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.
Table of Contents (14 chapters)

Introducing the Udacity open source self-driving car project

There is another open source self-driving car project by Udacity (https://github.com/udacity/self-driving-car) that was created for teaching their Nanodegree self-driving car program. This project aims to create a completely autonomous self-driving car using deep learning and using ROS as middleware for communication.

The project is split into a series of challenges, and anyone can contribute to the project and win a prize. The project is trying to train a Convolution Neural Network (CNN) from a vehicle camera dataset to predict steering angles. This approach is a replication of end-to-end deep learning from NVIDIA (https://devblogs.nvidia.com/parallelforall/deep-learning-self-driving-cars/), used in their self-driving car project called DAVE-2.

The following is the block diagram of DAVE-2. DAVE-2 stands for DARPA Autonomous...