AI-powered integration framework for drones and autonomous vehicles, combining computer vision, path planning, and real-time decision making.
flowchart TB
subgraph Vision["Computer Vision"]
YOLOv8[YOLOv8 Detection]
SLAM[Visual SLAM]
Track[Object Tracking]
end
subgraph Planning["Path Planning"]
AStar[A* Algorithm]
RRT[RRT* Planner]
Avoid[Obstacle Avoidance]
end
subgraph Decision["Decision Engine"]
BT[Behavior Trees]
RL[RL Agent]
Safety[Safety Systems]
end
subgraph Sensors["Sensor Fusion"]
Cam[Camera]
LiDAR[LiDAR]
IMU[IMU]
GPS[GPS]
end
subgraph Comm["Communication"]
MAV[MAVLink]
ROS[ROS 2]
end
Sensors --> Vision
Vision --> Decision
Planning --> Decision
Decision --> Comm
Comm --> Drone[Drone/Vehicle]
Drone AI provides a modular framework for integrating artificial intelligence into unmanned aerial vehicles (UAVs) and autonomous ground vehicles. The platform supports:
- Computer Vision: Object detection, tracking, and scene understanding
- Path Planning: Autonomous navigation with obstacle avoidance
- Decision Making: Real-time AI-powered flight/drive decisions
- Sensor Fusion: Combining data from multiple sensors (LiDAR, cameras, IMU)
- Communication: MAVLink protocol support for drone control
┌─────────────────────────────────────────────────────────────┐
│ Drone AI Platform │
├─────────────────────────────────────────────────────────────┤
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────┐ │
│ │ Vision │ │ Planning │ │ Decision Engine │ │
│ │ Module │ │ Module │ │ │ │
│ │ │ │ │ │ - Rule-based │ │
│ │ - Detection │ │ - A* Path │ │ - ML Models │ │
│ │ - Tracking │ │ - RRT* │ │ - Reinfortic │ │
│ │ - SLAM │ │ - Avoidance │ │ Learning │ │
│ └─────────────┘ └─────────────┘ └─────────────────────┘ │
├─────────────────────────────────────────────────────────────┤
│ Sensor Fusion Layer │
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────────────┐ │
│ │ Camera │ │ LiDAR │ │ IMU │ │ GPS │ │
│ └─────────┘ └─────────┘ └─────────┘ └─────────────────┘ │
├─────────────────────────────────────────────────────────────┤
│ Communication Layer │
│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────┐ │
│ │ MAVLink │ │ ROS 2 │ │ Custom │ │
│ └─────────────────┘ └─────────────────┘ └─────────────┘ │
└─────────────────────────────────────────────────────────────┘
- YOLOv8 object detection for real-time obstacle identification
- Visual SLAM for mapping and localization
- Optical flow for motion estimation
- Semantic segmentation for terrain analysis
- A* and Dijkstra algorithms for global planning
- RRT* (Rapidly-exploring Random Trees) for complex environments
- Dynamic obstacle avoidance with velocity obstacles
- Geofencing and no-fly zone compliance
- Behavior trees for mission execution
- Reinforcement learning for adaptive control
- Rule-based safety systems
- Emergency landing protocols
- Extended Kalman Filter (EKF) for state estimation
- Multi-sensor data alignment
- Redundancy and fault tolerance
- Real-time sensor health monitoring
# Clone the repository
git clone https://github.com/ianalloway/drone-ai.git
cd drone-ai
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Install ROS 2 (optional, for full simulation)
# See: https://docs.ros.org/en/humble/Installation.htmlfrom drone_ai import DroneController, VisionModule, PathPlanner
# Initialize the drone controller
drone = DroneController(connection_string="udp:127.0.0.1:14550")
# Set up computer vision
vision = VisionModule(model="yolov8n")
vision.start_detection()
# Create path planner
planner = PathPlanner(algorithm="rrt_star")
# Define mission waypoints
waypoints = [
(37.7749, -122.4194, 50), # San Francisco
(37.7849, -122.4094, 50),
(37.7949, -122.3994, 50),
]
# Execute autonomous mission
drone.arm()
drone.takeoff(altitude=50)
for waypoint in waypoints:
# Plan path with obstacle avoidance
path = planner.plan(drone.position, waypoint, vision.obstacles)
drone.follow_path(path)
drone.land()drone-ai/
├── src/
│ ├── vision/ # Computer vision modules
│ │ ├── detection.py # Object detection
│ │ ├── tracking.py # Object tracking
│ │ └── slam.py # Visual SLAM
│ ├── planning/ # Path planning algorithms
│ │ ├── astar.py # A* pathfinding
│ │ ├── rrt.py # RRT* algorithm
│ │ └── avoidance.py # Obstacle avoidance
│ ├── decision/ # Decision making
│ │ ├── behavior.py # Behavior trees
│ │ ├── rl_agent.py # Reinforcement learning
│ │ └── safety.py # Safety systems
│ ├── sensors/ # Sensor fusion
│ │ ├── fusion.py # Multi-sensor fusion
│ │ ├── ekf.py # Extended Kalman Filter
│ │ └── calibration.py
│ └── communication/ # Vehicle communication
│ ├── mavlink.py # MAVLink protocol
│ └── ros_bridge.py # ROS 2 integration
├── models/ # Pre-trained ML models
├── config/ # Configuration files
├── tests/ # Unit and integration tests
├── examples/ # Example scripts
└── docs/ # Documentation
- PX4 Autopilot
- ArduPilot
- DJI (via Mobile SDK)
- Custom flight controllers
- ROS 2 compatible robots
- NVIDIA Jetson platforms
- Custom autonomous vehicles
Create a config.yaml file:
drone:
connection: "udp:127.0.0.1:14550"
max_altitude: 120 # meters
max_speed: 15 # m/s
geofence:
enabled: true
radius: 500 # meters
vision:
model: "yolov8n"
confidence: 0.5
device: "cuda" # or "cpu"
planning:
algorithm: "rrt_star"
safety_margin: 2.0 # meters
replan_frequency: 5 # Hz
safety:
battery_threshold: 20 # percent
signal_loss_action: "return_home"
emergency_landing: trueThis software is for research and educational purposes. When operating real drones:
- Always follow local aviation regulations
- Maintain visual line of sight
- Never fly over people or crowds
- Check weather conditions before flight
- Ensure proper insurance coverage
- Test thoroughly in simulation first
Contributions are welcome! Please read our contributing guidelines and submit pull requests.
MIT License - see LICENSE file for details.
If you find this project helpful, consider supporting with ETH:
0x6f278ce76ba5ed31fd9be646d074863e126836e9
- Twitter: @ianallowayxyz
- Email: ian@allowayllc.com
- Portfolio: ianalloway.xyz