- Regarding SLAM, what sensor is most commonly used to create the data that SLAM needs to make a map?
- Why does SLAM work better with wheel odometer data available?
- In the Floor Finder algorithm, what does the Gaussian blur function do to improve the results?
- 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?
- Why did we cut the image in half horizontally before doing our neural network processing?
- What advantages does using the neural network approach provide that a technique such as SLAM does not?
- 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?
- How did we end up avoiding the stairs in the approach presented in the chapter? Do you feel this is adequate? Would...
Artificial Intelligence for Robotics
By :
Artificial Intelligence for Robotics
By:
Overview of this book
Artificial Intelligence for Robotics starts with an introduction to Robot Operating Systems (ROS), Python, robotic fundamentals, and the software and tools that are required to start out with robotics. You will learn robotics concepts that will be useful for making decisions, along with basic navigation skills.
As you make your way through the chapters, you will learn about object recognition and genetic algorithms, which will teach your robot to identify and pick up an irregular object. With plenty of use cases throughout, you will explore natural language processing (NLP) and machine learning techniques to further enhance your robot. In the concluding chapters, you will learn about path planning and goal-oriented programming, which will help your robot prioritize tasks.
By the end of this book, you will have learned to give your robot an artificial personality using simulated intelligence.
Table of Contents (13 chapters)
Preface
Free Chapter
Foundation for Advanced Robotics and AI
Setting Up Your Robot
A Concept for a Practical Robot Design Process
Object Recognition Using Neural Networks and Supervised Learning
Picking up the Toys
Teaching a Robot to Listen
Avoiding the Stairs
Putting Things Away
Giving the Robot an Artificial Personality
Conclusions and Reflections
Assessments
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