list 8 séquences
assignment Niveau : Introductif
chat_bubble_outline Langue : Anglais
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Les infos clés

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En résumé

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!

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Le programme

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
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Les intervenants

  • Sebastian Thrun - Sebastian Thrun is a Research Professor of Computer Science at Stanford University, a Google Fellow, a member of the National Academy of Engineering and the German Academy of Sciences. Thrun is best known for his research in robotics and machine learning, specifically his work with self-driving cars.
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Le concepteur

Georgia Institute of Technology
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La plateforme

Udacity est une entreprise fondé par Sebastian Thrun, David Stavens, et Mike Sokolsky offrant cours en ligne ouvert et massif.

Selon Thrun, l'origine du nom Udacity vient de la volonté de l'entreprise d'être "audacieux pour vous, l'étudiant ". Bien que Udacity se concentrait à l'origine sur une offre de cours universitaires, la plateforme se concentre désormais plus sur de formations destinés aux professionnels.

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