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Course Outline
Introduction
Overview of Artificial Intelligence (AI) and Robotics
- Computer-simulated versus physical
- Robotics as a branch of AI
- Applications for AI in robotics
Understanding Localization
- Locating your robot
- Using sensors to assess location and environment
- Probability exercises
Learning About Robot Motion
- Exact and inexact motions
- Sense and move functions
Using Probability Tools
- Bayes’ rule
- Theorem of total probability
Estimating Vehicle State Using Kalman Filter
- Gaussian processes
- Measurement and motion
- Kalman filtering (code, prediction, design, and matrices)
Tracking Your Robotic Car Using Particle Filter
- State space dimension and brief modality
- Robot class, robot world, and robot particles
Exploring Planning and Search Methods
- A* search algorithm
- Motion planning
- Compute cost and optimal path
Programming Your AI Robot
- First search program and expansion grid table
- Dynamic programming
- Computing value and optimal policy
Using PID Control
- Robot motion and path smoothing
- Implementing PID controller
- Parameter optimization
Mapping and Tracking Using SLAM
- Constraints
- Landmarks
- Implementing SLAM
Troubleshooting
Summary and Conclusion
Requirements
- Programming experience
- Basic understanding of computer science and engineering
- Familiarity with probability concepts and linear algebra
Audience
- Engineers
21 Hours
Testimonials (1)
Good conceptual explanations followed by good example exercises
