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Artificial Intelligence for Robotics

You're reading from   Artificial Intelligence for Robotics Build intelligent robots using ROS 2, Python, OpenCV, and AI/ML techniques for real-world tasks

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Product type Paperback
Published in Mar 2024
Publisher Packt
ISBN-13 9781805129592
Length 344 pages
Edition 2nd Edition
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Author (1):
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Francis X. Govers III Francis X. Govers III
Author Profile Icon Francis X. Govers III
Francis X. Govers III
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Table of Contents (18) Chapters Close

Preface 1. Part 1: Building Blocks for Robotics and Artificial Intelligence
2. Chapter 1: The Foundation of Robotics and Artificial Intelligence FREE CHAPTER 3. Chapter 2: Setting Up Your Robot 4. Chapter 3: Conceptualizing the Practical Robot Design Process 5. Part 2: Adding Perception, Learning, and Interaction to Robotics
6. Chapter 4: Recognizing Objects Using Neural Networks and Supervised Learning 7. Chapter 5: Picking Up and Putting Away Toys using Reinforcement Learning and Genetic Algorithms 8. Chapter 6: Teaching a Robot to Listen 9. Part 3: Advanced Concepts – Navigation, Manipulation, Emotions, and More
10. Chapter 7: Teaching the Robot to Navigate and Avoid Stairs 11. Chapter 8: Putting Things Away 12. Chapter 9: Giving the Robot an Artificial Personality 13. Chapter 10: Conclusions and Reflections 14. Answers 15. Index 16. Other Books You May Enjoy Appendix

Chapter 7

  1. Regarding SLAM, what sensor is most used to create the data that SLAM needs to make a map?

    Light detection and ranging (LiDAR) sensors are the most common SLAM sensors used by a wide margin. The 3D data that LiDAR provides is perfect for SLAM’s mapping function.

  2. Why does SLAM work better with wheel odometer data available?

    The wheel odometers reduce the search space that the SLAM algorithm needs to look for the possible locations of the robot after moving. Thus, it increases information and reduces uncertainty in the map. How does it do this? By giving extra measurements about where the robot is located (how far it moved), we can then reduce our search to match where we are against our sensor readings.

  3. In the Floor Finder algorithm, what does the Gaussian blur function do to improve the results?

    The Gaussian blur function reduces noise and gets rid of stray single pixels in the image, making for a smoother result.

  4. The final step in Floor Finder is to...
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