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Updated in [June 30th, 2023]
(Please note this course detail is from the official platform)
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!
Course 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 ProbabilityKalman 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 MatricesParticle Filters
Slate Space,Belief Modality,Particle Filters,Using Robot Class,Robot World,Robot ParticlesSearch
Motion Planning,Compute Cost,Optimal Path,First Search Program,Expansion Grid,Dynamic Programming,Computing Value,Optimal PolicyPID 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 OptimizationSLAM (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
