About this Course

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!

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Artificial Intelligence for Robotics
Course Cost
Free
Timeline
Approx. 2 months
Skill Level
Advanced
Included in Course
  • Icon course 01 3edf6b45629a2e8f1b490e1fb1516899e98b3b30db721466e83b1a1c16e237b1 Rich Learning Content

  • Icon course 04 2edd94a12ef9e5f0ebe04f6c9f6ae2c89e5efba5fd0b703c60f65837f8b54430 Interactive Quizzes

  • Icon course 02 2d90171a3a467a7d4613c7c615f15093d7402c66f2cf9a5ab4bcf11a4958aa33 Taught by Industry Pros

  • Icon course 05 237542f88ede3178ac4845d4bebf431ddd36d9c3c35aedfbd92e148c1c7361c6 Self-Paced Learning

  • Icon course 03 142f0532acf4fa030d680f5cb3babed8007e9ac853d0a3bf731fa30a7869db3a Student Support Community

Join the Path to Greatness

This free course is your first step towards a new career with the Machine Learning Engineer Nanodegree Program.

Free Course

Artificial Intelligence for Robotics

by Georgia Institute of Technology

Enhance your skill set and boost your hirability through innovative, independent learning.

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Course Leads

  • Sebastian Thrun
    Sebastian Thrun

    Instructor

What You Will Learn

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

Prerequisites and Requirements

Success in this course requires some programming experience and some mathematical fluency.

Programming in this course is done in Python. We will use some basic object-oriented concepts to model robot motion and perception. If you don’t know Python but have experience with another language, you should be able to pick up the syntax fairly quickly. If you have no programming experience, you should consider taking Udacity’s Introduction to Computer Science course before attempting this one.

The math used will be centered on probability and linear algebra. You don’t need to be an expert in either, but some familiarity with concepts in probability (e.g. probabilities must add to one, conditional probability, and Bayes’ rule) will be extremely helpful. It is possible to learn these concepts during the course, but it will take more work. Knowledge of linear algebra, while helpful, is not required.

See the Technology Requirements for using Udacity.

Why Take This Course

This course will teach you probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics.

At the end of the course, you will leverage what you learned by solving the problem of a runaway robot that you must chase and hunt down!

What do I get?
  • Instructor videos
  • Learn by doing exercises
  • Taught by industry professionals
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