Artificial Intelligence for Robotics
About this Course
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 …
Artificial Intelligence for Robotics
About this Course
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!
Learn how to program all the major systems of a robotic car. Topics include planning, search, localization, tracking, and control.
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This course will teach you probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics.
,At the end of the course, you will leverage what you learned by solving the problem of a runaway robot that you must chase and hunt down!
]
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
