• About the Course
  • Other Courses
• Getting Started
  • Prerequisites
• Usage
• Contributing
• License
• Contact
• Acknowledgements

This repository contains the material used in the course Self Driving and ROS - Learn by Doing! Odometry & Control that is currently available on the following platforms:

• Udemy

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, 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

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

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

Excited? Check it out:

• Udemy

In this course I'll guid 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
• MoveIt 2
• Using Arduino with ROS 2
• Interface Alexa with ROS 2

Looks funny? Check it out on the following platforms:

• Udemy

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, for this reason you can create and code your robotic arm both using ROS 1 and ROS 2:

• Udemy

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 opf the lessons and most of the code will work the same in the simulation as in the real robot

You don't need any prior knowledge of ROS nor of Self-Driving, I'll explain all the concepts as they came 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 nmot dwell too much on basic Programming concepts.

To prepare your PC you need:

• Install Ubuntu 20.04 on PC or in Virtual Machine Download the ISO Ubuntu 20.04 for your PC
• Install ROS Noetic on your Ubuntu 20.04
• Install ROS 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-noetic-ros-controllers \
     ros-noetic-gazebo-ros-control \
     ros-noetic-joint-state-publisher-gui \
     ros-noetic-joy \
     ros-noetic-joy-teleop \
     ros-noetic-turtlesim \
     ros-noetic-robot-localization \
     ros-noetic-actionlib-tools

To Launch the Simulation of the Robot

1. Clone the repo

git clone https://github.com/AntoBrandi/Self-Driving-and-ROS-Learn-by-Doing-Odometry-Control.git

2. Build the ROS workspace

cd ~/Self-Driving-and-ROS-Learn-by-Doing-Odometry-Control/Section11_Sensor-Fusion/bumperbot_ws
catkin_make

catkin_make

3. Source the ROS Workspace

source devel/setup.bash

4. Launch the Gazebo simulation

roslaunch bumperbot_description gazebo.launch

Contributions are what make the open source community such an amazing place to be 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

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

Antonio Brandi - LinkedIn - [email protected]

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

• Turtlebot 3 Software
• Turtlebot 3 Hardware