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Learn Robotics Programming - Second Edition

You're reading from  Learn Robotics Programming - Second Edition

Product type Book
Published in Feb 2021
Publisher Packt
ISBN-13 9781839218804
Pages 602 pages
Edition 2nd Edition
Languages
Concepts
Author (1):
Danny Staple Danny Staple
Profile icon Danny Staple

Table of Contents (25) Chapters

Preface 1. Section 1: The Basics – Preparing for Robotics
2. Chapter 1: Introduction to Robotics 3. Chapter 2: Exploring Robot Building Blocks – Code and Electronics 4. Chapter 3: Exploring the Raspberry Pi 5. Chapter 4: Preparing a Headless Raspberry Pi for a Robot 6. Chapter 5: Backing Up the Code with Git and SD Card Copies 7. Section 2: Building an Autonomous Robot – Connecting Sensors and Motors to a Raspberry Pi
8. Chapter 6: Building Robot Basics – Wheels, Power, and Wiring 9. Chapter 7: Drive and Turn – Moving Motors with Python 10. Chapter 8: Programming Distance Sensors with Python 11. Chapter 9: Programming RGB Strips in Python 12. Chapter 10: Using Python to Control Servo Motors 13. Chapter 11: Programming Encoders with Python 14. Chapter 12: IMU Programming with Python 15. Section 3: Hearing and Seeing – Giving a Robot Intelligent Sensors
16. Chapter 13: Robot Vision – Using a Pi Camera and OpenCV 17. Chapter 14: Line-Following with a Camera in Python 18. Chapter 15: Voice Communication with a Robot Using Mycroft 19. Chapter 16: Diving Deeper with the IMU 20. Chapter 17: Controlling the Robot with a Phone and Python 21. Section 4: Taking Robotics Further
22. Chapter 18: Taking Your Robot Programming Skills Further 23. Chapter 19: Planning Your Next Robot Project – Putting It All Together 24. Other Books You May Enjoy

Line-following computer vision pipeline

As we did with the previous computer vision tasks, we will visualize this as a pipeline. Before we do, there are many methods for tracking a line with computer vision.

Camera line-tracking algorithms

It is in our interests to pick one of the simplest ones, but as always, there is a trade-off, in that others will cope with more tricky situations or anticipate curves better than ours.

Here is a small selection of methods we could use:

  • Using edge detection: An edge detection algorithm, such as the Canny edge detector, can be run across the image, turning any transitions it finds into edges. OpenCV has a built-in edge detection system if we wanted to use this. The system can detect dark-to-light and light-to-dark edges. It is more tolerant of less sharp edges.
  • Finding differences along lines: This is like cheeky edge detection, but only on a particular row. By finding the difference between each pixel along a row in the image...
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