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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

Questions

  1. Regarding SLAM, what sensor is most commonly used to create the data that SLAM needs to make a map?
  2. Why does SLAM work better with wheel odometer data available?
  3. In the Floor Finder algorithm, what does the Gaussian blur function do to improve the results?
  4. The final step in the Floor Finder is to trace upward from the robot position to the first red pixel. In what other way can this step be accomplished (referring to Figure 7.3)?
  5. Why did we cut the image in half horizontally before doing our neural network processing?
  6. What advantages does using the neural network approach provide that a technique such as SLAM does not?
  7. If we used just a random driving function instead of the neural network, what new program or function would we have to add to the robot to achieve the same results?
  8. How did we end up avoiding the stairs in the approach presented in the chapter? Do you feel this is adequate? Would you suggest any other means for accomplishing...
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