README.md

Week 1-8: Cognitive robotics

This is how far we will get by the end of this lesson:

Table of Contents

1. ROS basics
2. Gazebo basics
3. Turtlebot3 simulation
4. Test on the real Turtlebot3
5. Turtlebot3 MOGI
6. Line following
7. Neural network
8. Test on the real robot

ROS basics

What is ROS(2)?

ROS, or Robot Operating System, is an open-source framework designed to facilitate the development of robotic applications. It provides a collection of tools, libraries, and conventions that simplify the process of designing complex robot behaviors across a wide variety of robotic platforms.

ROS was initially developed in 2007 by the Stanford Artificial Intelligence Laboratory and continued by Willow Garage, with the goal of providing a common platform for research and development in robotics. The primary motivation was to create a standard framework that could support a broad range of robotic applications, promote code reuse, and foster collaboration within the robotics community.

Key reasons for ROS development include:

• Standardization: Creating a common platform that simplifies the integration of different hardware and software components.
• Modularity: Enabling the development of modular and reusable software components (nodes) that can be easily shared and adapted for various robotic systems.
• Community Collaboration: Encouraging collaboration among researchers and developers, resulting in a vast collection of tools and libraries.

Transition to ROS 2

ROS 2 was developed to address the limitations of ROS 1 and meet the growing demands for industrial and commercial robotics applications. The development began around 2014 and aimed to enhance the capabilities of ROS, particularly in areas such as security, real-time performance, and support for multi-robot systems. In practice, the biggest difference is in the underlying middleware, ROS1 uses a custom transport layer and message-passing system that was not designed for real-time or distributed applications (see ROS1's roscore).

The latest ROS1 release is ROS Noetic which was intended to be used on Ubuntu 20.04. It goes to EOL in May, 2025 together with Ubuntu 20.04.

Required softwares

Ubuntu 24.04 LTS

In the course we'll use ROS2 Jazzy Jalisco, which requires Ubuntu 24.04 for the smoothest operation.

You have a couple of options, but the most recommended is the native installation of the operating system - external SSD, dual boot, etc.

1. Native install, the most recommended way. You will learn how to set up the environment, there won't be difficulties with GPU acceleration and many more advantages.
2. Windows 11 WSL2 (Windows Subsystem Linux), see instructions. It's a straightforward way if you want to use within Windows environment. You can still learn how to set up the environment but can be more challenging with GUI applications and 3D acceleration.
3. Virtual machine, VMware fusion is now free for personal use. Less flexible than WSL2 on Windows 11 but works well on macOS. 3D acceleration can be challenging.
4. Docker container. First it might look an easy way to use any ROS distribution on any host operating system, but it's getting more and more challenging if we need GUI applications, 3D acceleration and can be confusing for beginners how to work within the container. You might miss some important experience with setting up the environment if using a pre-configured container image. I only recommend this way for experienced Docker users.
5. Using an online environment e.g. The Construct. It looks promising that you don't have to install any special software, but you won't gain experience with setting up the environment. It can be difficult to cherry pick the software versions you need and accessing GUI applications through the web interface is a poor experience.

The options 1 and 2 are the most practical and preferred ways to use ROS. In an exotic case, if you want to run Ubuntu 24.04 and ROS2 Jazzy on macOS and Apple silicon this is a very good tutorial.

Pro tip if you want to mount directories from your host system into your guest Ubuntu 24.04 running in VMware fusion, more details on this link:

/usr/bin/vmhgfs-fuse .host:/BME/ROS2-lessons /home/david/ros2_ws/src/ROS2-lessons -o subtype=vmhgfs-fuse,allow_other

Visual Studio Code

The recommended code editor during the course is Visual Studio Code, but it's up to your choice if you want to go with your different editor. Depending on your Ubuntu install method you might install it natively on Ubuntu, in your virtual environment or on your host operating system.

Recommended extensions to install:

• Markdown All in One
• C/C++
• Python
• CMake Tools
• Remote - SSH - if you work on physical robots, too
• Remote - WSL - if you do the course using WSL2

GitHub and a git client

The course materials are available on GitHub, and the submissions of your final projects shall also use GitHub. You'll need a very good excuse why to use alternative git solutions like GitLab.

So I encourage everyone to register your GitHub accounts, and if you are there don't forget to sign up for the GitHub Student Developer Pack which gives you a bunch of powerful developer tools for free.

I recommend to use a graphical git client that can boost your experience with git, in my optinion the best one is GitKraken, which is not a free software, but you get the pro version as part of the GitHub Student Developr Pack! If you prefer using git as a cli tool then no worries, it's absoluetely all right.

Markdown

Markdown is not a standalone software but rather a lightweight, plain-text formatting language used to create formatted documents. It was created by John Gruber in 2004 with the goal of being easy to read and write, using simple syntax to style text, create lists, links, images, and more. It is widely used for writing documentation, readme files, and content for static websites.

Basic Markdown Syntax

• Headings: # Heading 1, ## Heading 2, etc.
• Bold: **bold text** or __bold text__
• Italic: *italic text* or _italic text_
• Lists:
  • Unordered: - Item or * Item
  • Ordered: 1. Item
  • Links: [Link text](URL)
  • Images: ![Alt text](Image URL)
  • Code: Inline code or code blocks using triple backticks (```)

GitHub Flavored Markdown (GFM)

GitHub Flavored Markdown (GFM) is a variant of Markdown used by GitHub to provide additional features and syntax that are not available in standard Markdown. It includes:

• Tables:

  | Column 1 | Column 2 |
  |----------|----------|
  | Row 1    | Data     |
  | Row 2    | Data     |
  

• Task lists:

  - [x] Task 1
  - [ ] Task 2
  

• Strikethrough: ~~strikethrough text~~
• Syntax highlighting in a specific language:

  ```python
  def hello_world():
  print("Hello, world!")
  

• Tables of Contents
• @mentions for users, references to issues, and pull requests using #number

Most of the tips and tricks that you might need for your own project documentation can be found in the source of this readme that you read right now, feel free to use any snippets from it!

A good terminal

It's up to your choice which terminal tool would you like to use, but I strongly recommend one that support multiple split windows in a single unified window, because we will use a lot of terminals! On Linux, I can recommend terminator:

In case you use WSL2, the built-in Windows terminal also support multiple panes and works really well!

And finally, install ROS2 Jazzy

ROS always had very good and detailed installed guides, it's not anything different for ROS2's Jazzy release. The installation steps can be found here, with Ubuntu 24.04 it can be installed simply through pre-built, binary deb packages.

After installing it we have to set up our ROS2 environment with the following command:

source /opt/ros/jazzy/setup.bash

By default, we have to run this command in every new shell session we start, but there is a powerful tool in Linux for such use cases. .bashrc file is always in the user's home directory and it is used for user-specific settings for our shell sessions. You can edit .bashrc directly in a terminal window with a basic text editor, like nano:

david@david-ubuntu24:~$ nano .bashrc

Here, you can add your custom user-specific settings in the end of the file, that will be executed every time you initiate a new shell session. I created an example gist that you can add to the end of your file and use it during the course.

ROS2 Jazzy has an even more detailed tutorial about setting up your environment, you can check it out, too!

Running some examples

Your ROS2 install comes with a couple of good examples as you can also find it on the install page.

Let's try them!

The following command starts a simple publisher written in C++. A publisher is a node that is responsible for sending messages with a certain type over a specific topic (in this example the topic's name is chatter and the type is a string). A topic is a communication pipeline in the publish-subscribe communication model where a single message is sent to multiple subscribers, unlike message-queues that are point-to-point models, where a single message is sent to a single consumer. Publishers broadcast messages to topics, and subscribers listen to those topics to receive a copy of the message.

Publish-subscribe models are asynchronous, one-to-many or many-to-many interactions where the publishers don't know how many subscribers there are (if any). Therefore publisher never expects any response or confirmation from the subscribers.

Now, let's run the demo publisher written in C++:

ros2 run demo_nodes_cpp talker

Your output should look like this:

david@david-ubuntu24:~$ ros2 run demo_nodes_cpp talker
[INFO] [1727116062.558281395] [talker]: Publishing: 'Hello World: 1'
[INFO] [1727116063.558177802] [talker]: Publishing: 'Hello World: 2'
[INFO] [1727116064.558010534] [talker]: Publishing: 'Hello World: 3'
[INFO] [1727116065.557939861] [talker]: Publishing: 'Hello World: 4'
[INFO] [1727116066.557849645] [talker]: Publishing: 'Hello World: 5'

Let's start a subscriber - written in Python - in another terminal window, which subscribes to the chatter topic and listens to the publisher node's messages:

david@david-ubuntu24:~$ ros2 run demo_nodes_py listener
[INFO] [1727116231.574662048] [listener]: I heard: [Hello World: 170]
[INFO] [1727116232.560517676] [listener]: I heard: [Hello World: 171]
[INFO] [1727116233.558907367] [listener]: I heard: [Hello World: 172]
[INFO] [1727116234.560768278] [listener]: I heard: [Hello World: 173]
[INFO] [1727116235.559821377] [listener]: I heard: [Hello World: 174]
[INFO] [1727116236.559993767] [listener]: I heard: [Hello World: 175]

Useful cli and graphical tools

Now both nodes are running we can try a few useful tools. The first on let us know what kind of nodes are running in your ROS2 system:

ros2 node list

Which gives us the following output:

david@david-ubuntu24:~/ros2_ws$ ros2 node list
/listener
/talker

If we want to know more about one of our nodes, we can use the ros2 node info /node command:

david@david-ubuntu24:~/ros2_ws$ ros2 node info /listener 
/listener
  Subscribers:
    /chatter: std_msgs/msg/String
  Publishers:
    /parameter_events: rcl_interfaces/msg/ParameterEvent
    /rosout: rcl_interfaces/msg/Log
  Service Servers:
    /listener/describe_parameters: rcl_interfaces/srv/DescribeParameters
    /listener/get_parameter_types: rcl_interfaces/srv/GetParameterTypes
    /listener/get_parameters: rcl_interfaces/srv/GetParameters
    /listener/get_type_description: type_description_interfaces/srv/GetTypeDescription
    /listener/list_parameters: rcl_interfaces/srv/ListParameters
    /listener/set_parameters: rcl_interfaces/srv/SetParameters
    /listener/set_parameters_atomically: rcl_interfaces/srv/SetParametersAtomically
  Service Clients:

  Action Servers:

  Action Clients:

At the moment, the most interesting detail we can gather about a node is if it's subscribing or publishing to any topic. In later lessons we'll learn more about parameters and services.

---

In a very similar way, we can also list all of our topics with ros2 topic list command:

david@david-ubuntu24:~/ros2_ws$ ros2 topic list 
/chatter
/parameter_events
/rosout

And we can get more details about a certain topic with the ros2 topic info /topic command:

david@david-ubuntu24:~/ros2_ws$ ros2 topic info /chatter 
Type: std_msgs/msg/String
Publisher count: 1
Subscription count: 1

Another powerful tool is rqt_graph that helps us visualizing the nodes and topics in a graph.

rqt_graph can be used as a standalone tool, or part of rqt which can be used to build a complete dashboard to mintor and control your nodes. We'll spend a lot of time with it, at the moment let's just see the message monitoring function:

---

Let's run more examples: turtlesim

Let's see another built in example which is a simple 2D plotter game.

In a case it's not automatically installed, you can install it with the following command:

sudo apt install ros-jazzy-turtlesim

To run the main node just execute the follwoing command:

ros2 run turtlesim turtlesim_node

And in another terminal start its remote controller, you can simply drive the turtle with the arrows:

ros2 run turtlesim turtle_teleop_key

We can use the same tools as before to see the running nodes and topics, here is how does it look like in rqt_graph.

We should notice two important things:

1. turtlesim is more complex than the previous example with multiple services and parameters that we'll check in the end of this lesson.
2. the turtle is controlled with a cmd_vel message which is a 6D vector in space. We'll use this exact same message type in the future to drive our simulated robots.

Now let's move on to create our own nodes!

Create a colcon workspace

To create, build and run custom nodes we need packages, but first we need a workspace where we'll maintain our future packages. There are 2 new terms we must learn about ROS2 workspaces:

1. ament provides the underlying build system and tools specifically for ROS2 packages. ament_cmake is a CMake-based build system for C/C++ nodes and ament_python provides the tools for packing and installing python nodes and libraries.
2. colcon (COmmand Line COLlectioN) is a general-purpose tool to build and manage entire workspaces with various build systems, including ament, cmake, make, and more.

It means that our ROS2 workspace will be a colcon workspace which - in the backround - will use ament for building the individual packages.

If you have experience with ROS1, colcon and ament replaces the old catkin tools.

Let's create our workspace inside our user's home directory:

mkdir -p ~/ros2_ws/src
cd ~/ros2_ws

A workspace must have a src folder where we maintain the source files of our packages, during building of the workspace colcon will create folders for the deployment of binaries and other output files.

Let's go into the src folder and create our first python package:

ros2 pkg create --build-type ament_python bme_ros2_tutorials_py

During package creation we should define if it's a C/C++ (ament_cmake) or a python (ament_python) package. If we don't do it, the default is always ament_cmake!

We'll put our python scripts under bme_ros2_tutorials_py which is an automatically created folder with the same name as our package, it already has an empty file __init__.py, let's add our first node here: hello_world.py.

We can create files in Linux in several different ways, just a few examples:

• Right click in the folder using the desktop environment
• Through the development environment, in our case Visual Studio Code
• From command line in the current folder using the touch command: touch hello_world.py

At this point our workspace should look like this (other files and folders are not important at this point):

david@david-ubuntu24:~/ros2_ws$ tree -L 4
.
└── src
    └── bme_ros2_tutorials_py
        ├── bme_ros2_tutorials_py
        │   ├── __init__.py
        │   └── hello_world.py
        ├── package.xml
        └── setup.py

It's always recommended to fill the description, maintainer with your name and email address and license fields in your package.xml and setup.py files. I personally prefer a highly permissive license in non-commercial packages of mine, like BSD or Apache License 2.0.

Let's write the simplest possible hello_world in python:

#!/usr/bin/env python3

# Main entry point, args is a parameter that is used to pass arguments to the main function
def main(args=None):
    print("Hello, world!")

# Check if the script is being run directly
if __name__ == '__main__':
    main()

Although this is a python script that doesn't require any compilation, we have to make sure that ament will pack, copy and install our node. It's important to understand that we are not running python scripts directly from the source folder!

Let's edit setup.py that was automatically generated when we defined that our package will use ament_python.

Add an entry point for our python node. An entry point describes the folder, the filename (without .py) and the main entry point within the script:

...
        entry_points={
            'console_scripts': [
                'py_hello_world = bme_ros2_tutorials_py.hello_world:main'
            ],
        },
...

Our first node within our first package is ready for building it! Build must be initiated always in the root of our workspace!

cd ~/ros2_ws

And here we execute the colcon build command.

After a successful build we have to update our environnment to make sure ROS2 cli tools are aware about of any new packages. To do this we have to run the following command:

source install/setup.bash

As we did with the base ROS2 environment, we can add this to the .bashrc so it'll be automatically sourced every time when we open a terminal:

source ~/ros2_ws/install/setup.bash

And now we are ready to run our first node:

ros2 run bme_ros2_tutorials_py py_hello_world

Athough we could run our first node, it was just a plain python script, not using any ROS API. Let's upgrade hello world to a more ROS-like hello world. We import the rclpy which is the ROS2 python API and we start using the most basic functions of rclpy like init(), create_node() and shutdown(). If you already want to do a deep-dive in the API functions you can find everything here.

#!/usr/bin/env python3
import rclpy # Import ROS2 python interface

# Main entry point, args is a parameter that is used to pass arguments to the main function
def main(args=None):
    rclpy.init(args=args)                          # Initialize the ROS2 python interface
    node = rclpy.create_node('python_hello_world') # Node constructor, give it a name
    node.get_logger().info("Hello, ROS2!")         # Use the ROS2 node's built in logger
    node.destroy_node()                            # Node destructor
    rclpy.shutdown()                               # Shut the ROS2 python interface down

# Check if the script is being run directly
if __name__ == '__main__':
    main()

We don't have to do anything with setup.py, the entrypoint is already there, but we have to re-build the colcon workspace!

After the build we can run our node:

ros2 run bme_ros2_tutorials_py py_hello_world

Create a python publisher

Let's make our first publisher in python, we create a new file in the bme_ros2_tutorials_py folder: publisher.py.

We start expanding step-by-step our knowledge about the ROS2 API with publishing related functions (create_publisher() and publish()).

#!/usr/bin/env python3
import rclpy
from std_msgs.msg import String # Import 'String' from ROS2 standard messages
import time

def main(args=None):
    rclpy.init(args=args)
    node = rclpy.create_node('python_publisher')
    # Register the node as publisher
    # It will publish 'String' type to the topic named 'topic' (with a queue size of 10)
    publisher = node.create_publisher(String, 'topic', 10)

    msg = String()                     # Initialize msg as a 'String' instance
    i = 0
    while rclpy.ok():                  # Breaks the loop on ctrl+c
        msg.data = f'Hello, world: {i}' # Write the actual string into msg's data field
        i += 1
        node.get_logger().info(f'Publishing: "{msg.data}"')
        publisher.publish(msg)         # Let the node publish the msg according to the publisher setup
        time.sleep(0.5)                # Python wait function in seconds

    node.destroy_node()
    rclpy.shutdown()

if __name__ == '__main__':
    main()

We have to edit setup.py, registering our new node as entry point:

...
    entry_points={
        'console_scripts': [
            'py_hello_world = bme_ros2_tutorials_py.hello_world:main',
            'py_publisher = bme_ros2_tutorials_py.publisher:main'
        ],
    },
...

Don't forget to rebuild the workspace and we can run our new node:

david@david-ubuntu24:~$ ros2 run bme_ros2_tutorials_py py_publisher
[INFO] [1727526317.470055907] [python_publisher]: Publishing: "Hello, world: 0"
[INFO] [1727526317.971461827] [python_publisher]: Publishing: "Hello, world: 1"
[INFO] [1727526318.473896872] [python_publisher]: Publishing: "Hello, world: 2"
[INFO] [1727526318.977439178] [python_publisher]: Publishing: "Hello, world: 3"

We can observe the published topic through rqt's topic monitor:

Or we can use a simple but powerful tool, the topic echo:

david@david-ubuntu24:~$ ros2 topic echo /topic 
data: 'Hello, world: 23'
---
data: 'Hello, world: 24'
---
data: 'Hello, world: 25'

The publisher node above is very simple and looks exactly how we historically impelented nodes in ROS1. But ROS2 provides more powerful API functions and also places a greater emphasis on object-oriented programming. So let's create another publisher in a more OOP way and using the timer functions (create_timer()) of the ROS2 API. The other important API function is rclpy.spin(node) which keeps the node running until we don't quit it with ctrl+c in the terminal.

#!/usr/bin/env python3
import rclpy
from rclpy.node import Node     # Import ROS2 Node as parent for our own node class
from std_msgs.msg import String

class MyPublisherNode(Node):
    def __init__(self):
        super().__init__("python_publisher_oop")
        self.publisher_ = self.create_publisher(String, 'topic', 10)
        self.timer = self.create_timer(0.5, self.timer_callback)     # Timer callback, period in seconds, not frequency!
        self.i = 0
        self.msg = String()
        self.get_logger().info("Publisher OOP has been started.")

    def timer_callback(self):                                        # Timer callback function implementation
        self.msg.data = f"Hello, world: {self.i}"
        self.i += 1
        self.get_logger().info(f'Publishing: "{self.msg.data}"')
        self.publisher_.publish(self.msg)

def main(args=None):
    rclpy.init(args=args)
    node = MyPublisherNode() # node is now a custom class based on ROS2 Node
    rclpy.spin(node)         # Keeps the node running until it's closed with ctrl+c
    node.destroy_node()
    rclpy.shutdown()

if __name__ == "__main__":
    main()

As we did previously, add the new script's entrypoint in the setup.py, build the workspace and run our new node:

ros2 run bme_ros2_tutorials_py py_publisher_oop

Create a python subscriber

Let's create a new file subscriber.py in our python package (bme_ros2_tutorials_py). First we make a very simple implementation and after that we'll implement a more OOP version of it again. We further extend our knowledge with more API functions related to subscriptions (create_subscription()).

#!/usr/bin/env python3
import rclpy
from std_msgs.msg import String


def main(args=None):
    rclpy.init(args=args)
    node = rclpy.create_node('python_subscriber')

    def subscriber_callback(msg):                      # Subscriber callback will be invoked every time when a message arrives to the topic it has subsctibed
        node.get_logger().info(f"I heard: {msg.data}")

    # Register the node as a subscriber on a certain topic: 'topic' (with a certain data type: String)
    # and assign the callback function that will be invoked when a message arrives to the topic
    # with a queue size of 10 which determines how many incoming messages can be held in the subscriber’s
    # queue while waiting to be processed by the callback function
    subscriber = node.create_subscription(String, 'topic', subscriber_callback, 10) 
    node.get_logger().info("Subsciber has been started.")

    rclpy.spin(node)

    node.destroy_node()
    rclpy.shutdown()


if __name__ == '__main__':
    main()

Add the node to the setup.py file as a new entry point

...
    entry_points={
        'console_scripts': [
            'py_hello_world = bme_ros2_tutorials_py.hello_world:main',
            'py_publisher = bme_ros2_tutorials_py.publisher:main',
            'py_publisher_oop = bme_ros2_tutorials_py.publisher_oop:main',
            'py_subscriber = bme_ros2_tutorials_py.subscriber:main'
        ],
    },
...

Then, build the workspace and we can run our new node!

david@david-ubuntu24:~$ ros2 run bme_ros2_tutorials_py py_subscriber
[INFO] [1727606328.416973729] [python_subscriber]: Subsciber has been started.

If we don't start a publisher, then our subscriber is just keep listening to the /topic but the callback function is not invoked. The node doesn't stop running because of the rclpy.spin(node) function.

Let's start our C++ publisher in another terminal:

david@david-ubuntu24:~$ ros2 run bme_ros2_tutorials_cpp publisher_cpp 
[INFO] [1727606744.184678739] [cpp_publisher]: CPP publisher has been started.
[INFO] [1727606744.685934650] [cpp_publisher]: Publishing: 'Hello, world: 0'
[INFO] [1727606745.185073828] [cpp_publisher]: Publishing: 'Hello, world: 1'
[INFO] [1727606745.686288921] [cpp_publisher]: Publishing: 'Hello, world: 2'
[INFO] [1727606746.186169881] [cpp_publisher]: Publishing: 'Hello, world: 3'

And let's see what happens with the subscriber! It's subscription callback function is invoked every time when the publisher sends a message onto the /topic.

david@david-ubuntu24:~/ros2_ws$ ros2 run bme_ros2_tutorials_py py_subscriber
[INFO] [1727606614.099180007] [python_subscriber]: Subsciber has been started.
[INFO] [1727606744.695260304] [python_subscriber]: I heard: Hello, world: 0
[INFO] [1727606745.187956805] [python_subscriber]: I heard: Hello, world: 1
[INFO] [1727606745.689289484] [python_subscriber]: I heard: Hello, world: 2
[INFO] [1727606746.188467429] [python_subscriber]: I heard: Hello, world: 3

We can also check it with rqt_graph:

And we can also observe the language agnostic approach of ROS2, without any additional effort this middleware provides interfacing between nodes written in different programming languages.

As before, let's make our subscriber more OOP using our previous template from the publisher. Compared to the publisher we just need to replace the timer callback with a subscription callback and that's all!

#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
from std_msgs.msg import String

class MySubscriberNode(Node):
    def __init__(self):
        super().__init__("python_subsciber_oop")
        self.subscriber_ = self.create_subscription(String, 'topic', self.subscriber_callback, 10)
        self.get_logger().info("Subsciber OOP has been started.")

    def subscriber_callback(self, msg):
        self.get_logger().info(f"I heard: {msg.data}")

def main(args=None):
    rclpy.init(args=args)
    node = MySubscriberNode()
    rclpy.spin(node)
    node.destroy_node()
    rclpy.shutdown()

if __name__ == "__main__":
    main()

Build the workspace and run the node.

Launchfiles

As you noticed with the previous examples we have to use as many terminals as many nodes we start. With a simple publisher and subscriber this isn't really a big deal, but in more complex robotic projects, it's quite common to use ROS nodes in the range of tens or even hundreds. Therefore ROS provides an efficient interface to start multiple nodes together and even re-map their topics to different ones or change its parameters instead of changing the source code itself.

Compared to ROS1 it's a bit more complicated to bundle these launchfiles with our nodes, so as a best practice, I recommend creating an individual pakage only for our launcfiles.

Let's create a new package with ament_cmake or simply without specifying the build type (by default it's ament_cmake).

ros2 pkg create bme_ros2_tutorials_bringup

Now let's create a launch folder within this new package. We can freely delete include and src folders:

If you want to delete a folder from command line that is not empty you can use the rm -rf folder command

rm -rf include/ src/

Add the following to the CMakeLists.txt to install the content of launch when we build the workspce:

install(DIRECTORY
  launch
  DESTINATION share/${PROJECT_NAME}
)

Create a new launch file, and let's call it publisher_subscriber.launch.py. In ROS2 the launchfiles are special declarative python scripts (with some imperative flavours) instead of the xml files we used in ROS1! Actually ROS2 also has the possibility to use xml based launch files, but the general usage and the documentation of this feature is very poor. Initially the python based launch system was intended to be the backend of xml launchfiles but it wasn't ready for the initial launch of ROS2 and the community rather jumped on using the python launch system.

touch publisher_subscriber.launch.py

Let's create our template that we can re-use in the future with only one publisher first. When we add a node to the launch file we must define the the package, the node (executable) and a freely chosen name.

#!/usr/bin/env python3
from launch import LaunchDescription
from launch_ros.actions import Node

def generate_launch_description():
    ld = LaunchDescription()

    publisher_node = Node(
        package="bme_ros2_tutorials_py",
        executable="py_publisher",
        name="my_publisher"
    )

    ld.add_action(publisher_node)
    return ld

Build and don't forget to source the workspace because we added a new package!

After it we can execute our launchfile with the ros2 launch command:

david@david-ubuntu24:~$ ros2 launch bme_ros2_tutorials_bringup publisher_subscriber.launch.py
[INFO] [launch]: All log files can be found below /home/david/.ros/log/2024-09-29-14-17-29-864407-david-ubuntu24-41228
[INFO] [launch]: Default logging verbosity is set to INFO
[INFO] [py_publisher-1]: process started with pid [41231]
[py_publisher-1] [INFO] [1727612250.056173684] [my_publisher]: Publishing: "Hello, world: 0"
[py_publisher-1] [INFO] [1727612250.559170990] [my_publisher]: Publishing: "Hello, world: 1"
[py_publisher-1] [INFO] [1727612251.061618736] [my_publisher]: Publishing: "Hello, world: 2"

If we don't write the launch word explicitly in the filename of our launch file, the ros2 launch cli tool won't be able to autocomplete the filenames.

We can notice that our node is now called my_publisher instead of python_publisher as we coded in the node itself earlier. With the launch files we can easily rename our nodes for better handling and organizing as our application scales up.

We can use the node list tool to list our nodes and the output will look like this:

david@david-ubuntu24:~/ros2_ws$ ros2 node list 
/my_publisher

Now let's add the subscriber too:

Every time when we add a node to the launch file we also have to register it with the ld.add_action() function:

from launch import LaunchDescription
from launch_ros.actions import Node

def generate_launch_description():
    ld = LaunchDescription()

    publisher_node = Node(
        package="bme_ros2_tutorials_py",
        executable="py_publisher",
        name="my_publisher",
    )

    subscriber_node = Node(
        package="bme_ros2_tutorials_py",
        executable="py_subscriber",
        name="my_subscriber",
    )

    ld.add_action(publisher_node)
    ld.add_action(subscriber_node)
    return ld

Don't forget to rebuild the workspace so the changed launchfile will be installed, after that we can run it!

david@david-ubuntu24:~$ ros2 launch bme_ros2_tutorials_bringup publisher_subscriber.launch.py
[INFO] [launch]: All log files can be found below /home/david/.ros/log/2024-09-29-14-20-15-603371-david-ubuntu24-41380
[INFO] [launch]: Default logging verbosity is set to INFO
[INFO] [py_publisher-1]: process started with pid [41383]
[INFO] [py_subscriber-2]: process started with pid [41384]
[py_publisher-1] [INFO] [1727612415.811451529] [my_publisher]: Publishing: "Hello, world: 0"
[py_subscriber-2] [INFO] [1727612415.811459737] [my_subscriber]: Subsciber has been started.
[py_subscriber-2] [INFO] [1727612415.811878677] [my_subscriber]: I heard: Hello, world: 0
[py_publisher-1] [INFO] [1727612416.313222973] [my_publisher]: Publishing: "Hello, world: 1"
[py_subscriber-2] [INFO] [1727612416.315340170] [my_subscriber]: I heard: Hello, world: 1

We can see that both nodes started and their logging to the standard output is combined in this single terminal window.

We can verify this with node list or using rqt_graph visually:

david@david-ubuntu24:~$ ros2 node list 
/my_publisher
/my_subscriber

We can also verify the used topics with the topic list tool:

david@david-ubuntu24:~$ ros2 topic list 
/parameter_events
/rosout
/topic

Gazebo basics

Gazebo is a powerful robotics simulation tool that provides a 3D environment for simulating robots, sensors, and objects. It is widely used in the ROS ecosystem for testing and developing robotics algorithms in a realistic virtual environment before deploying them to real hardware.

Gazebo integrates tightly with ROS, enabling simulation and control of robots using ROS topics, services, and actions. In ROS2 with the latest Gazebo releases the integration is facilitated by ros_gz.

Key Features of Gazebo:

• 3D Physics Engine: Simulates rigid body dynamics, collision detection, and other physics phenomena using engines like ODE, Bullet, and DART.
• Realistic Sensors: Simulates cameras, LiDAR, IMUs, GPS, and other sensors with configurable parameters.
• Plugins: Extensible via plugins to control robots, customize physics, or add functionality.
• Worlds and Models: Enables users to create complex environments with pre-built or custom objects and robots.

Besides Gazebo, there are many alternative simulation environments for ROS, but usually the setup of these simulators are more complicated and less documented. Certain simulators also have very high requirements for the GPU.

Simulator	Best For	Advantages	Disadvantages
Gazebo	General robotics simulation in ROS	Free, accurate physics, ROS support	Moderate visuals, resource-heavy
Unity	High-fidelity visuals and AI/ML tasks	Realistic graphics, AI tools	Steep learning curve, not robotics-specific
Webots	Beginner-friendly robotics simulation	Easy setup, cross-platform	Limited graphics, less customizable
Isaac Sim	High-end AI and robotics simulation	High-fidelity physics, AI support	GPU-intensive, complex setup

Install Gazebo

Before we install Gazebo we have to understand the compatibility between Gazebo versions and ROS distributions.

ROS Distribution	Gazebo Citadel (LTS)	Gazebo Fortress (LTS)	Gazebo Garden	Gazebo Harmonic (LTS)	Gazebo Ionic
ROS 2 Rolling	❌	❌	⚡	⚡	✅
ROS 2 Jazzy (LTS)	❌	❌	⚡	✅	❌
ROS 2 Iron	❌	✅	⚡	⚡	❌
ROS 2 Humble (LTS)	❌	✅	⚡	⚡	❌
ROS 2 Foxy (LTS)	✅	❌	❌	❌	❌
ROS 1 Noetic (LTS)	✅	⚡	❌	❌	❌

Since we use the latest LTS ROS2 distribution, Jazzy, we need Gazebo Harmonic.

To install Gazebo Harmonic binaries on Ubuntu 24.04 simply follow the steps on this link.

Once it's installed we can try it with the following command:

gz sim shapes.sdf

If everything works well you should see the following screen:

If you have a problem with opening this example shapes.sdf there might be various reasons that requires some debugging skills with Gazebo and Linux.

• If you see a Segmentation fault (Address not mapped to object [(nil)]) due to problems with Qt you can try to set the following environmental variable to force Qt to use X11 instead of Wayland. Link

  export QT_QPA_PLATFORM=xcb

• If you run Gazebo in WSL2 or virtual machine the most common problem is with the 3D acceleration with the OGRE2 rendering engine of Gazebo. You can either try disabling HW acceleration (not recommended) or you can switch the older OGRE rendering engine with the following arguments. Link

  gz sim shapes.sdf --render-engine ogre

• If you run Ubuntu natively on a machine with an integrated Intel GPU and a discrete GPU you can check this troubleshooting guide.

After Gazebo successfully starts we can install the Gazebo ROS integration with the following command:

sudo apt install ros-jazzy-ros-gz

You can find the official install guide here.

Run Gazebo examples

Let's start again the gz sim shapes.sdf example again and let's see what is important on the Gazebo GUI:

1. Blue - Start and pause the simulation. By default Gazebo starts the simulation paused but if you add the -r when you start Gazebo it automatically starts the simulation.
2. Cyan - The display shows the real time factor. It should be always close to 100%, if it drops seriously (below 60-70%) it's recommended to change the simulation step size. We'll see this later.
3. Red - You can add basic shapes or lights here and you can move and rotate them.
4. Pink - The model hierarchy, every item in the simulation is shown here, you can check the links (children) of the model, their collision, inertia, etc.
5. Green - Detailed information of the selected model in 4. some parameters can be changed most of them are read only.
6. Plug-in browser, we'll open useful tools like Resource Spawner, Visualize Lidar, Image Display, etc.

Gazebo has an online model database available here, you can browse and download models from here. Normally this online model library is accessible within Gazebo although there might be issues in WSL2 or in virtual machines, so I prepared an offline model library with some basic models.

You can download this offline model library from Google Drive.

After download unzip it and place it in the home folder of your user. To let Gazebo know about the offline model library we have to set the GZ_SIM_RESOURCE_PATH environmental variable, the best is to add it to the .bashrc:

export GZ_SIM_RESOURCE_PATH=~/gazebo_models

After setting up the offline model library let's open the empty.sdf in Gazebo and add a few models through the Resource Spawner within the plug-in browser:

Turtlebot3 simulation

In this lesson we'll use the simulated and the real Turtlebot3 robot in burger configuration. Turtlebot3 is not supported anymore with the latest ROS2 and Gazebo distributions, but we maintain our own packages to ensure compatibility.

Let's download the following GitHub repositories with the right branch (using the -b branch flag) to our colcon workspace:

git clone -b ros2 https://github.com/MOGI-ROS/turtlebot3_msgs
git clone -b mogi-ros2 https://github.com/MOGI-ROS/turtlebot3
git clone -b new_gazebo https://github.com/MOGI-ROS/turtlebot3_simulations

We'll need to install a couple of other dependencies with apt:

sudo apt install ros-jazzy-dynamixel-sdk
sudo apt install ros-jazzy-hardware-interface
sudo apt install ros-jazzy-nav2-msgs
sudo apt install ros-jazzy-nav2-costmap-2d
sudo apt install ros-jazzy-nav2-map-server
sudo apt install ros-jazzy-nav2-bt-navigator
sudo apt install ros-jazzy-nav2-bringup
sudo apt install ros-jazzy-interactive-marker-twist-server
sudo apt install ros-jazzy-cartographer-ros
sudo apt install ros-jazzy-slam-toolbox

If for some reasons you want to install the Dynamixel SDK from source you can download the following branch from GitHub:

git clone -b humble-devel https://github.com/MOGI-ROS/DynamixelSDK/

and if your Dynamixel SDK runs into a problem with module em, uninstall existing em and install this version as it's reported in this GitHub issue. You might also need to install the module lark:

pip install empy==3.3.4
pip install lark

Before we can test the Turtlebot3 packages we have to set up TURTLEBOT3_MODEL environmental variable:

export TURTLEBOT3_MODEL=burger

It's only valid for that terminal session where you set it up, so it's recommended to add it into your .bashrc file so every time when you open a new terminal, it will be executed. You can use the following gist as an example how to set up the .bashrc file.

After building the workspace and sourcing the setup.bash file we can test the simulation of the Turtlebot3 burger:

ros2 launch turtlebot3_gazebo empty_world.launch.py

If we start a keyboard teleop node we can already drive the robot in the simulation:

ros2 run teleop_twist_keyboard teleop_twist_keyboard 

Or there is another example world:

ros2 launch turtlebot3_gazebo turtlebot3_world.launch.py

Where we can try the cartographer package for mapping:

ros2 launch turtlebot3_cartographer cartographer.launch.py use_sim:=true

There is another simulated environment:

ros2 launch turtlebot3_gazebo turtlebot3_house.launch.py

Where we can try the nav2 navigation stack:

ros2 launch turtlebot3_navigation2 navigation2_use_sim_time.launch.py map_yaml_file:=/home/david/ros2_ws/src/turtlebot3_simulations/turtlebot3_gazebo/maps/map.yaml

Replace the map_yaml path to your path!

Test on the real Turtlebot3

start on real robot: ros2 launch turtlebot3_bringup hardware.launch.py

Turtlebot3 MOGI

ros2 launch turtlebot3_mogi simulation_bringup_slam.launch.py ros2 launch turtlebot3_mogi simulation_bringup_navigation.launch.py ros2 launch turtlebot3_mogi simulation_bringup_navigation_with_slam.launch.py

ros2 launch turtlebot3_mogi simulation_bringup_line_follow.launch.py ros2 run turtlebot3_mogi_py line_follower

Line following

Python environment: sudo apt install python3-pip sudo apt install pipx pipx ensurepath pipx install virtualenv pipx install virtualenvwrapper

.bashrc

replace (created by pipx ensurepath)

Created by pipx on 2024-12-15 20:49:03

export PATH="$PATH:/home/david/.local/bin"

Virtual environment for pipx and tensorflow

export PATH="$PATH:/home/$USER/.local/bin" export WORKON_HOME=~/.virtualenvs export VIRTUALENVWRAPPER_PYTHON=/home/$USER/.local/share/pipx/venvs/virtu> source /home/$USER/.local/share/pipx/venvs/virtualenvwrapper/bin/virtuale> workon tf

ERROR: Environment 'tf' does not exist. Create it with 'mkvirtualenv tf'.

mkvirtualenv tf

(tf) david@david-ubuntu24:~$ pip install tensorflow pip install imutils pip install scikit-learn pip install opencv-python pip install matplotlib pip install numpy==1.26.4 - to downgrade from 2.x.x

https://drive.google.com/file/d/1DOdRYKexACRd480pxIIv4g2WUbkFnlHY/view?usp=sharing sudo dd if=/dev/sda of=/home/david/Backups/backup250129_shrinked.img status=progress sudo dd of=/dev/sda if=/home/david/Backups/backup250129_shrinked.img status=progress

t3_mogi_py$ python3 line_follower_cnn.py [INFO] Version: OpenCV version: 4.11.0 Tensorflow version: 2.18.0 Keras version: b'3.7.0' CNN model: ../network_model/model.best.h5 Model's Keras version: 2.9.0 You are using Keras version b'3.7.0' , but the model was built using 2.9.0

ros2 launch turtlebot3_bringup hardware.launch.py ros2 run turtlebot3_mogi_py line_follower_cnn_robot

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ conv2d (Conv2D) │ (None, 24, 24, 20) │ 1,520 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ activation (Activation) │ (None, 24, 24, 20) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ max_pooling2d (MaxPooling2D) │ (None, 12, 12, 20) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ conv2d_1 (Conv2D) │ (None, 12, 12, 50) │ 25,050 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ activation_1 (Activation) │ (None, 12, 12, 50) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ max_pooling2d_1 (MaxPooling2D) │ (None, 6, 6, 50) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ flatten (Flatten) │ (None, 1800) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense (Dense) │ (None, 500) │ 900,500 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ activation_2 (Activation) │ (None, 500) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_1 (Dense) │ (None, 4) │ 2,004 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ activation_3 (Activation) │ (None, 4) │ 0 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘ Total params: 2,787,224 (10.63 MB) Trainable params: 929,074 (3.54 MB) Non-trainable params: 0 (0.00 B) Optimizer params: 1,858,150 (7.09 MB)

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ conv2d (Conv2D) │ (None, 28, 28, 2) │ 52 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ activation (Activation) │ (None, 28, 28, 2) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ max_pooling2d (MaxPooling2D) │ (None, 7, 7, 2) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ conv2d_1 (Conv2D) │ (None, 7, 7, 4) │ 204 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ activation_1 (Activation) │ (None, 7, 7, 4) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ max_pooling2d_1 (MaxPooling2D) │ (None, 2, 2, 4) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ flatten (Flatten) │ (None, 16) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense (Dense) │ (None, 32) │ 544 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ activation_2 (Activation) │ (None, 32) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_1 (Dense) │ (None, 4) │ 132 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ activation_3 (Activation) │ (None, 4) │ 0 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘ Total params: 2,798 (10.93 KB) Trainable params: 932 (3.64 KB) Non-trainable params: 0 (0.00 B) Optimizer params: 1,866 (7.29 KB)