🚁 Intention Broadcasting System (IBS)

A simulation framework for autonomous drone networks using intention broadcasting and dynamic mesh networking. This project demonstrates advanced concepts in multi-agent coordination, conflict resolution, and emergency response systems.

🌟 Key Features

Intention Broadcasting

• Dynamic Path Planning: Real-time path generation and adjustment
• Confidence-based Decision Making: Path confidence calculation and conflict resolution
• Emergency Route Planning: Automatic generation of emergency escape routes

Mesh Networking

• Dynamic Network Formation: Adaptive mesh network connections
• Network Health Monitoring: Real-time connectivity and density metrics
• Priority-based Communication: Multi-level priority system for message handling

Advanced Analytics

• Real-time Metrics: Comprehensive system performance monitoring
• Network Analysis: Detailed mesh network statistics
• Conflict Detection: Sophisticated collision risk assessment
• Visual Analytics: Rich visualization of system dynamics

🚀 Quick Start

Installation

# Clone the repository
cd intention-broadcast-system

# 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 ibs.network import IntentionBroadcastSimulation

# Create and run a simulation
sim = IntentionBroadcastSimulation(space_size=(50, 50, 30))
sim.run()

🎮 Advanced Usage

Custom Network Configuration

from ibs.network import IntentionBroadcastNetwork
import numpy as np

# Initialize the network
network = IntentionBroadcastNetwork(space_size=(50, 50, 30))

# Configure network parameters
network.broadcast_interval = 0.5
network.mesh_range = 15.0
network.collision_threshold = 5.0

# Add drones with priorities
network.add_drone(
    drone_id="emergency_1",
    position=np.array([10, 10, 5]),
    goal=np.array([40, 40, 25]),
    priority_level="emergency"
)

Priority Level Configuration

# Define custom priority levels
priority_config = {
    'emergency': 5,
    'medical': 4,
    'express': 3,
    'standard': 2,
    'flexible': 1
}

network = IntentionBroadcastNetwork(
    space_size=(50, 50, 30),
    priority_levels=priority_config
)

🔧 Technical Details

Core Components

1. Intention Broadcasting

   • Dynamic waypoint generation
   • Confidence calculation
   • Path risk assessment
   • Emergency route planning

2. Mesh Networking

   • Dynamic connection management
   • Network topology optimization
   • Priority-based message routing

3. Conflict Resolution

   • Collision detection and avoidance
   • Priority-based path adjustment
   • Emergency response triggers

Performance Optimization

• Efficient spatial queries using KD-trees
• Vectorized path calculations
• Optimized network updates
• Streamlined conflict detection

📊 Applications

• Urban Air Mobility: Manage dense drone traffic in urban environments
• Emergency Response: Coordinate emergency vehicle routing
• Logistics Operations: Optimize delivery fleet movements
• Search and Rescue: Coordinate multi-agent search patterns
• Event Coverage: Manage drone formations for event surveillance

📄 License

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

🙏 Acknowledgments

• Network algorithms inspired by modern mesh networking research
• Visualization components built on Matplotlib and NetworkX
• Advanced analytics powered by NumPy, Pandas, and SciPy
• Special thanks to all contributors and the open-source community

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