Artificial Intelligence for Robotics - Georgia Tech

Udacity
Online

Free

Important information

  • Course
  • Online
  • When:
    Flexible
Description

Learn how to program all the major systems of a robotic car. Topics include planning, search, localization, tracking, and control.

Important information
Venues

Where and when

Starts Location
Flexible
Online

What you'll learn on the course

Artificial Intelligence
Planning
systems
Robots
Localization
Simultaneous Localization and Mapping

Course programme

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