Artificial Intelligence for Robotics - Georgia Tech
Course
Online
Description
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Type
Course
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Methodology
Online
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Start date
Different dates available
Learn how to program all the major systems of a robotic car. Topics include planning, search, localization, tracking, and control.
Facilities
Location
Start date
Start date
Reviews
Subjects
- Artificial Intelligence
- Planning
- Systems
- Localization
- Robots
- Simultaneous Localization and Mapping
Course programme
- 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
- 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
- Slate Space
- Belief Modality
- Particle Filters
- Using Robot Class
- Robot World
- Robot Particles
- Motion Planning
- Compute Cost
- Optimal Path
- First Search Program
- Expansion Grid
- Dynamic Programming
- Computing Value
- Optimal Policy
- 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
- Localization
- Planning
- Segmented Ste
- Fun with Parameters
- SLAM
- Graph SLAM
- Implementing Constraints
- Adding Landmarks
- Matrix Modification
- Untouched Fields
- Landmark Position
- Confident Measurements
- Implementing SLAM
Artificial Intelligence for Robotics - Georgia Tech