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.

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Table of Contents (19 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Section 1: Physical Robot Assembly and Testing
Section 1: Physical Robot Assembly and Testing
Free Chapter
2
Assembling the Robot
Assembling the Robot
Understanding the GoPiGo3 robot
Getting familiar with the embedded hardware
Deep diving into the electromechanics
Putting it all together
Quick hardware test
Summary
Questions
Further reading
3
Unit Testing of GoPiGo3
Unit Testing of GoPiGo3
Technical requirements
Getting started with Python and JupyterLab
Unit testing of sensors and drives
Summary
Questions
Further reading
4
Getting Started with ROS
Getting Started with ROS
Technical requirements
ROS basic concepts
Configuring your ROS development environment
Communication between ROS nodes – messages and topics
Using publicly available packages for ROS
Summary
Questions
Further reading
5
Section 2: Robot Simulation with Gazebo
Section 2: Robot Simulation with Gazebo
6
Creating the Virtual Two-Wheeled ROS Robot
Creating the Virtual Two-Wheeled ROS Robot
Technical requirements
Getting started with RViz for robot visualization
Building a differential drive robot with URDF
Inspecting the GoPiGo3 model in ROS with RViz
Robot frames of reference in the URDF model
Using RViz to check the model while building
Summary
Questions
Further reading
7
Simulating Robot Behavior with Gazebo
Simulating Robot Behavior with Gazebo
Technical requirements
Getting started with the Gazebo simulator
Making modifications to the robot URDF
Verifying a Gazebo model and viewing the URDF
Moving your model around
Summary
Questions
Further reading
8
Section 3: Autonomous Navigation Using SLAM
Section 3: Autonomous Navigation Using SLAM
9
Programming in ROS - Commands and Tools
Programming in ROS - Commands and Tools
Technical requirements
Setting up a physical robot
A quick introduction to ROS programming
Case study 1 – writing a ROS distance-sensor package
Working with ROS commands
Creating and running publisher and subscriber nodes
Automating the execution of nodes using roslaunch
Case study 2 – ROS GUI development tools – the Pi Camera
Customizing robot features using ROS parameters
Summary
Questions
Further reading
10
Robot Control and Simulation
Robot Control and Simulation
Technical requirements
Setting up the GoPiGo3 development environment
Case study 3 – remote control using the keyboard
Remote control using ROS topics
Remotely controlling both physical and virtual robots
Summary
Questions
Further reading
11
Virtual SLAM and Navigation Using Gazebo
Virtual SLAM and Navigation Using Gazebo
Technical requirements
Dynamic simulation using Gazebo
Components in navigation
Robot perception and SLAM
Practising SLAM and navigation with the GoPiGo3
Summary
Questions
Further reading
12
SLAM for Robot Navigation
SLAM for Robot Navigation
Technical requirements
Preparing an LDS for your robot
Creating a navigation application in ROS
Practicing navigation with GoPiGo3
Summary
Questions
Further reading
13
Section 4: Adaptive Robot Behavior Using Machine Learning
Section 4: Adaptive Robot Behavior Using Machine Learning
14
Applying Machine Learning in Robotics
Applying Machine Learning in Robotics
Technical requirements
Setting up the system for TensorFlow
ML comes to robotics
From ML to deep learning
A methodology to programmatically apply ML in robotics
Deep learning applied to robotics – computer vision
Summary
Questions
Further reading
15
Machine Learning with OpenAI Gym
Machine Learning with OpenAI Gym
Technical requirements
An introduction to OpenAI Gym
Running an environment
Configuring the environment file
Running the simulation and plotting the results
Summary
Questions
Further reading
16
Achieve a Goal through Reinforcement Learning
Achieve a Goal through Reinforcement Learning
Technical requirements
Preparing the environment with TensorFlow, Keras, and Anaconda
Understanding the ROS Machine Learning packages
Setting the training task parameters
Training GoPiGo3 to reach a target location while avoiding obstacles
Summary
Questions
Further reading
17
Assessment
Assessment
Chapter 1: Assembling the Robot
Chapter 2: Unit Testing of GoPiGo3
Chapter 3: Getting Started with ROS
Chapter 4: Creating the Virtual Two-Wheeled ROS Robot
Chapter 5: Simulating Robot Behavior with Gazebo
Chapter 6: Programming in ROS - Commands and Tools
Chapter 7: Robot Control and Simulation
Chapter 8: Virtual SLAM and Navigation Using Gazebo
Chapter 9: SLAM for Robot Navigation
Chapter 10: Applying Machine Learning in Robotics
Chapter 11: Machine Learning with OpenAI Gym
Chapter 12: Achieve a Goal through Reinforcement Learning
18
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Section 4: Adaptive Robot Behavior Using Machine Learning

This section is specifically devoted to artificial intelligence in robotics. Building on top of the robot navigation task we covered in the previous section, we will learn how to make that behavior adaptive so that the robot is capable of learning from the results of its actions in order to improve its performance, that is, so that it can achieve the desired goal faster.

This section comprises the following chapters:

  • Chapter 10, Applying Machine Learning in Robotics
  • Chapter 11, Achieving a Goal through Reinforcement Learning
  • Chapter 12, Machine Learning with OpenAI Gym
Previous Section
End of Chapter 13
Next Chapter