CS 4803-RBA - Robotics AI Techniques

Special Topics CS 4903-RBA Taught in the summer of 2024 is an undergraduate course based on the CS 7638 - Robotics AI Techniques graduate course. You will learn how to program all the major systems of a robotic car. You will be watching lectures from the former leader of Google’s and Stanford's autonomous driving teams, Sebastian Thrun before class, and receiving supplemental instruction and activities to test your knowledge in class. You will learn some of the basic techniques in artificial intelligence, including probabilistic inference, planning and search algorithms, localization, tracking, and PID control, all with a focus on robotics. Extensive programming examples and assignments in Python will apply these methods in the context of autonomous vehicles.

Learning Objectives

Upon successfully completing this course, you will be able to:

Instructors

Materials

  • Textbook: Probabilistic Robotics by Wolfram Burgard, Dieter Fox, and Sebastian Thrun.
    www.probabilistic-robotics.org
  • PBS Nova: The Great Robot Race
  • Canvas is the primary website you will be using for this course ( https://gatech.instructure.com/ ). Lectures and problem sets will be accessed via Canvas in the Modules and Assignments pages, respectively.
  • There is also an older (Python 2) version of the course videos available for free on the Udacity website, which you can find at the direct URL: https://www.udacity.com/course/artificial-intelligencefor-robotics--cs373.
  • All online course communication including public questions about content and private questions about individual grades will be handled via the EdDiscussion website. You will be automatically enrolled in EdDiscussion using your GaTech Official login.


Projects

  1. Kalman Filter Project
  2. Particle Filter Project
  3. PID Project
  4. Search (A* & VI Policy) Project
  5. SLAM Project

Important Dates and Deadlines

Wed, May 15th, 2024 First Day of Class
Monday, May 20thSyllabus & Policy Guidelines Quiz Due
Wed, May 22ndProblem Set 0 Due
Fri, May 24thProblem Set 1 Due
Wed, May 29thProblem Set 2 Due
Wed, June 5thKalman Filter Project Due
Mon, June 10thProblem Set 3 Due
Fri, June 14thProblem Set 4 Due
Wed, June 19thParticle Filter Project Due
Mon, June 24thProblem Set 5 Due
Wed June 26thMidterm Exam
(Covering the Topics of: Localization, Kalman Filters, Particle Filters)
Fri, June 28thPID Project Due
Wed, July 10thSearch Project Due
Fri July 12thProblem Set 6 Due
Mon, July 22ndSLAM Project Due
Wed, July 24thFinal Exam
(Comprehensive, covering all course material, but focusing on last 3 modules.)

Grading Policy

Your overall course grade will be calculated from your weighted scores on the following deliverable items:
The minimum required percentage scores (we do NOT round up) for course letter grades are:
Students wishing to use this course for academic credit within the Georgia Tech College of Computing Threads model may find this letter describing this 4803RBA special topics course in relation to other CoC undergraduate courses useful.