Was: CS8803-O01 Special Topics: Artificial Intelligence For Robotics

An introduction to programming for robotics, taught by Sebastian Thrun himself. The Udacity course has a final project but this course has five projects throughout the course, though the homeworks are identical to what is on Udacity.

The primary techniques/algorithms covered are:

  • Kalman Filters
  • Particle Filters
  • PID (Proportional–Integral–Derivative) controller
  • A*
  • SLAM (Simultaneous Localization and Mapping)

This is a fairly easy class for an entire semester. Homework is to implement experiments and functions from the lecture in Python on Udacity. Almost all assignments, Thrun goes through his working code if you skip ahead, which can make this absurdly easy. Once you complete the homeworks the projects are heavily based on the lecture homework assignments, but do require additional work beyond the lectures to complete them.

Knowing Python ahead of time is helpful, especially numpy. Particle Filters and SLAM can get particularly tricky.

The book Probabilistic Robotics is useful on a couple more complex things like implementing Kalman filters, though many only resources are more directly useful/relevant to the projects. It's fairly basic on other topics. You can get through the class without it.

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