Book Image

ROS Robotics Projects - Second Edition

By : Ramkumar Gandhinathan
Book Image

ROS Robotics Projects - Second Edition

By: Ramkumar Gandhinathan

Overview of this book

Nowadays, heavy industrial robots placed in workcells are being replaced by new age robots called cobots, which don't need workcells. They are used in manufacturing, retail, banks, energy, and healthcare, among other domains. One of the major reasons for this rapid growth in the robotics market is the introduction of an open source robotics framework called the Robot Operating System (ROS). This book covers projects in the latest ROS distribution, ROS Melodic Morenia with Ubuntu Bionic (18.04). Starting with the fundamentals, this updated edition of ROS Robotics Projects introduces you to ROS-2 and helps you understand how it is different from ROS-1. You'll be able to model and build an industrial mobile manipulator in ROS and simulate it in Gazebo 9. You'll then gain insights into handling complex robot applications using state machines and working with multiple robots at a time. This ROS book also introduces you to new and popular hardware such as Nvidia's Jetson Nano, Asus Tinker Board, and Beaglebone Black, and allows you to explore interfacing with ROS. You'll learn as you build interesting ROS projects such as self-driving cars, making use of deep learning, reinforcement learning, and other key AI concepts. By the end of the book, you'll have gained the confidence to build interesting and intricate projects with ROS.
Table of Contents (14 chapters)

Summary

In this chapter, we understood what reinforcement learning is and how it stands out from other machine learning algorithms. The components of reinforcement learning were individually explained and we enhanced our understanding through examples. Then, reinforcement learning algorithms were introduced, both practically and mathematically, using suitable examples. We also saw reinforcement learning implementations in ROS, where robots such as TurtleBot 2 and the MARA robot arm were used in application-specific environments, and we understood how they're implemented and their usage. This chapter acted as a simple and gentle introduction to machine learning and its usage in ROS.

In the next chapter, we will see how deep we can dive into machine learning methods to make the agent more effective in terms of learning and achieving its goal.

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