# Artificial Intelligence for Robotics (Udacity)

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

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Artificial Intelligence for Robotics (Udacity)

## 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
• Matrix Modification
• Untouched Fields
• Landmark Position
• Confident Measurements
• Implementing SLAM

Runaway Robot Final Project

Sebastian Thrun