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

This chapter was an in-depth discussion of self-driving cars and their implementation. This chapter started by discussing the basics of self-driving car technology and its history. After that, we discussed the core blocks of a typical self-driving car. We also discussed the concept of autonomy levels in self-driving cars. Then, we took a look at different sensors and components commonly used in a self-driving car.

We discussed how to simulate such a car in Gazebo and interfacing it with ROS. After discussing all sensors, we looked at an open source self-driving car project that incorporated all sensors and simulated the car model itself in Gazebo. We visualized its sensor data and moved the robot using a teleoperation node. We also mapped the environment using hector SLAM. The next project was from Dataspeed Inc., in which we saw how to interface a real DBW-compatible...