects,

Simulation of multiple space ships  behaviors.
Swarm Intelligence Problem Solving: Ant Colony Foraging.
Computer Animation of Human Action.
Analysis of Rectilinear Environment Discovery Algorithms.
Neural Networks for Path Finding.
Navigating and discrovering unknown environments.
Topological Map Building in Dynamic Environments.