ArduGazeboSim-Docker

🎥 Demo Video

ArduGazeboSim-Docker is a comprehensive Docker-based simulation environment for ArduPilot and Gazebo Classic 11, designed for drone development and testing. This project provides a complete containerized setup with ROS Noetic, allowing developers to work seamlessly across different machines without worrying about dependencies.

Features / Özellikler

• ✅ Containerized Environment: Docker-based setup with all dependencies pre-installed
• ✅ VS Code Integration: Full development environment with Dev Containers
• ✅ Multi-Drone Support: Simulate multiple drones simultaneously
• ✅ ROS Noetic Integration: Complete ROS ecosystem for drone control
• ✅ Gazebo Classic 11: Realistic physics simulation
• ✅ MAVProxy Support: Direct connection to ArduPilot SITL
• ✅ Cross-Platform: Works on any system with Docker support
• ✅ Persistent Storage: Project files stored on host machine

Prerequisites / Ön Gereksinimler

• Docker and Docker Compose
• VS Code with Docker, Dev Containers and Remote - Containers extensions
• Sufficient disk space (~10GB for initial setup)

Installation / Kurulum

1. Host Machine Preparation / Ana Makine Hazırlığı

🟢 Ubuntu / Debian

# Update system
sudo apt update && sudo apt upgrade -y

# Install Docker and Docker Compose
sudo apt install -y docker.io docker-compose docker-buildx-plugin

# Enable and start Docker
sudo systemctl enable --now docker

# Add user to docker group (logout and login required)
sudo usermod -aG docker $USER

# Install NVIDIA Container Toolkit
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
  && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \
  && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

sudo apt update
sudo apt install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

# Allow X11 forwarding for GUI applications
xhost +local:

---

🔵 Arch Linux

# Update system
sudo pacman -Syu --noconfirm

# Install Docker and Docker Compose
sudo pacman -S --noconfirm docker docker-compose docker-buildx

# Enable and start Docker
sudo systemctl enable --now docker

# Add user to docker group (logout and login required)
sudo usermod -aG docker $USER

# Install NVIDIA Container Toolkit
yay -S --noconfirm nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

# Allow X11 forwarding for GUI applications
xhost +local:

---

🟠 Fedora

# Update system
sudo dnf -y update

# Install Docker and Docker Compose
sudo dnf -y install docker docker-compose docker-buildx

# Enable and start Docker
sudo systemctl enable --now docker

# Add user to docker group (logout and login required)
sudo usermod -aG docker $USER

# Install NVIDIA Container Toolkit
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
  && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo rpm --import - \
  && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/yum.repos.d/nvidia-container-toolkit.repo

sudo dnf -y clean expire-cache
sudo dnf -y install nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

# Allow X11 forwarding for GUI applications
xhost +local:

---

2. Project Setup / Proje Kurulumu

# Create project directory
mkdir -p ~/ArduGazeboSim
cd ~/ArduGazeboSim
code .

3. Docker Configuration / Docker Yapılandırması

Create a Dockerfile in the project root:

FROM osrf/ros:noetic-desktop-full

# Install system dependencies
RUN apt-get update && \
    DEBIAN_FRONTEND=noninteractive apt-get install -y \
    git wget curl nano cmake build-essential \
    python3-dev python3-pip python3-setuptools python3-wheel \
    python3-matplotlib python3-numpy python3-pandas python3-scipy \
    python3-sqlalchemy python3-pexpect python3-wstool \
    python3-rosinstall-generator python3-catkin-lint python3-catkin-tools \
    ros-noetic-geographic-msgs \
    gazebo11 libgazebo11-dev \
    python3-wxgtk4.0 \
    && rm -rf /var/lib/apt/lists/*

# Install Python packages
RUN pip3 install --no-cache-dir future lxml pymavlink MAVProxy osrf-pycommon empy

# Ekran kartı için yorum satırını kaldırın
# apt-get update && apt-get install -y mesa-utils

# Create project directory
RUN mkdir -p /home/user/drone_project
WORKDIR /home/user/drone_project

# Set PATH for ArduPilot tools
ENV PATH="/home/user/drone_project/ardupilot:/home/user/drone_project/ardupilot/Tools/autotest:${PATH}"

CMD ["/bin/bash"]

4. Dev Container Configuration / Dev Container Yapılandırması

Create .devcontainer/devcontainer.json:

{
  "name": "ArduGazeboSim Environment",
  "dockerFile": "../Dockerfile",
  "customizations": {
    "vscode": {
      "extensions": [
        "ms-python.python",
        "ms-azuretools.vscode-docker",
        "ms-vscode-remote.remote-containers"
      ]
    }
  },
  "forwardPorts": [8100, 8200, 8300],
  "mounts": [
    "source=${localWorkspaceFolder},target=/home/user/drone_project,type=bind"
  ],
  "runArgs": [
    // Ekran kartı için yorum satırlarını kaldırın.
    //"--gpus=all",
    "--net=host",
    "--env=DISPLAY=${env:DISPLAY}",
    "--env=QT_X11_NO_MITSHM=1",
    "--volume=/tmp/.X11-unix:/tmp/.X11-unix:rw",
    //"--device=/dev/dri:/dev/dri"
  ]
}

📂 Project Folder Structure

ArduGazeboSim/
├── .devcontainer/                  # VS Code Dev Container settings
│   └── devcontainer.json           # References the Dockerfile for container setup
│
├── Dockerfile                      # Main Dockerfile for the development environment
│
....
│   (your project files go here)

5. Start the Container / Konteyneri Başlatma

In VS Code:

1. Locate the >< icon in the bottom-left corner of the VS Code window.
2. Click it and select “Reopen in Container” from the menu.
3. Wait for the container to build and start (first time may take 10-15 minutes)

6. Install ROS and MAVROS / ROS ve MAVROS Kurulumu

Instead of performing all installations manually, you can run the setup_simulation.sh script prepared inside the Docker container. This will automatically install all dependencies.

Steps to Run:

1. Make the script executable inside the Docker container:

chmod +x setup_simulation.sh

2. Start the script:

./setup_simulation.sh

Note: If you get errors like PreArm: Main loop slow or Arm: COMPONENT_ARM_DISARM: FAILED, run this in the same terminal as SITL:

param set ARMING_CHECK 0

This disables pre-arm checks for simulation.

Manual Seting

After the container is built, open a terminal inside VS Code (inside the container) and run the following commands step by step.

# Create catkin workspace
cd /home/user/drone_project
mkdir -p catkin_ws/src
cd catkin_ws
source /opt/ros/noetic/setup.bash
catkin init

# Install MAVROS and MAVLink
apt-get update && apt-get install -y software-properties-common && add-apt-repository universe
wstool init src
rosinstall_generator --rosdistro noetic --upstream mavros | tee /tmp/mavros.rosinstall
rosinstall_generator --rosdistro noetic mavlink | tee -a /tmp/mavros.rosinstall
wstool merge -t src /tmp/mavros.rosinstall
wstool update -t src
rosdep update
rosdep install --from-paths src --ignore-src --rosdistro noetic -y

# Clone IQ Simulation package
cd src
git clone https://github.com/Intelligent-Quads/iq_sim.git

# Set Gazebo model path
echo "export GAZEBO_MODEL_PATH=\$GAZEBO_MODEL_PATH:/home/user/drone_project/catkin_ws/src/iq_sim/models" >> ~/.bashrc

# Build catkin workspace
cd /home/user/drone_project/catkin_ws
catkin build
echo "source /home/user/drone_project/catkin_ws/devel/setup.bash" >> ~/.bashrc
source ~/.bashrc

7. Install ArduPilot / ArduPilot Kurulumu

In the same terminal inside VS Code, run the following commands to clone and build ArduPilot.

# Clone ArduPilot
cd /home/user/drone_project
git clone --recurse-submodules https://github.com/ArduPilot/ardupilot.git
cd ardupilot
./waf configure --board sitl
./waf copter

# Set PATH for ArduPilot tools
echo "export PATH=\$PATH:/home/user/drone_project/ardupilot:/home/user/drone_project/ardupilot/Tools/autotest" >> ~/.bashrc
source ~/.bashrc

8. Install ArduPilot Gazebo Plugin / ArduPilot Gazebo Eklentisi Kurulumu

In the same terminal inside VS Code, run the following commands to clone and build ArduPilot.

# Clone and build the plugin
cd /home/user/drone_project/ardupilot
git clone https://github.com/khancyr/ardupilot_gazebo.git ardupilot_gazebo_classic
cd ardupilot_gazebo_classic
mkdir build && cd build
cmake ..
make -j$(nproc)
make install

# Set environment variables
echo 'export GAZEBO_PLUGIN_PATH=$GAZEBO_PLUGIN_PATH:/usr/local/lib' >> ~/.bashrc
echo 'export GAZEBO_MODEL_PATH=$GAZEBO_MODEL_PATH:/home/user/drone_project/ardupilot/ardupilot_gazebo_classic/models' >> ~/.bashrc
source ~/.bashrc

Usage / Kullanım

Running the Simulation / Simülasyonu Çalıştırma

1. Terminal 1 - Start Gazebo and ROS:

   Open a new terminal inside VS Code (inside the container) to start the simulation environment. Then run:

   source ~/.bashrc
   roslaunch iq_sim multi_drone.launch

2. Terminal 2 - Start ArduCopter SITL:

   Open another new terminal inside VS Code for ArduPilot connection. This terminal will handle drone SITL simulation:

   cd /home/user/drone_project/ardupilot
   sim_vehicle.py -v ArduCopter -f gazebo-iris --console --map -I0

Note: After completing the simulation setup, you can:

• Use the container to start your own projects inside VS Code.
• Open a new terminal inside the container to run Python scripts, test ROS nodes, or start additional simulations.

Tips and Recommendations / İpuçları ve Öneriler

• Persistent Environment: All project files are stored on your host machine. The container can be stopped and restarted without losing your work.
• Rebuilding: If you modify the Dockerfile, you can update the container by clicking the >< icon in the bottom-left corner of VS Code again and selecting “Rebuild Container”. VS Code will automatically rebuild the container with your changes.
• GPU Support: For GPU acceleration, add "--gpus=all" to runArgs in devcontainer.json and install NVIDIA Docker.

Happy flying! / İyi uçuşlar! 🚁✨