Free Course
Artificial Intelligence for Robotics
School Of Artificial Intelligence
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
Free Course
Artificial Intelligence for Robotics
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Nanodegree Program
Introduction to Programming
Course Details
Included in Product
Rich Learning Content
Taught by Industry Professionals
Student Support Community
Interactive Quizzes
Self-Paced Learning
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 Leads
Sebastian Thrun
InstructorSyllabus
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