Artificial Intelligence for Robotics - Georgia Tech

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Gratis

Informazione importanti

  • Corso
  • Online
  • Quando:
    Da definire
Descrizione

Learn how to program all the major systems of a robotic car. Topics include planning, search, localization, tracking, and control.

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Dove e quando

Inizio Luogo
Da definire
Online

Cosa impari in questo corso?

Artificial Intelligence
Planning
systems
Robots
Localization
Simultaneous Localization and Mapping

Programma

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