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)

Introduction to SVM and its application in robotics

We have set up scikit-learn, so what is next? Actually, we are going to discuss a popular machine learning technique called SVM and its applications in robotics. After discussing the basics, we can implement an ROS application using SVM.

So, what is SVM? It is a supervised machine learning algorithm that can be used for classification or regression. In SVM, we plot each data item in n-dimensional space along with its value. After plotting, it performs classification by finding a hyper-plane that separates those data points. This is how the basic classification is done! SVM can perform better for small datasets, but it does not do well if the dataset is very large. Also, it will not be suitable if the dataset has noisy data. SVM is widely used in robotics, especially in computer vision for classifying objects and various kinds...