Artificial Intelligence for Robotics

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Free Online Course: Artificial Intelligence for Robotics provided by Udacity is a comprehensive online course, which lasts for 8 weeks long. The course is taught in English and is free of charge. Artificial Intelligence for Robotics is taught by Sebastian Thrun.

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!

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
    • Search
      • 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