Artificial Intelligence (AI) for Robotics

• Programming experience
• Basic understanding of computer science and engineering
• Familiarity with probability concepts and linear algebra

Audience

• Engineers

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.

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