🐢 Learning-ROS2

A personal workspace for learning and experimenting with ROS 2 Humble using Docker, GPU acceleration (NVIDIA RTX 4060), and VS Code integration. The notes cover the author’s Docker/NVIDIA setup and common ROS commands; adapt the host-specific examples to your own machine.

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Start here

This repository contains learning notes, not a ROS package workspace or a ready-to-run container definition.

• Main setup walkthrough: Manjaro host, Docker and optional NVIDIA GPU support.
• ROS 2 notes: concepts and commands to revisit while learning.
• WSL 2 / ROS 2 Humble guide: the separate Windows/WSL setup path.
• Build/run examples: use these in a workspace containing your own packages.

The Dockerfile, Compose and VS Code snippets below are examples. Save and adapt the relevant snippets before running their commands. The sample Dockerfile intentionally leaves ROS repository/package installation as a placeholder; it does not install ROS by itself. The examples are not a tested, pinned environment definition. A GPU is optional for basic ROS publisher/subscriber learning.

📖 What is ROS 2?

ROS 2 (Robot Operating System 2) is a flexible framework for writing robot software. It provides:

• A publish/subscribe communication system between nodes
• Tools for simulation (Gazebo, RViz2)
• Libraries for robotics, AI, and machine learning integration
• Support for C++ and Python development

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📚 Table of Contents

• Overview

• Design Choices

• Prerequisites (Host: Manjaro Linux)

• Recommended (Docker) Installation

  • NVIDIA / GPU Passthrough
  • Docker Compose Example
  • Common Container Workflow

• Developing Inside the Container (VS Code)

• Building & Running ROS 2 Packages

• Alternative Installation Methods

• Tips & Troubleshooting

• Resources & Further Reading

• Contributing

• License

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🏁 Overview

This repository prioritizes a Docker-first approach so you get a reproducible, clean development environment with minimal interference from host packages. That also makes GPU acceleration, VS Code integration, and CI-friendly builds much simpler.

Why Docker?

• Isolates ROS 2 Humble environment from host package manager (Manjaro)
• Easier to enable NVIDIA GPU support via --gpus and nvidia-container-toolkit
• Quick reproducible setup for teammates or future you

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⚙️ Design Choices

1. Docker (Recommended) – reproducible, GPU-enabled, minimal host changes.
2. AUR packages – quicker to get binaries but can break with rolling releases.
3. Source build – most flexible but time-consuming; only recommended for maintainers or for experimental changes to core packages.

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🧾 Prerequisites (Host: Manjaro Linux)

These steps assume you have an NVIDIA GPU (RTX 4060) and want GPU acceleration inside the container.

1. Install NVIDIA drivers (host) and verify:

nvidia-smi

2. Install Docker and enable the service:

sudo pacman -Syu docker
sudo systemctl enable --now docker

3. Install NVIDIA container tooling (on Manjaro):

sudo pacman -S nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

4. Test GPU passthrough:

docker run --rm --gpus all nvidia/cuda:12.2.0-base-ubuntu22.04 nvidia-smi

You should see your RTX 4060 listed in the container output.

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✅ Recommended (Docker) Installation

1) Clone this repository

git clone https://github.com/SuhanVerse/Learning-ROS2.git
cd Learning-ROS2

2) Example Dockerfile (optional)

If you want a custom image (base: Ubuntu 22.04 + ROS Humble):

# Dockerfile
FROM ubuntu:22.04
ARG DEBIAN_FRONTEND=noninteractive

# Install basic dependencies and ROS 2 keys & repos (follow official instructions)
RUN apt-get update && apt-get install -y \
    curl gnupg2 lsb-release locales sudo && \
    locale-gen en_US.UTF-8

# (Add ROS 2 apt repository & install ros-humble-desktop or ros-humble-ros-base as needed)

# Optional: create a developer user
RUN useradd -m developer && echo "developer:developer" | chpasswd && adduser developer sudo
USER developer
WORKDIR /home/developer

CMD ["/bin/bash"]

Tip: Use ros:humble or ros:humble-ros-base official images for faster iteration when possible.

3) docker-compose.yml (example)

A minimal docker-compose.yml for development with GPU support:

version: '3.8'
services:
  ros2_dev:
    image: ros:humble
    container_name: ros2_dev
    tty: true
    privileged: true
    network_mode: "host"
    environment:
      - DISPLAY=${DISPLAY}
      - QT_X11_NO_MITSHM=1
    volumes:
      - $HOME/LEARN:/root/LEARN
      - /tmp/.X11-unix:/tmp/.X11-unix:rw
    deploy:
      resources:
        reservations:
          devices:
            - capabilities: [gpu]
    runtime: nvidia
    command: /bin/bash

After saving the Compose example as docker-compose.yml and adjusting its workspace mount, start the container:

docker compose up -d --build

Attach to the container shell:

docker exec -it ros2_dev bash

Inside the container, source ROS 2:

source /opt/ros/humble/setup.bash

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🖥️ Developing with VS Code

You can configure VS Code to open a terminal that drops you into the running container with your workspace sourced. Examples for .vscode/settings.json and .vscode/tasks.json are shown below.

Example: .vscode/settings.json terminal profile

{
  "terminal.integrated.profiles.linux": {
    "ROS2-Docker": {
      "path": "/bin/bash",
      "args": [
        "-c",
        "docker exec -it ros2_dev bash -c '(source /opt/ros/humble/setup.bash && source /root/LEARN/install/setup.bash) || true; exec bash'"
      ]
    }
  },
  "terminal.integrated.defaultProfile.linux": "ROS2-Docker"
}

Example: .vscode/tasks.json snippets for running talker/listener

{
  "version": "2.0.0",
  "tasks": [
    {
      "label": "run talker",
      "type": "shell",
      "command": "docker exec -it ros2_dev bash -c 'source /opt/ros/humble/setup.bash && source /root/LEARN/install/setup.bash && ros2 run demo_nodes_cpp talker'"
    },
    {
      "label": "run listener",
      "type": "shell",
      "command": "docker exec -it ros2_dev bash -c 'source /opt/ros/humble/setup.bash && source /root/LEARN/install/setup.bash && ros2 run demo_nodes_cpp listener'"
    }
  ]
}

Tip: Use the Remote - Containers extension if you prefer VS Code to manage containers for you.

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🛠 Building Your Workspace (inside container)

Assuming your ROS 2 workspace is at /root/LEARN inside the container:

cd /root/LEARN
colcon build
source install/setup.bash

Install extra packages if needed:

apt update && apt install -y ros-humble-turtlesim

Run the simulator:

# Terminal 1
ros2 run turtlesim turtlesim_node

# Terminal 2
ros2 run demo_nodes_cpp talker

# Terminal 3 (source ROS in this shell too)
ros2 run demo_nodes_cpp listener

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🔁 Common Workflow (Quick Checklist)

• Start container: docker compose up -d
• Attach shell: docker exec -it ros2_dev bash
• Source ROS 2 and workspace: source /opt/ros/humble/setup.bash && source /root/LEARN/install/setup.bash
• Build: colcon build
• Run nodes with ros2 run or ros2 launch

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🧭 Alternative Installation Methods on Manjaro

• AUR packages – e.g., yay -S ros-humble-desktop. Good for native installs but may require maintenance on rolling distributions.
• Source build – follow the official ROS 2 instructions to build from source. Useful for bleeding-edge patches or custom core changes.

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🛠 Tips & Troubleshooting

• GPU not visible in container: ensure nvidia-container-toolkit is installed and docker run --gpus all ... works.
• Docker permission issues: add your user to the docker group or use sudo for Docker commands.
• GUI apps not showing: mount the X11 socket (/tmp/.X11-unix) and run xhost +local:root (or a more secure alternative).
• colcon build failures: verify package.xml and CMakeLists.txt for missing dependencies and use rosdep to install OS dependencies.

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🔗 Resources & Further Reading

• ROS 2 official docs: https://docs.ros.org/en/humble/
• Colcon build tool: https://colcon.readthedocs.io/
• NVIDIA Container Toolkit: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/
• ROS 2 tutorials: https://docs.ros.org/en/humble/Tutorials.html

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🤝 Contributing

Feel free to open issues or PRs if you have suggestions, updates for newer ROS 2 distributions, or improved Docker setups.

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📜 License

Licensed under the Apache-2.0 License.

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