Duration
21 hours (usually 3 days including breaks)
Requirements
- Programming experience
- Basic understanding of computer science and engineering
- Familiarity with probability concepts and linear algebra
Audience
- Engineers
Overview
Robotics is an area in artificial intelligence (AI) that deals with the programming and designing of intelligent and efficient machines.
This instructor-led, live training (online or onsite) is aimed at engineers who wish to program and create robots through basic AI methods.
By the end of this training, participants will be able to:
- Implement filters (Kalman and particle) to enable the robot to locate moving objects in its environment.
- Implement search algorithms and motion planning.
- Implement PID controls to regulate a robot’s movement within an environment.
- Implement SLAM algorithms to enable a robot to map out an unknown environment.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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