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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Getting Started with ROS

Getting Started with ROS

Technical requirements

Getting started with ROS

Fundamentals of ROS

ROS client libraries

Installing ROS Melodic on Ubuntu 18.04 LTS

Setting up ROS on VirtualBox

Introduction to Docker

Setting up the ROS workspace

Opportunities for ROS in industries and research

Introduction to ROS-2 and Its Capabilities

Introduction to ROS-2 and Its Capabilities

Technical requirements

Getting started with ROS-2

Fundamentals of ROS-2

ROS-2 client libraries (RCL)

Installing ROS-2

Setting up the ROS-2 workspace

Writing ROS-2 nodes

Bridging ROS-1 and ROS-2

Building an Industrial Mobile Manipulator

Building an Industrial Mobile Manipulator

Technical requirements

Understanding available mobile manipulators

Applications of mobile manipulators

Getting started building mobile manipulators

Building the robot base

Getting started building the robot arm

Putting things together

Handling Complex Robot Tasks Using State Machines

Handling Complex Robot Tasks Using State Machines

Technical requirements

Introduction to ROS actions

Waiter robot analogy

Introduction to state machines

Introduction to SMACH

Getting started with SMACH examples

Building an Industrial Application

Building an Industrial Application

Technical requirements

Application use case – robot home delivery

Making our robot base intelligent

Making our robot arm intelligent

Simulating the application

Improvements to the robot

Multi-Robot Collaboration

Multi-Robot Collaboration

Technical requirements

Understanding the swarm robotics application

Swarm robot classification

Multiple robot communication in ROS

Introduction to the multimaster concept

A multi-robot use case

ROS on Embedded Platforms and Their Control

ROS on Embedded Platforms and Their Control

Technical requirements

Understanding embedded boards

Introduction to microcontroller boards

Introduction to single-board computers

Debian versus Ubuntu

Setting up ROS on Tinkerboard S

Setting up ROS on BeagleBone Black

Setting up ROS on Raspberry Pi 3/4

Setting up ROS on Jetson Nano

Controlling GPIOS from ROS

Benchmarking embedded boards

Getting started with Alexa and connecting with ROS

Reinforcement Learning and Robotics

Reinforcement Learning and Robotics

Technical requirements

Introduction to machine learning

Understanding reinforcement learning

MDP and the Bellman equation

Reinforcement learning algorithms

Reinforcement learning in ROS

Deep Learning Using ROS and TensorFlow

Deep Learning Using ROS and TensorFlow

Technical requirements

Introduction to deep learning and its applications

Deep learning for robotics

Deep learning libraries

Getting started with TensorFlow

Image recognition using ROS and TensorFlow

Introducing to scikit-learn

Introduction to SVM and its application in robotics

Creating a Self-Driving Car Using ROS

Creating a Self-Driving Car Using ROS

Technical requirements

Getting started with self-driving cars

Components of a typical self-driving car

Simulating and interfacing self-driving car sensors in ROS

Simulating a self-driving car with sensors in Gazebo

Interfacing a DBW car with ROS

Introducing the Udacity open source self-driving car project

Teleoperating Robots Using a VR Headset and Leap Motion

Teleoperating Robots Using a VR Headset and Leap Motion

Technical requirements

Getting started with a VR headset and Leap Motion

Designing and working on the project

Installing the Leap Motion SDK on Ubuntu 14.04.5

Visualizing Leap Motion data in RViz

Creating a teleoperation node using the Leap Motion controller

Building a ROS-VR Android application

Working with the ROS-VR application and interfacing with Gazebo

TurtleBot simulation in VR

Troubleshooting the ROS-VR application

Integrating the ROS-VR application and Leap Motion teleoperation

Face Detection and Tracking Using ROS, OpenCV, and Dynamixel Servos

Face Detection and Tracking Using ROS, OpenCV, and Dynamixel Servos

Technical requirements

Overview of the project

Hardware and software prerequisites

Configuring a Dynamixel servo using RoboPlus

Interfacing Dynamixel with ROS

Creating face tracker ROS packages

Working with the face-tracking ROS package

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Deep learning libraries

There are some popular deep learning libraries used in research and commercial applications. Let's look at them individually here:

TensorFlow: This is an open source software library for numerical computation using data flow graphs. The TensorFlow library (https://www.tensorflow.org/) is designed for machine intelligence and developed by the Google Brain team. The main aim of this library is to perform machine learning and deep neural network research. It can be used in a wide variety of other domains as well.
Theano: Theano is an open source Python library (http://deeplearning.net/software/theano/) allowing us to streamline and assess mathematical expressions including multidimensional arrays proficiently. Theano was essentially created by the machine learning group at the Montreal University, Canada.

Torch: Torch is a scientific computing framework...