Artificial Intelligence for Robotics
Introduce of Artificial Intelligence for Robotics
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 modules and syllabus of Artificial Intelligence for Robotics
- 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
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