Book Image

ROS Robotics By Example, Second Edition - Second Edition

By : Carol Fairchild, Lentin Joseph, Dr. Thomas L. Harman
Book Image

ROS Robotics By Example, Second Edition - Second Edition

By: Carol Fairchild, Lentin Joseph, Dr. Thomas L. Harman

Overview of this book

ROS is a robust robotics framework that works regardless of hardware architecture or hardware origin. It standardizes most layers of robotics functionality from device drivers to process control and message passing to software package management. But apart from just plain functionality, ROS is a great platform to learn about robotics itself and to simulate, as well as actually build, your first robots. This does not mean that ROS is a platform for students and other beginners; on the contrary, ROS is used all over the robotics industry to implement flying, walking and diving robots, yet implementation is always straightforward, and never dependent on the hardware itself. ROS Robotics has been the standard introduction to ROS for potential professionals and hobbyists alike since the original edition came out; the second edition adds a gradual introduction to all the goodness available with the Kinetic Kame release. By providing you with step-by-step examples including manipulator arms and flying robots, the authors introduce you to the new features. The book is intensely practical, with space given to theory only when absolutely necessary. By the end of this book, you will have hands-on experience on controlling robots with the best possible framework.
Table of Contents (18 chapters)
ROS Robotics By Example Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Index

Summary


The aim of this chapter was to stretch your knowledge of ROS by implementing an advanced practical experience to identify and highlight some of the ROS advantages. A ROS system of nodes was created to visualize the environment in which a Crazyflie quadrotor was seen and controlled. The Kinect for Windows v2 depth camera was used to visualize this environment, and ROS nodes handled the detection of markers on the Crazyflie and the target. The location of the Crazyflie was identified in Cartesian coordinates (x, y, z), with the x and y values referring to the quadrotor's position in the image frame and z referring to its distance from the camera. These coordinates were converted into a tf transform and published. The target location was published in a message by a separate ROS node.

The advantage of ROS layers of tf and message passing leaves lower-level details to be handled by another dedicated node. The tf transform for the Crazyflie was used by a controller node to apply PID control...