Artificial Intelligence for Robotics (Udacity)

Artificial Intelligence for Robotics (Udacity)

Artificial Intelligence for Robotics is online course offered by Stanford University via Udacity.

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Overview

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!

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!

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

Coach

Sebastian Thrun

Additional information

Course Delivery

Online

Course Enrollment

Free

Course Efforts

5-6 Hours a Week

Course Language

English

Course Length

8 Weeks

Course Level

Advanced

Course Provider

Course School

Course Subtitles

English

Flexible Learning

Yes

Verified Certificate

Free

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