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ROS Robotics Projects,

ROS Robotics Projects,

By : Ramkumar Gandhinathan
2.3 (3)
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ROS Robotics Projects,

ROS Robotics Projects,

2.3 (3)
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)
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Introduction to deep learning and its applications

What actually is deep learning? It is a buzzword in neural network technology. What is a neural network then? An artificial neural network is a computer software model that replicates the behavior of neurons in the human brain. A neural network is one way to classify data. For example, if we want to classify an image based on whether it contains an object or not, we can use this method.

There are several other computer software models for classification such as logistic regression and Support Vector Machine (SVM); a neural network is one of them. So, why are we not calling it a neural network instead of deep learning? The reason is that, in deep learning, we use a large number of artificial neural networks. So, you may ask, why was it not possible before? The answer is: to create a large number of neural networks (multilayer perceptron...

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