Meetings > Autonomy Short Course

Location: Levine Hall 307

August 12, 10:00 - 16:00

  1. Motivation (Mintz)
  2. Basics (Mintz)
    • 2.1 Basic linear algebra
    • 2.2 Basic probability theory
    • 2.3 Basic dynamic systems
    • 2.4 Discrete state equations
Lunch (on your own)

3 Filtering and state estimation - I (Mintz)

  • 3.1 The Kalman filter
  • 3.2 Exercises with linear systems

4 MATLAB (Kallem/Kushleyev/Mellinger/Lindsey/Gray)

  • 4.1 Implementation
  • 4.2 Exercise

August 13, 10:00 - 16:00

5 Filtering and state estimation - II

  • 5.1 Kinematic model of a mobile robot (Michael)
  • 5.2 Extended Kalman filter (Michael)
  • 5.3 Unscented Kalman Filter (Michael)
  • 5.4 Particle Filter (Michael)
  • 5.5 Implementation in MATLAB (Kallem/Kushleyev/Lindsey/Gray)
Working Lunch - Matlab/Player/Gazebo (Fink)

6 Planning

  • 6.1 The A* algorithm (Likachev)
  • 6.2 The D* algorithm (Likachev)
  • 6.3 Planning under uncertainty (Likachev) planningRobotics.pdf
  • 6.4 Demos (Kushleyev/Lindsey/Gray)