Book Image

ROS Programming: Building Powerful Robots

By : Anil Mahtani, Aaron Martinez, Enrique Fernandez Perdomo, Luis Sánchez, Lentin Joseph
Book Image

ROS Programming: Building Powerful Robots

By: Anil Mahtani, Aaron Martinez, Enrique Fernandez Perdomo, Luis Sánchez, Lentin Joseph

Overview of this book

This learning path is designed to help you program and build your robots using open source ROS libraries and tools. We start with the installation and basic concepts, then continue with the more complex modules available in ROS, such as sensor and actuator integration (drivers), navigation and mapping (so you can create an autonomous mobile robot), manipulation, computer vision, perception in 3D with PCL, and more. We then discuss advanced concepts in robotics and how to program using ROS. You'll get a deep overview of the ROS framework, which will give you a clear idea of how ROS really works. During the course of the book, you will learn how to build models of complex robots, and simulate and interface the robot using the ROS MoveIt motion planning library and ROS navigation stacks. We'll go through great projects such as building a self-driving car, an autonomous mobile robot, and image recognition using deep learning and ROS. You can find beginner, intermediate, and expert ROS robotics applications inside! It includes content from the following Packt products: ? Effective Robotics Programming with ROS - Third Edition ? Mastering ROS for Robotics Programming ? ROS Robotics Projects
Table of Contents (37 chapters)
Title page
Copyright and Credits
Packt Upsell
Preface
Bibliography
Index

Summary


This chapter was a deep discussion of self-driving cars and their implementation. The chapter started by discussing the basics of self-driving car technology and its history. Afterward, 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 saw an open-source self-driving car project that incorporates 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 vehicle with ROS. We visualized the offline data of the vehicle using Rviz. Finally, we took a look at the Udacity self-driving...