Artificial Intelligence for Robotics

Course Feature

Cost

Free
Provider

Udacity
Certificate

No Information
Language

English
Start Date

On-Demand
Learners

No Information
Duration

No Information
Instructor

Sebastian Thrun

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5.0

32,490 Ratings

Learn from the experts at Google and Stanford how to program the major systems of a robotic car. This Artificial Intelligence course covers probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics. With programming examples and assignments, you'll be able to build self-driving cars. Georgia Tech's Masters in Computer Science offers this course, which includes a final project of chasing a runaway robot. Don't miss out on this exciting opportunity!

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Course Overview

❗The content presented here is sourced directly from Udacity platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [June 30th, 2023]

(Please note this course detail is from the official platform)

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!

Course Syllabus

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

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

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