Hands-On ROS with GoPiGo3 Robot

The Witty Book on ROS teaching Navigation and Machine Learning with a Low Cost Robot

Following the former story Learning Robotics with ROS made easy I am proud to announce the publication of a new book to learn ROS from scratch, reaching advanced topics such as Robot Navigation and Deep & Reinforcement Learning applied to Robotics.

Thanks to the technical support of the Dexter Industries team, manufacturer of GoPiGo3, Packt Publishing has delivered today the electronic version of Hands-On ROS for Robotics Programming. You can also get the book in the electronic or paperback version at Amazon.

You surely know programming is but a small part of what it takes to work with robots. To become really good at robotics, you’ll also need to master areas such as electromechanics, robot simulation, autonomous navigation, and machine learning/reinforcement learning.

Following this vision, the book is divided into four parts, each one getting deeper into the areas listed above.

Part 1, Physical Robot Assembly and Testing, focuses on electromechanics and describes each hardware part of the robot, providing practical demonstrations of how to test every sensor and actuator that it is equipped with. This part of the book should provide you with a good understanding of how a mobile robot works.

Part 2, Robot Simulation with Gazebo, deals with robot simulation. Here where we introduce ROS and develop a two-wheeled robot simulation that emulates both the physical aspects and the behavior of an actual robot. We explore the concept of the digital twin, a virtual robot that is the twin of a physical one. This is a fundamental part of developing robotic applications, as it cuts the costs associated with testing real hardware.
The digital twin allows us to speed up the development process and save testing with the physical robot for the advanced stages of development.

Part 3, Autonomous Navigation Using SLAM, is devoted to Robot Navigation, the most common task for mobile robots. State-of-the-Art algorithms and techniques are explained in a practical manner, first in simulation and then with a physical robot.

Part 4, Adaptive Robot Behavior Using Machine Learning, focuses on Deep Learning applied to Computer Vision and Reinforcement Learning, the most active fields in robot research and real-world robotic applications. By using this technology, a robot is able to transition from a pure automatism — where every possible behavior or answer is coded — to exhibit a flexible behavior machine, where the robot is capable of reacting in a smart way to environmental demands by learning from data. This data can be obtained from the robot’s previous experience or
gathered from the experience of similar robots.

To build a State-of-the-Art robot application, you will first need to master and then combine these four building blocks. The result will be what is commonly known as a smart robot. This is your task — this is your challenge.

You can already get your copy at Packt or Amazon.

I will be happy to answer the comments and questions of Medium readers interested in the topic.