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

Creating transforms


The navigation stack needs to know the position of the sensors, wheels, and joints.

To do that, we use the Transform Frames (tf) software library. It manages a transform tree. You could do this with mathematics, but if you have a lot of frames to calculate, it will be a bit complicated and messy.

Thanks to tf, we can add more sensors and parts to the robot, and tf will handle all the relations for us.

If we put the laser 10 cm backwards and 20 cm above with reference to the origin of the base_link coordinates, we would need to add a new frame to the transformation tree with these offsets.

Once inserted and created, we could easily know the position of the laser with reference to the base_link value or the wheels. The only thing we need to do is call the tf library and get the transformation.

Creating a broadcaster

Let's test this with a simple code. Create a new file in chapter5_tutorials/src with the name tf_broadcaster.cpp, and put the following code inside it:

#include &lt...