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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!
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 ProbabilityLesson 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 MatricesLesson 3: Particle Filters
- Slate Space - Belief Modality - Particle Filters - Using Robot Class - Robot World - Robot ParticlesLesson 4: Search
- Motion Planning - Compute Cost - Optimal Path - First Search Program - Expansion Grid - Dynamic Programming - Computing Value - Optimal PolicyLesson 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 OptimizationLesson 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 ProjectInstructors
- 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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Udacity is a for-profit educational organization founded by Sebastian Thrun, David Stavens, and Mike Sokolsky offering massive open online courses (MOOCs). According to Thrun, the origin of the name Udacity comes from the company's desire to be "audacious for you, the student". While it originally focused on offering university-style courses, it now focuses more on vocational courses for professionals.
