Book Image

Hands-On ROS for Robotics Programming

By : Bernardo Ronquillo Japón
Book Image

Hands-On ROS for Robotics Programming

By: Bernardo Ronquillo Japón

Overview of this book

Connecting a physical robot to a robot simulation using the Robot Operating System (ROS) infrastructure is one of the most common challenges faced by ROS engineers. With this book, you'll learn how to simulate a robot in a virtual environment and achieve desired behavior in equivalent real-world scenarios. This book starts with an introduction to GoPiGo3 and the sensors and actuators with which it is equipped. You'll then work with GoPiGo3's digital twin by creating a 3D model from scratch and running a simulation in ROS using Gazebo. Next, the book will show you how to use GoPiGo3 to build and run an autonomous mobile robot that is aware of its surroundings. Finally, you'll find out how a robot can learn tasks that have not been programmed in the code but are acquired by observing its environment. You'll even cover topics such as deep learning and reinforcement learning. By the end of this robot programming book, you'll be well-versed with the basics of building specific-purpose applications in robotics and developing highly intelligent autonomous robots from scratch.
Table of Contents (19 chapters)
1
Section 1: Physical Robot Assembly and Testing
5
Section 2: Robot Simulation with Gazebo
8
Section 3: Autonomous Navigation Using SLAM
13
Section 4: Adaptive Robot Behavior Using Machine Learning

An introduction to OpenAI Gym

In the previous chapter, we provided a practical overview of what you can expect in RL when applied to robotics. In this chapter, we will provide a general view in which you will discover how RL is used to train smart agents.

First, we will need to install OpenAI Gym and OpenAI ROS on our laptop in preparation for the practical examples. Then, we will explain its concepts.

Installing OpenAI Gym

As we did in the previous chapter, we are going to create a virtual environment for the Python setup of this chapter, which we will call gym. The following two commands allow for the creation and then the activation of gym:

$ conda create -n gym pip python=2.7
$ conda activate gym

Following this, install...