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

Questions

  1. What are the essential concepts of reinforcement learning?

A) Robot actions and penalties
B) Neural networks and deep learning
C) States, actions, and rewards

  1. Why do you need to use neural networks in reinforcement learning?

A) Because the robot needs to use deep learning to recognize objects and obstacles.
B) Because the robot has to learn to associate states with the most effective actions.
C) We do not need neural networks in reinforcement learning; we apply different algorithms.

  1. How do you encourage the robot to achieve the goal of the task?

A) By giving it rewards when it performs good actions.
B) By giving it penalties when it performs bad actions.
C) By giving it rewards when it performs good actions, and penalties in the case of bad actions.

  1. Can you apply the reinforcement learning ROS package from this chapter to other robots?

A) Yes, because we have separated...