EULER

Lightweight Swarm Coordination Protocol for Autonomous Drones

Overview

EULER is a decentralized drone swarm system enabling 10-50 drones to fly coordinated formations, avoid collisions, and accomplish collective missions through local communication and simple behavioral rules. Designed for embedded systems with minimal computational overhead.

Problem Domain

Traditional drone swarms require centralized ground control stations creating single point of failure and communication bottlenecks. Existing solutions use complex AI requiring powerful onboard computers, limiting payload capacity and flight time.

EULER solves through emergence: simple local rules produce complex collective behavior without global coordination.

Core Innovation

Decentralized Architecture: Each drone autonomous unit making decisions from local information only. No master controller - swarm intelligence emerges from peer interaction.

Lightweight Protocols: Communication messages under 32 bytes. Position updates at 10Hz sufficient for coordination. Collision avoidance using simple geometric calculations executable in microseconds.

Behavioral Primitives: Five basic behaviors compose all missions: maintain formation, follow leader, avoid collision, reach waypoint, land safely. Complex tasks emerge from primitive combinations.

Technical Architecture

Communication Layer

Protocol: UDP broadcast on WiFi mesh or LoRa for long range. Each drone broadcasts position/velocity every 100ms. Neighbors within radio range receive and process.

Message Format:

struct DroneState {
    uint8_t drone_id;
    float position[3];    // x, y, z in meters
    float velocity[3];    // vx, vy, vz in m/s
    uint8_t battery;      // percentage
    uint8_t status;       // flags: armed, mission_active, emergency
};

Collision Avoidance

Algorithm: Artificial potential fields. Other drones create repulsive forces, waypoint creates attractive force. Drone moves along gradient of combined field.

Implementation:

• Calculate vector to each nearby drone
• If distance < safety_radius (5m), add repulsive force inversely proportional to distance squared
• Sum all forces, normalize, apply to velocity command
• Computation: ~50 microseconds for 10 neighbors

Formation Control

Virtual Structure Method: Define formation as geometric shape (line, grid, circle). Each drone assigned position in structure. Control law drives drone toward assigned position while maintaining formation orientation.

Leader-Follower: Designate leader drone broadcasting waypoints. Followers maintain relative positions using PID controllers tracking leader's position with fixed offsets.

Application Use Cases

Agricultural Spraying: Drones form line formation covering field width, maintain spacing, follow GPS waypoints. Collective coverage 10x faster than single drone.

Search and Rescue: Grid formation sweeps area systematically. First drone detecting target signals others to converge. Thermal cameras share detections triggering coordinated response.

Light Shows: Drones form 3D shapes in sky changing colors synchronously. Pre-programmed choreography with collision avoidance ensuring safety. 100+ drones create spectacular displays.

Delivery Fleet: Multiple drones transport packages from warehouse to destinations. Dynamic routing avoids congestion, shares battery charging stations, load-balances deliveries.

Performance Characteristics

Latency: 100ms position update cycle. 50ms collision avoidance reaction time. Total response latency <200ms sufficient for flight speeds up to 10 m/s.

Scalability: System tested with 50 drones. Communication overhead grows linearly (each drone processes N-1 neighbor messages). Practical limit ~100 drones before bandwidth saturation.

Reliability: No single point of failure. Individual drone failure doesn't impact swarm. Surviving drones adjust formation automatically filling gaps.

Resource Usage:

• RAM: 16KB for neighbor tracking
• CPU: <5% of 168MHz ARM Cortex-M4
• Battery impact: <2% additional consumption for coordination

Hardware Requirements

Flight Controller: Pixhawk, ArduPilot, or custom STM32-based controller with MAVLink support.

Communication: ESP32 WiFi module or LoRa transceiver for long range. Bandwidth: 250kbps sufficient for 50 drones.

Sensors: GPS for positioning, IMU for attitude, barometer for altitude, ultrasonic for ground detection.

Typical Drone Specs: 250-450mm frame, 1000-2000mAh battery, 15-25 minute flight time, 5-10kg payload capacity.

License

MIT License

References

1. Reynolds, C. (1987). "Flocks, Herds, and Schools: A Distributed Behavioral Model"
2. Olfati-Saber, R. (2006). "Flocking for Multi-Agent Dynamic Systems"
3. Tanner, H. (2004). "Formation Control with Prescribed Performance"