Artificial Intelligence for Robotics

Learn how to program all the major systems of a robotic car. Topics include planning, search, localization, tracking, and control.

Artificial Intelligence for Robotics

PriceFREE TRIAL

SchoolUdacity
ScheduleOn Demand
LocationOnline
Duration2 months
Credits0
More Details
Rating Not Rated
Reviews No Reviews
PopularityN/A
In CertificateNo
Difficultyadvanced
EffortN/A
Course DetailsCourse FAQ

Artificial Intelligence for Robotics

### 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 ### Runaway Robot Final Project

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 provided by: Udacity