Autonomous Robots: Model Predictive Control [Video]

More Information
Learn
  • Design and implement a Model Predictive Controller for an autonomous vehicle
  • Program a self-driving car pull into a parking space
  • Make a self-driving car follow the speed limit
  • Program a self-driving car to avoid obstacles
About

This course takes a practical, hands-on approach to teach you all about Model Predictive Control. MPC is crucial for solving a wide range of robotics as well as non-robotics problems. To enhance your learning experience, the author has created a simulator that will allow you to code an entire Model Predictive Controller and see the results of your work in real time. The objective of this course is to help you implement MPC in code and understand the MPC logic intuitively.

All the code and supporting files for this course are available here: https://github.com/PacktPublishing/Autonomous-Robots-Model-Predictive-Control

Features
  • Manage the speed of your autonomous vehicle on a highway
  • Learn how to park the vehicle
  • Avoid obstacles that are on the road in the way
Course Length 3 hours 49 minutes
ISBN 9781800560574
Date Of Publication 29 Apr 2020

Authors

Daniel Stang

Daniel Stang is a robotics software engineer who holds a master’s degree in mechanical engineering, which he earned for his research in control system design for automotive applications. In his first job out of school, he was responsible for designing motion controllers and stabilization systems for military tank turrets. Daniel has previously written robotic software for a startup based out of Toronto, Canada, and currently writes software for autonomous vehicles in California.