🚁 Emergent Flocking Simulation

A 3D simulation framework for autonomous drone swarms exhibiting emergent flocking behavior. This project demonstrates advanced concepts in swarm intelligence, multi-agent systems, and biomimetic algorithms.

🌟 Key Features

Emergent Flocking Behavior

• Dynamic Flock Formation: Drones naturally form cohesive groups through local interactions
• Mission-Based Behavior: Support for different mission types (delivery, surveillance, emergency)
• Adaptive Streaming: Formation of efficient traffic streams based on movement patterns

Advanced Environment Simulation

• 3D Space Management: Full three-dimensional movement and coordination
• Obstacle Avoidance: Intelligent pathfinding around physical obstacles
• Spatial Partitioning: Efficient neighbor detection using KD-trees

Real-Time Visualization

• Interactive 3D Display: Beautiful Matplotlib-based visualization with real-time updates
• Stream Analysis: Visual feedback on traffic stream formation and cohesion
• Mission Progress: Real-time tracking of mission completion and efficiency
• Path Tracking: Visual history of drone movements and interactions

🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/yourusername/emergent-flocking.git
cd emergent-flocking

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install the package
pip install -e .

Basic Usage

from emergent_flocking.simulation import EmergentFlockingSimulation

# Create a simulation with custom space dimensions
sim = EmergentFlockingSimulation(space_size=(50, 50, 30))

# Run the simulation with real-time visualization
sim.run()

🎮 Advanced Usage

Custom Drone Configurations

from emergent_flocking.simulation import EmergentFlockingGrid
import numpy as np

# Initialize the simulation environment
grid = EmergentFlockingGrid(space_size=(50, 50, 30))

# Add drones with specific missions
grid.add_drone(
    drone_id="delivery_1",
    position=np.array([10, 10, 5]),
    goal=np.array([40, 40, 25]),
    mission_type="delivery"
)

# Configure flocking parameters
grid.separation_weight = 1.5
grid.cohesion_weight = 0.8
grid.alignment_weight = 1.0

Mission Type Customization

# Define custom mission parameters
mission_config = {
    "delivery": {
        "max_speed": 2.0,
        "priority": "efficiency"
    },
    "emergency": {
        "max_speed": 3.0,
        "priority": "speed"
    }
}

sim = EmergentFlockingSimulation(mission_config=mission_config)

🔧 Technical Details

Core Components

1. Flocking Behavior

   • Separation: Collision avoidance
   • Alignment: Velocity matching
   • Cohesion: Group centering
   • Mission-oriented movement

2. Traffic Stream Formation

   • Dynamic stream identification
   • Adaptive grouping
   • Efficient path sharing

3. Path Planning

   • Goal-oriented movement
   • Obstacle avoidance
   • Stream integration

Performance Considerations

• Optimized numpy operations for swarm calculations
• Efficient neighbor detection using KD-trees
• Vectorized force computations

📊 Applications

• Swarm Robotics Research: Test emergent behavior algorithms
• Traffic Management: Study drone traffic patterns and optimization
• Mission Planning: Evaluate multi-agent mission strategies
• Urban Air Mobility: Simulate drone delivery systems
• Emergency Response: Model coordinated emergency response scenarios

Development Setup

# Create a new branch
git checkout -b feature/your-feature-name

# Install development dependencies
pip install -r requirements-dev.txt

# Run tests
pytest tests/

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Flocking algorithms inspired by Craig Reynolds' Boids
• Visualization components built on Matplotlib and NumPy
• Scientific computing powered by SciPy and Pandas
• Special thanks to all contributors and the open-source community

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