Artificial Intelligence for Robotics by Georgia Tech - Ubbergo

Artificial Intelligence for Robotics by Georgia Tech

FREE video Artificial Intelligence for Robotics by Georgia Tech

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Course type

Level

Advanced

Time/Effort

2 months

Created by

Georgia Tech, Udacity

Provider

Session type

Self-paced

Pricing

Instalments

N/A

Certificate

No

Projects

Yes

Tutor support

No

Access

Course lifetime

More features

Interactive quizzes, Practical exercises

Language

English

Subtitles

English

Skills Covered:

Probabilistic Models, Particle Filters, Kalman Filters, Localization & Mapping, Motion Planning, Control

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ABOUT THIS COURSE


Learn from the leaders of Google and Stanford’s autonomous driving teams how to program a robotic vehicle.

This session will introduce you to fundamental AI techniques, such as probabilistic inference, planning and search, localization, tracking, and control, with an emphasis on robotics. Advanced technical examples and assignments will demonstrate how to utilize these techniques in the context of self-driving vehicle development.

This course is part of the Georgia Tech Master of Science in Computer Science program. The new course features a final assignment in which you must pursue a running robot!

 

WHAT YOU WILL LEARN


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

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

 

Particle Filters

  • Slate Space
  • Belief Modality
  • Particle Filters
  • Using Robot Class
  • Robot World
  • Robot Particles

 

Search

  • Motion Planning
  • Compute Cost
  • Optimal Path
  • First Search Program
  • Expansion Grid
  • Dynamic Programming
  • Computing Value
  • Optimal Policy

 

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

 

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

 

WHY TAKE THIS COURSE?


This course will introduce you to probability inference, planning and search, localization, tracking, and control, with a particular emphasis on robotics.

At the conclusion of the course, you will use what you have learned by resolving the issue of a runaway robot that you must track down and capture!

Videos: Artificial Intelligence for Robotics by Georgia Tech

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Artificial Intelligence for Robotics by Georgia Tech
Artificial Intelligence for Robotics by Georgia Tech

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