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Learning Robotics using Python

You're reading from   Learning Robotics using Python Design, simulate, program, and prototype an autonomous mobile robot using ROS, OpenCV, PCL, and Python

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788623315
Length 280 pages
Edition 2nd Edition
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Author (1):
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Lentin Joseph Lentin Joseph
Author Profile Icon Lentin Joseph
Lentin Joseph
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Table of Contents (12) Chapters Close

Preface 1. Getting Started with Robot Operating System FREE CHAPTER 2. Understanding the Basics of Differential Robots 3. Modeling the Differential Drive Robot 4. Simulating a Differential Drive Robot Using ROS 5. Designing ChefBot Hardware and Circuits 6. Interfacing Actuators and Sensors to the Robot Controller 7. Interfacing Vision Sensors with ROS 8. Building ChefBot Hardware and the Integration of Software 9. Designing a GUI for a Robot Using Qt and Python 10. Assessments 11. Other Books You May Enjoy

Chapter 4, Simulating a Differential Drive Robot Using ROS

  1. Sensor modeling in Gazebo can be done using Gazebo plugins. The sensor model can be written using C++, which can be plugged in to the Gazebo simulator.
  2. ROS is interfaced to Gazebo using Gazebo ROS plugin. When we load this plugin into Gazebo, we can able to control Gazebo through ROS interface.
  3. The important tags are <inertia>, <collision>, and <gazebo>.
  4. The Gmapping package in ROS is an implementation of Fast SLAM algorithm, which can be used in robot to map the environment and localizing on it. Using Gmapping in ROS is a straightforward process, including the gmapping node with necessary parameters and topics such as odometry and laser scan.
  5. The Move_base node has a provision to handle various navigation subsystem in a robot. It is having a provision to handle global and local planner, also the map...
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