PX4 Drone Simulation with ROS 2 and Gazebo 🚁

This repository provides a workspace for simulating and controlling an Iris drone using PX4, ROS 2, and Gazebo Classic. It includes packages for low-level motor control, motor failure detection, trajectory publishing, and real-time visualization. The workspace is designed to streamline drone simulation and testing, offering tools for motor control, parameter configuration, and motor failure analysis.

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Workspace Structure 🗂️

ROS_PX4/
├── src/
│   ├── px4_detector/              # Package for motor failure detection and trajectory control
│   │   └── px4_detector/
│   │       ├── trajectory_publisher.py  # Publishes trajectories for the drone ✈️
│   │       └── detector.py             # Motor failure detection logic ⚙️
│   ├── px4_motor_control/        # Low-level motor control package 🔧
│   │   ├── config/
│   │   │   ├── initial_gains_iris.yaml  # Initial PID gain values
│   │   │   ├── iris_param.yaml          # Iris drone parameters
│   │   │   └── sitl_params.yaml         # Simulation parameters
│   │   ├── include/
│   │   │   └── px4_offboard_lowlevel/
│   │   │       ├── controller_node.h    # Node header for motor control 🛠️
│   │   │       └── controller.h         # Main motor control logic
│   │   ├── launch/
│   │   │   └── setup.launch.py          # ROS 2 launch file for simulation 🚀
│   │   ├── src/
│   │   │   ├── controller_node.cpp      # ROS 2 node for motor control
│   │   │   └── controller.cpp           # Motor control implementation
│   │   ├── CMakeLists.txt
│   │   └── package.xml
│   ├── px4_msgs/                  # ROS 2 custom messages for PX4 📬
│   │   └── msgs/
│   │       ├── ActionRequest.msg        # Custom message definitions
│   │       ...
│   └── px4_ros_com/               # Interface for ROS 2 and PX4 communication
├── .gitignore
├── .gitmodules
└── README.md

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Features ✨

1. Drone Simulation 🛸:
   Simulate the Iris drone in Gazebo Classic using PX4's SITL (Software in the Loop) environment.

2. Low-Level Motor Control 🔧:
   Implement precise motor control using a ROS 2 node with configurable PID gains.

3. Motor Failure Detection ⚠️:
   Simulate motor failures for testing robust flight control and fault tolerance.

4. Trajectory Control 📈:
   Publish desired drone trajectories using Python scripts.

5. Real-Time Visualization 📊:
   Visualize drone parameters like thrust, torque, and position tracking using PlotJuggler.

6. Configurable Parameters 🛠️:
   Customize drone parameters, PID gains, and SITL settings via YAML files.

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Installation and Setup

Prerequisites ✅

• Operating System: Ubuntu 22.04 🐧
• ROS 2: Humble 🤖
• PX4-Autopilot: Follow the PX4 installation guide.
• Gazebo Classic: Install for realistic drone simulation.
• PlotJuggler (optional): For real-time data visualization.

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Cloning the Repository 📂

git clone https://github.com/Hit2737/ROS_PX4.git

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Building the Workspace 🏗️

1. Navigate to the workspace directory:

   cd ROS_PX4

2. Build the workspace using colcon:

   colcon build --symlink-install

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Running the Simulation 🎮

1. Launch the Iris Drone in Gazebo Classic 🌍

Start the PX4 SITL simulation for the Iris drone:

cd PX4-Autopilot
make px4_sitl gazebo-classic_iris

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2. Source the Workspace 🧩

Before running any ROS 2 commands, source the workspace:

source install/setup.bash

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3. Launch the ROS 2 Setup 🚀

Run the main launch file for motor control and trajectory configuration:

ros2 launch px4_motor_control setup.launch.py

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4. Publish a Trajectory ✈️

In a new terminal, source the workspace and run the trajectory publisher:

ros2 run px4_detector trajectory_publisher

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5. Simulate Motor Failure ⚙️

To fail a specific motor during the simulation, use the following command:

gz topic -p /gazebo/motor_failure_num -m "data: <motor_number>"

Replace <motor_number> with the motor index you want to fail (e.g., 2).

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6. Visualize Data with PlotJuggler (Optional) 📊

For real-time data visualization, launch PlotJuggler:

ros2 run plotjuggler plotjuggler

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Configuration Files 🛠️

The px4_motor_control/config/ directory contains key configuration files:

• initial_gains_iris.yaml:
  Configures initial PID gains for motor control.

• iris_param.yaml:
  Drone-specific parameters such as mass, inertia, and motor coefficients.

• sitl_params.yaml:
  SITL-specific parameters for tuning the simulation environment.

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Acknowledgments 🙌

This repository is inspired by:

• SMART Research Group's Repository.
• The PX4 open-source community for providing tools and documentation.

For detailed documentation and updates, visit the PX4 website.

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Enjoy Your PX4 Drone Simulation! 🚁💻

Explore the possibilities of PX4, ROS 2, and Gazebo Classic for building robust drone systems. 🛸🎉