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)

Deep Learning Using ROS and TensorFlow

You may have come across deep learning many times on the web. Most of us are not fully aware of this technology, and many people are trying to learn it too. So, in this chapter, we are going to see the importance of deep learning in robotics and how we can implement robotics applications using deep learning and ROS. We begin with understanding how deep learning works and is implemented, followed by a glimpse of commonly used tools and libraries for deep learning. We will learn to install TensorFlow for Python and embed TensorFlow APIs in ROS. We will learn to carry out image recognition using ROS and TensorFlow. Later, you will come across practical examples using these libraries and interfacing them with ROS.

Here are the main topics we are going to discuss in this chapter:

  • Introducing deep learning and its applications
  • Deep learning for...