🛸 SWARM_X: Autonomous Airspace Management Engine

"The Roads of the Future are in the Sky."

📜 Executive Summary

Swarm_X is a high-fidelity Unmanned Traffic Management (UTM) simulation engine designed to solve the "Last Mile" logistics problem for autonomous drone swarms.

Unlike traditional centralized controllers, Swarm_X utilizes Decentralized Swarm Intelligence (Boids Algorithm), A Pathfinding*, and Vector-Based Physics to manage mixed-priority agents in a shared 3D airspace without collisions. The system features a tactical "Dark Mode" radar interface and creates a complex urban environment to stress-test autonomous decision-making.

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🧠 Core Architecture

The system is built on a modular "Engine-First" philosophy, prioritizing mathematical accuracy over game mechanics.

1. Physics Kernel (core/physics.py)

• Vector Dynamics: Calculates Inertia, Drag, and Thrust vectors for every agent independently.
• Drift Model: Implements Euler Integration to simulate organic drift and momentum; drones do not stop instantly, they decelerate.

2. Swarm Logic (core/boids.py)

• Separation: Agents actively repel neighbors to maintain safe flight distance.
• Alignment: Squads synchronize velocity vectors to fly in formation.
• Cohesion: Drones tend to stay within signal range of their local cluster.

3. Priority & Routing (core/pathfinding.py)

The engine implements a dynamic Impact Hierarchy to resolve right-of-way conflicts:

• 🔴 Medical Drones: Highest Priority. They trigger a "Green Corridor," forcing other agents to yield.
• 🟡 Military Drones: High Velocity, aggressive pathing.
• ⚪ Commercial Drones: Fuel-efficient pathing, yields to all others.

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🛠️ Built With

Software Stack

• Python 3.10+: Core Simulation Logic.
• Pygame: Real-time rendering engine (60 FPS).
• NumPy: High-performance vector mathematics and matrix operations.

Tools

• Custom Map Editor: Built-in tool to trace real-world building layouts and save them as collision maps.

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🕹️ Controls & Usage

Input	Action
Left Click	Set Target: The swarm will recalculate A* paths to converge on this location.
Right Click	Deploy Agent: Spawns a new autonomous drone into the airspace.
Spacebar	Emergency Stop: Freezes simulation state (Debug Mode).
S Key	Save Map: (In Editor Mode) Saves current obstacles to city_map.pkl.

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🚀 Installation

1. Clone the Repo:

   git clone [https://github.com/YourUsername/Swarm_X.git](https://github.com/YourUsername/Swarm_X.git)
   cd Swarm_X

2. Install Dependencies:

   pip install pygame numpy

3. Run the Engine:

   python main.py

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🔮 Future Roadmap (Upcoming V2.0)

We are currently developing a Cyber-Physical System update to bridge the gap between the simulation and the real world.

• Hardware-in-the-Loop (HITL): Integrating an ESP32 microcontroller to receive real-time telemetry from the Python engine.
• Physical Dashboard: Using an OLED Display and Status LEDs to show mission status (Medical/Military/Commercial) physically on the user's desk.
• Collision Alarm: Active buzzer integration to sound alerts when a simulation crash occurs.
• Energy Anxiety: Implementing battery drain logic requiring drones to abandon missions for charging.

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##youtube video:="https://youtu.be/48npqVH9bZs?si=ENRzs_GT30HXWPzE"

👥 Author

• [Raghav Dhingra] - Lead Developer

"Code determines the behavior, but Physics determines the destiny."

🛠️ Installation (Dev Mode)

# 1. Clone the secure repository
git clone [https://github.com/YOUR_USERNAME/Swarm_X.git](https://github.com/YOUR_USERNAME/Swarm_X.git)

# 2. Initialize the environment
cd Swarm_X
python -m venv venv
source venv/bin/activate  # (Windows: venv\Scripts\activate)

# 3. Install High-Performance dependencies
pip install -r requirements.txt

# 4. Launch the Radar
python main.py