Book Image

Artificial Intelligence for Robotics

By : Francis X. Govers III
Book Image

Artificial Intelligence for Robotics

By: Francis X. Govers III

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)

Summary

Well, this has been a very busy chapter. We covered the various uses of decision trees for a variety of applications. The garden variety decision tree has leaves (nodes) and links, or branches, that each represent a decision or a change in a path. We learned about Fishbone diagrams and root cause analysis, a special type of decision tree. We showed a method using Scikit-Learn to have the computer build a classification decision tree for us and create a usable graph. We discussed the concept of random forests, which are just an evolved form of using groups of decision trees to perform prediction or regression. Then we got into graph search algorithms and path planners, spending some time on the A* or A-Star algorithm, which is widely used for making routes and paths. For times when we do not have a map created in advance, the D* or Dynamic A-Star process can use dynamic...