Course Summary

Learn how to program all the major systems of a robotic car from the leader of Google and Stanford’s autonomous driving teams. This class will teach you basic methods in Artificial Intelligence, including: probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics. Extensive programming examples and assignments will apply these methods in the context of building self-driving cars.

This course is offered as part of the Georgia Tech Masters in Computer Science. The updated course includes a final project, where you must chase a runaway robot that is trying to escape!

What Will I Learn?

Syllabus

Lesson 1: Localization

Localization
Total Probability
Uniform Distribution
Probability After Sense
Normalize Distribution
Phit and Pmiss
Sum of Probabilities
Sense Function
Exact Motion
Move Function
Bayes Rule
Theorem of Total Probability
Lesson 2: Kalman Filters

Gaussian Intro
Variance Comparison
Maximize Gaussian
Measurement and Motion
Parameter Update
New Mean Variance
Gaussian Motion
Kalman Filter Code
Kalman Prediction
Kalman Filter Design
Kalman Matrices
Lesson 3: Particle Filters

Slate Space
Belief Modality
Particle Filters
Using Robot Class
Robot World
Robot Particles
Lesson 4: Search

Motion Planning
Compute Cost
Optimal Path
First Search Program
Expansion Grid
Dynamic Programming
Computing Value
Optimal Policy
Lesson 5: PID Control

Robot Motion
Smoothing Algorithm
Path Smoothing
Zero Data Weight
Pid Control
Proportional Control
Implement P Controller
Oscillations
Pd Controller
Systematic Bias
Pid Implementation
Parameter Optimization
Lesson 6: SLAM (Simultaneous Localization and Mapping)

Localization
Planning
Segmented Ste
Fun with Parameters
SLAM
Graph SLAM
Implementing Constraints
Adding Landmarks
Matrix Modification
Untouched Fields
Landmark Position
Confident Measurements
Implementing SLAM
Runaway Robot Final Project

DOWNLOAD PART 1 | 1 GB

DOWNLOAD PART 2 | 1 GB

DOWNLOAD PART 3 | 200 MB