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Hands-On ROS for Robotics Programming

Transform your technical career by mastering the Robot Operating System training, the definitive skill set for the modern robotics engineer.

(ROBOTICS.AJ1) / ISBN : 979-8-90059-023-3
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About This Course

Are you ready to move beyond basic hobbyist scripts and truly leverage the power of industrial-grade middleware to revolutionize your robotics projects? The role of the developer in this field is fundamentally changing, demanding specialized knowledge in how to strategically integrate complex algorithms into physical systems. This hands-on ROS tutorial moves you past simple "Hello World" examples and dives deep into architecting, securing, and operationalizing robot intelligence across the entire development lifecycle.

You will master ROS2 fundamentals, learn professional URDF modeling for accurate robot representation, and explore Gazebo simulation to test your designs in physics-based virtual environments. Whether you are aiming for precise autonomous navigation, designing multi-robot systems, or implementing advanced SLAM navigation, this program provides the practical, hands-on knowledge to design and launch sophisticated robotics solutions from prototype to production. By focusing on Raspberry Pi robotics and the GoPiGo3 platform, we bridge the gap between simulation and the real world.

Skills You’ll Get

  • Hardware Foundations & ROS Core: Master the assembly of GoPiGo3 components and Raspberry Pi robotics integration while gaining a deep understanding of ROS architecture, nodes, topics, and services.
  • Modeling & Physics Simulation: Design complex robot structures using URDF modeling and validate their real-world behavior within a high-fidelity Gazebo simulation environment.
  • Autonomous Navigation & SLAM: Implement SLAM navigation and AMCL for precise localization, enabling your robot to perform autonomous navigation and path planning in dynamic environments.
  • Intelligent Control & Reinforcement Learning: Enhance robot capabilities by integrating computer vision and Reinforcement Learning for robotics, training agents to solve goal-driven tasks with OpenAI.
Download Course Outline

1

Preface

  • Who this course is for
  • What this course covers
2

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
3

Unit Testing of GoPiGo3

  • Technical requirements
  • Getting started with Python and JupyterLab
  • Unit testing of sensors and drives
  • Summary
4

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
5

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
6

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
7

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
8

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
9

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
10

SLAM for Robot Navigation

  • Technical requirements
  • Preparing an LDS for your robot
  • Creating a navigation application in ROS
  • Practicing navigation with GoPiGo3
  • Summary
11

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
12

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
13

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

1

Assembling the Robot

  • Configuring GoPiGo3 Hardware Interfaces for ROS Operation
2

Unit Testing of GoPiGo3

  • Running GoPiGo3 Sensor Unit Tests
3

Getting Started with ROS

4

Creating the Virtual Two-Wheeled ROS Robot

5

Simulating Robot Behavior with Gazebo

6

Programming in ROS - Commands and Tools

7

Robot Control and Simulation

8

Virtual SLAM and Navigation Using Gazebo

9

SLAM for Robot Navigation

10

Applying Machine Learning in Robotics

11

Machine Learning with OpenAI Gym

12

Achieve a Goal through Reinforcement Learning

Any questions?
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 This hands-on ROS tutorial is ideal for software developers, mechatronics engineers, and students looking to transition into professional robotics software development using industry-standard tools. 

While the course uses the GoPiGo3 and Raspberry Pi for real-world labs, we place a heavy emphasis on Gazebo simulation. This allows you to master autonomous navigation and URDF modeling entirely in a virtual environment if hardware is unavailable.

Yes, the program is built on ROS2 (Foxy/Humble), ensuring you learn the most modern, secure, and multi-robot capable version of the Robot Operating System training used by top global firms.

Beyond standard control, we explore Reinforcement Learning for robotics. You will learn to use OpenAI Gym with ROS to train robots for complex tasks, merging traditional SLAM navigation with modern machine learning.

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