Advanced drone control system with AI-powered object detection, natural language commands, and comprehensive flight management capabilities.
- TensorFlow Object Detection: Real-time detection of 91+ object classes
- Natural Language Commands: Control drone using plain English commands
- AI Mission Planner: Automatically generate optimal flight paths
- Smart Target Following: AI-powered object tracking and following
- Modern GUI: Contemporary dark theme with intuitive controls
- Real-time Video Display: Live drone camera feed with detection overlays
- Comprehensive Flight Controls: Manual and automated flight options
- Status Monitoring: Real-time telemetry and system status
- Voice Commands: Hands-free drone control using speech recognition
- Text-to-Speech: Audio feedback and status announcements
- Multi-language Support: Multiple language options available
- Live Video Stream: Real-time video from drone camera
- 360° Panorama Mode: Automated panoramic photography
- Photo/Video Capture: High-quality media capture during flight
- Emergency Stop: Immediate drone shutdown capability
- Battery Monitoring: Real-time battery level tracking
- Flight Limits: Configurable safety boundaries
- Auto-Landing: Automatic landing on low battery
- DJI Tello Standard: Full compatibility
- DJI Tello EDU: Enhanced features support
- Simulation Mode: No physical drone required for testing
- Operating System: Windows 10+, macOS 10.15+, or Linux (Ubuntu 18.04+)
- Python: Version 3.11 or higher
- RAM: Minimum 8GB (16GB recommended for AI features)
- Storage: 2GB free space
- Network: Wi-Fi capability for drone connection
- TensorFlow 2.20+
- OpenCV 4.12+
- djitellopy 2.5+
- Azure OpenAI API (for natural language commands)
# Clone the repository
git clone https://github.com/yourusername/TelloDroneControlSystem.git
cd TelloDroneControlSystem
# Create virtual environment
python -m venv drone_env
# Windows
drone_env\Scripts\activate
# macOS/Linux
source drone_env/bin/activate
# Install dependencies
pip install -r requirements.txt
# Set up environment variables
cp .env.example .env
# Edit .env with your API keys
# Run the application
python drone_gui.py# Install core dependencies
pip install djitellopy>=2.5.0
pip install tensorflow>=2.20.0
pip install opencv-python>=4.12.0.88
pip install pillow>=11.3.0
pip install numpy>=2.2.6
pip install openai>=1.107.3
# ... see full requirements in pyproject.toml- Launch the application:
python drone_gui.py - System automatically starts in simulation mode
- Try natural language commands: "take off and hover"
- Test AI detection using your webcam
- Experiment with voice commands and manual controls
- Power on your Tello drone
- Connect to Tello Wi-Fi network (TELLO-XXXXXX)
- Launch the application:
python drone_gui.py - Click "Connect" to establish connection
- Start with simple commands: "take off"
"Take off, fly forward 2 meters, turn right 90 degrees, take a photo, then come back and land"
"Start recording video, fly in a circle around the yard, then stop recording and land"
"If you see a person, follow them for 30 seconds then return home"
"Scan the room for objects and tell me what you find"
- Activate microphone and speak naturally
- Supported languages: English, Spanish, French, German, Chinese
- Clear pronunciation recommended for best results
- Toggle Detection Mode: Switch between OpenCV and AI detection
- 91+ Object Classes: People, vehicles, animals, household items, etc.
- Real-time Processing: <100ms inference time on CPU
- Confidence Scoring: Adjustable detection thresholds
Complete documentation is available in multiple formats:
- User Guide (PDF) - Comprehensive 84-page guide
- Technical Manual (Word) - Editable documentation
- Developer Reference (Markdown) - Full technical reference
TelloDroneControlSystem/
├── drone_gui.py # Main GUI application
├── tello_drone_agent.py # Core drone control logic
├── tello_simulator.py # Simulation system
├── models/ # AI detection models
│ ├── detect.tflite # TensorFlow Lite model
│ └── labelmap.txt # Object class labels
├── logs/ # System logs
├── attached_assets/ # Media assets
├── pyproject.toml # Project configuration
├── requirements.txt # Python dependencies
└── README.md # This file
Create a .env file with:
AZURE_OPENAI_API_KEY=your_azure_openai_key_here
AZURE_OPENAI_DEPLOYMENT_NAME=your_deployment_name
AZURE_OPENAI_ENDPOINT=your_azure_endpointConfigure detection sensitivity, flight limits, and other preferences through the GUI settings panel.
# Run simulation tests
python test_video_simulator.py
# Test CLI interface
python drone_cli.py --help# Build Windows executable
python build_windows_app.py
# Build GUI application
python build_gui_app.pyConnection Problems
- Ensure drone is powered and in pairing mode
- Check Wi-Fi connection to Tello network
- Verify no firewall blocking UDP ports 8889/8890
AI Detection Issues
- Verify TensorFlow installation
- Check lighting conditions
- Update graphics drivers
- Ensure models are present in
models/directory
Performance Issues
- Close unnecessary applications
- Use wired internet connection
- Optimize detection settings
- Check system resource usage
For detailed troubleshooting, see the Complete Documentation.
We welcome contributions! Please:
- Fork the repository
- Create a feature branch:
git checkout -b feature-name - Commit changes:
git commit -am 'Add new feature' - Push to branch:
git push origin feature-name - Submit a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
- DJI for the Tello drone platform
- TensorFlow team for the object detection models
- OpenCV community for computer vision tools
- Azure OpenAI for natural language processing capabilities
For support, bug reports, or feature requests:
- Issues: GitHub Issues
- Discussions: GitHub Discussions
- Email: your.email@example.com
🚁 Ready to take flight with AI-powered drone control!
Built with ❤️ for the drone community