Drone Autonomous Mission Project

Overview

This project is a simulation and control system developed for TEKNOFEST 25/26 Rotary-Winged UAV Category. The project includes color perception-based tasks, state machine management, and security protocols.

Features

• 🎯 Multi-mission autonomous drone operations
• 🔍 Computer vision based object detection (red, blue, white)
• 🛡️ Failsafe mechanisms and emergency protocols
• 🗺️ State machine based mission management
• 🌍 Gazebo simulation environment
• 📊 Configurable parameters and scenarios

Project Structure

.
├── 📁 config/                           # Configuration files
│   ├── drone_params.yaml               # Drone parameters and settings
│   └── world_params.yaml               # World/environment parameters
│
├── 📁 docs/                             # Documentation
│   ├── 📁 setup/                       # Setup guides
|   |   ├── python_venv_24.04          # Project Python venv Setup guide
│   │   ├── ubuntu_22.04.md            # Ubuntu 22.04 installation guide
|   |   └── ubuntu_24.04.md            # Ubuntu 24.04 installation guide
│   ├── architecture.md                 # System architecture documentation
│   ├── checklist.md                    # Development and testing checklist
│   └── simulation_notes.md             # Simulation setup notes
│
├── 📁 environments/                     # Gazebo simulation environments
│   ├── 📁 models/                      # 3D models for simulation
│   │   ├── 📁 blue_hexagon/            # Blue hexagon shaped objects
│   │   ├── 📁 blue_square/             # Blue square objects
│   │   ├── 📁 blue_triangle/           # Blue triangle objects
│   │   ├── 📁 red_hexagon/             # Red hexagon shaped objects
│   │   ├── 📁 red_square/              # Red square objects
│   │   ├── 📁 red_triangle/            # Red triangle objects
│   │   └── 📁 white_pole/              # Tall White Pole
│   ├── 📁 worlds/                      # Gazebo world files
│   │   ├── iris_default.sdf            # Default simulation world of Ardupilot
│   │   ├── iris_parkour.sdf            # Main simulation world
│   │   └── iris_showcase.sdf           # Showcase world for testing the Model size/color
│   └── how-to-use.md                   # Environment usage guide
│
├── 📁 scenarios/                        # Mission scenarios
│   ├── 📁 comm_loss/                   # Communication loss scenarios
│   ├── 📁 gps_loss/                    # GPS loss scenarios
│   ├── 📁 normal_mission/              # Normal mission scenarios
│   │   ├── 📁 first/                   # First mission scenario
│   │   ├── 📁 second/                  # Second mission scenario
│   │   └── 📁 third/                   # Third mission scenario
│   └── 📁 weather_degraded/            # Weather degraded scenarios
│
|── 📁 scripts/                        # Project Scripts
|   ├── simulation_params_example      # Example of how "simulation_params" should be created on scripts directory
|   ├── ardupilot_sitl                 # Ardupilot SITL + MavLink Starter Terminal Script
|   └── sauro_gz                       # Gazebo World Starter Terminal Script
|
├── 📁 src/                              # Source code
│   ├── 📁 app/                         # Application entry point
│   │   └── run.py                      # Main application runner
│   ├── 📁 core/                        # Core functionality modules
│   │   ├── 📁 detection/               # Object detection algorithms
│   │   │   ├── blue_detection.py       # Blue object detection
│   │   │   ├── red_detection.py        # Red object detection
│   │   │   └── white_detection.py      # White object detection
│   │   ├── 📁 failsafe/                # Safety and failsafe mechanisms
│   │   │   └── failsafe_example.py     # Failsafe implementation example
│   │   ├── 📁 missions/                # Mission definitions
│   │   │   ├── first_mission.py        # First mission implementation
│   │   │   ├── second_mission.py       # Second mission implementation
│   │   │   └── third_mission.py        # Third mission implementation
│   │   └── 📁 state_machine/           # State machine management
│   │       ├── states.py               # State definitions
│   │       └── state_machine.py        # State machine logic
│   └── 📁 tests/                       # Application test Codes
|       ├── colorDetect.py              # Color Detection Python Script for detecting pole and targets
|       └── openCVraw.py                # OpenCV Raw Image Visualisation Script.
|
│
├── requirements.txt                     # Python dependencies
└── README.md                           # This file

Prerequisites

• Ubuntu 22.04 LTS or Ubuntu 24.04 LTS
• Python 3.8+
• ROS 2 (Humble recommended)
• Gazebo simulation environment
• ArduPilot SITL

Installation

1. Clone the repository:

   git clone <repository-url>
   cd <project-directory>

2. Install Python dependencies:

   pip install -r requirements.txt

3. Setup simulation environment:

   • Follow one of the guides in docs/setup/ ( Simulation is built on 24.04 LTS )
   • Configure Gazebo models and worlds

Usage

Running the Application

# Navigate to source directory
cd src/app

# Run the main application
python run.py

Configuration

• Modify config/drone_params.yaml for drone-specific settings
• Adjust config/world_params.yaml for environment parameters
• Select scenarios from the scenarios/ directory

Mission Modes

• First Mission: Basic navigation and object detection
• Second Mission: Advanced maneuvers and multi-target tracking
• Third Mission: Complex autonomous operations

Architecture

The system follows a modular architecture:

• Detection Module: Computer vision algorithms for object recognition
• State Machine: Mission flow control and state management
• Mission Manager: High-level mission logic and coordination
• Failsafe System: Emergency protocols and safety mechanisms

Development

Adding New Missions

1. Create mission file in src/core/missions/
2. Define states in src/core/state-machine/states.py
3. Add scenario configuration in scenarios/
4. Update documentation

Testing

• Use different scenarios in scenarios/ directory
• Test failsafe mechanisms under various conditions
• Validate detection algorithms with different object configurations

Documentation

• 📖 System Architecture
• ⚙️ Setup Guide
• ✅ Development Checklist
• 🔧 Simulation Notes
• 🌍 Environment Usage

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

MIT

Support

For questions and support, please refer to the documentation or open an issue in the repository.