Robotics and ROS 2 - Learn by Doing! Manipulators

Table of Contents

• About the Course
• Other Courses
  • Self Driving and ROS 2 - Learn by Doing! Odometry & Control
  • Self Driving and ROS 2 - Learn by Doing! Map & Localization
• Getting Started
  • Prerequisites
• Installation
• Usage
• Contributing
• License
• Contact
• Acknowledgements

About the Course

This repository contain the material used in the course Robotics and ROS 2 - Learn by Doing! Manipulators that is currently available on the following platforms:

• Udemy

In this course, I'll guide you through the creation of a real robotic arm that you can control with your voice using the Amazon Alexa voice assistant. Some of the concepts that are covered in this course are

• Gazebo Simulation
• Robot Kinematics
• ROS 2 Basics
• ros2_control Library
• MoveIt! 2
• Using Arduino with ROS 2
• Interface Alexa with ROS 2

Furthermore, all the laboratory classes in which we are going to develop the actual Software of our mobile robot are available both in Python and in C++ to let you have the freedom of choosing the programming language you like the most or become proficient in both!

Other Courses

If you find this course interesting and you are passionate about robotics in general (not limited to manipulators), then you definitely have to take a look at my other courses!

Self Driving and ROS 2 - Learn by Doing! Odometry & Control

If you are passionate about Self-Driving and you want to make a real robot Autonomously Navigate, then this course is for you! Apart from explaining in detail all the functionalities and the logic of ROS 2, the latest version of the Robot Operating System, it covers some key concepts of Autonomous Navigation such as

• Sensor Fusion
• Kalman Filter
• Probability Theory
• Robot Kinematics
• Odometry
• Robot Localization
• Control

Looks funny? Check it out on the following platforms:

• Udemy

Self Driving and ROS 2 - Learn by Doing! Map & Localization

Have you ever developed a mapping and localization algorithm for your robot? Do you want to know more about SLAM (Simultaneous Localization and Mapping) and how to use it to enable your robot to create a nice and accurate map of the environment using a 2D LiDAR sensor?

Then this course will teach you exactly that, with many more topics:

• Robot Localization
• Map Representations
• Mapping
• SLAM
• Obstacle Avoidance
• Speed and Separation monitoring
• Using LiDAR Sensors

enroll on the following platforms:

• Udemy

Self Driving and ROS 2 - Learn by Doing! Plan & Navigation

Have you ever developed a path-planning algorithm for your robot? Do you want to know more about Motion Planning algorithms and how to use them to autonomously move your robot while avoiding obstacles using a Costmap of the environment?

Then this course will teach you exactly that, with many more topics:

• Path Planning
• Motion Planning
• Decision Making
• Behavior Trees
• Obstacle Avoidance
• Costmaps
• Nav2

Enroll on the following platforms:

• Udemy

ROS 1 Nostalgic?

Do you want to master Self-Driving or Manipulation using ROS, the first version of the Robot Operating System?

Despite many companies already starting to switch to ROS 2, knowing both ROS 1 and ROS 2 will position you at the forefront of this demand, making you an attractive candidate for a wide range of roles.

Here you can access the same courses, where will be created the same robots, implementing the same functionalities in ROS 1

• Self Driving and ROS - Learn by Doing! Odometry & Control

• Robotics and ROS - Learn by Doing! Manipulators

Getting Started

You can decide whether to build the real robot or just have fun with the simulated one. The course can be followed either way, most of the lessons and most of the code will work the same in the simulation as in the real robot

Prerequisites

You don't need any prior knowledge of ROS 2 or of Robotics, I'll explain all the concepts as they come out and as they are needed to implement new functionalities to our robot. A basic knowledge of programming, either using C++ or Python is required as this is not a Programming course and so I'll not dwell too much on basic Programming concepts.

To prepare your PC you need:

• Install Ubuntu 22.04 on PC or in Virtual Machine Download the ISO Ubuntu 22.04 for your PC
• Install ROS 2 Humble on your Ubuntu 22.04
• Install ROS 2 missing libraries. Some libraries that are used in this project are not in the standard ROS package. Install them with:

sudo apt-get update && sudo apt-get install -y \
     ros-humble-joint-state-publisher-gui \
     ros-humble-xacro \
     ros-humble-ros2-control \
     ros-humble-moveit* \
     ros-humble-ros2-controllers \
     ros-humble-ros-gz-* \
     ros-humble-*-ros2-control

• Install VS Code and Arduino IDE on your PC in order to build and load the Arduino code on the device
• Install Python and C++ additional libraries

sudo apt-get update && sudo apt-get install -y \
     libserial-dev \
     python3-pip

pip install pyserial

Installation

1. Clone the repo

git clone https://github.com/AntoBrandi/Robotics-and-ROS-2-Learn-by-Doing-Manipulators.git

1. Build the ROS 2 workspace

cd ~/Robotics-and-ROS-2-Learn-by-Doing-Manipulators/Section9-Build/arduinobot_ws

colcon build

1. Source the project

. install/setup.bash

Usage

To launch the ROS 2 Simulated robot

ros2 launch arduinobot_bringup simulated_robot.launch.py

To launch the Real robot, connect the Arduino to the PC and upload the code in the folder on the Arduino controller. Then launch the real robot

ros2 launch arduinobot_bringup real_robot.launch.py

To launch the interface with Alexa download ngrok and create an account then setup ngrok with your key

./ngrok authtoken <YOUR-KEY>

Then start the ngrok web server with

./ngrok http 5000

Copy the link that provides ngrok and paste it in the section Endpoint of your Alexa Developer account

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the Apache 2.0 License. See LICENSE for more information.

Contact

Antonio Brandi - LinkedIn - antonio.brandi@outlook.it

My Projects: https://github.com/AntoBrandi

Acknowledgements

• Course Cover Images
• Arduino 3D-Printed Robotic Arm
• EEZYbotARM