Book Image

Mastering ROS for Robotics Programming - Second Edition

By : Jonathan Cacace, Lentin Joseph
Book Image

Mastering ROS for Robotics Programming - Second Edition

By: Jonathan Cacace, Lentin Joseph

Overview of this book

In this day and age, robotics has been gaining a lot of traction in various industries where consistency and perfection matter. Automation is achieved via robotic applications and various platforms that support robotics. The Robot Operating System (ROS) is a modular software platform to develop generic robotic applications. This book focuses on the most stable release of ROS (Kinetic Kame), discusses advanced concepts, and effectively teaches you programming using ROS. We begin with aninformative overview of the ROS framework, which will give you a clear idea of how ROS works. During the course of this book, you’ll learn to build models of complex robots, and simulate and interface the robot using the ROS MoveIt! motion planning library and ROS navigation stacks. Learn to leverage several ROS packages to embrace your robot models. After covering robot manipulation and navigation, you’ll get to grips with the interfacing I/O boards, sensors, and actuators of ROS. Vision sensors are a key component of robots, and an entire chapter is dedicated to the vision sensor and image elaboration, its interface in ROS and programming. You’ll also understand the hardware interface and simulation of complex robots to ROS and ROS Industrial. At the end of this book, you’ll discover the best practices to follow when programming using ROS.
Table of Contents (22 chapters)
Title Page
Copyright and Credits
www.PacktPub.com
Contributors
Preface
Index

Working with a robot pick-and-place task using MoveIt!


We can do pick and place in various ways. One is by using pre-defined sequences of joint values; in this case, we put the object in a known position, and move the robot into towards that by providing direct joint values or forward kinematics. Another method of pick and place is by using inverse kinematics without any visual feedback. In this case, we command the robot to move in a cartesian position with respect to the robot base frame, and by solving IK. In this way, the robot can reach that position and pick up that object. One more method is to use external sensors, such as the vision ones, to calculate the pick-and-place positions; in this case, a vision sensor is used to identify the object's location, and the arm goes to that position by solving to pick the object. Of course, the use of vision sensors requires the development of robust algorithms to perform object recognition and tracking, and are able to calculate the best grasping...