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

Configuring the environment file

In step 2 of the preceding algorithmic description of the start_training.py script, ROS parameters are loaded into the model. Their definitions come from this line of the start_training.launch file:

<rosparam command="load" file="$(find cartpole_dqn)/config/cartpole_dqn_params.yaml" />

When executing this part, the parameters in the cartpole_dqn_params.yaml file are loaded into memory and are available to the cartpole_dqn.py script. The more relevant are the following:

alpha = rospy.get_param('/cartpole_v0/alpha')
gamma = rospy.get_param('/cartpole_v0/gamma')
epsilon = rospy.get_param('/cartpole_v0/epsilon')

cartpole_v0 is the namespace that is declared before the definitions in the yaml file. The meaning of every parameter was covered in the Self driving cab example using RL algorithm subsection...