Swarm Squad

A simulation framework for multi-agent systems.

  

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✨ Key Features

1. Agent Simulation: Swarm Squad allows you to simulate the behavior of multiple agents in a controlled environment. This is essential for testing how agents interact with each other and their environment.

2. Scalability: It should be able to handle a large number of agents simultaneously. This is important for testing the system's performance under various load conditions.

3. Behavior Specification: Swarm Squad may include a way to define and specify the expected behavior of agents. This can be used to evaluate whether the agents are acting as intended.

4. Environment Modeling: It provides tools for creating and managing the environment in which the agents operate. This could be a physical or virtual space with obstacles, goals, or other entities.

5. Metrics and Analytics: The framework likely offers mechanisms for collecting and analyzing data on agent behavior. This could include metrics like speed, coordination, efficiency, or any other relevant performance indicators.

6. Customization and Extensibility: It should allow users to customize and extend the framework to suit their specific needs. This might involve adding new types of agents, modifying the environment, or defining custom evaluation criteria.

7. Visualization and Reporting: Swarm Squad may include tools for visualizing the simulation in real-time or for generating reports after a simulation run. This helps in understanding and communicating the results.

8. Integration with Other Tools: It may have the capability to integrate with other software or libraries commonly used in multi-agent systems development, such as reinforcement learning libraries, communication protocols, or visualization tools.

9. Support for Various Types of Agents: Swarm Squad should be versatile enough to support different types of agents, such as robots, drones, and autonomous vehicles.

10. Documentation and Support: Proper documentation and support resources are essential for users to effectively utilize the framework.

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🛠️ Setup & Installation

1. Clone the repository and navigate to project folder:

   git clone https://github.com/Sang-Buster/Swarm-Squad
   cd Swarm-Squad

2. Install uv first:

   # macOS/Linux
   curl -LsSf https://astral.sh/uv/install.sh | sh

   # Windows
   powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

3. Create a virtual environment at /weather-dashboard/.venv/:

   uv venv --python 3.12.1

4. Activate the virtual environment:

   # macOS/Linux
   source .venv/bin/activate

   # Windows
   .venv\Scripts\activate

5. Install the required packages:

   uv pip install -r requirements.txt

👨‍💻 Development Setup

1. Install pre-commit:

   uv pip install pre-commit

   Pre-commit helps maintain code quality by running automated checks before commits are made.

2. Install git hooks:

   pre-commit install --hook-type commit-msg --hook-type pre-commit --hook-type pre-push

   These hooks perform different checks at various stages:

   • commit-msg: Ensures commit messages follow the conventional format
   • pre-commit: Runs Ruff linting and formatting checks before each commit
   • pre-push: Performs final validation before pushing to remote

3. Code Linting:

   ruff check
   ruff format

4. Run the application:

   python src/app.py

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📅 Plan

• Use Plotly Dash, SQLite, and Mapbox to improve the simulator.
• Wrap into a local simulator not planning on deploying it to a web app as of now.

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📊 Dashboard

Agent List:

• Agent Name: (1, 2, 3, ...)
• Agent ID: (UUID)
• Agent Type: (quadcopter, fixed-wing, vehicle, robots)
• Status: (Connected/Disconnected)
• Mode: (Manual/Autonomous)
• Location: (Genera location name)
• Error/Alert Count: (1, 2, 3,...)
• Log: (log_file_name.txt)

Telemetry Data:

• Coordinates: (UTM/XYZ)
• Destination Coordiantes: (desired_coord)
• Altitude: (m)
• Pressue: (psi/kPa)
• Airspeed/Velocity: (kmh/mph)
• Acce leration: (m/s)
• Angular Velovity: (rad/s)
• Freq_IMU: (Hz)
• Freq_GPS: (Hz)
• Freq_Radio: (Hz)
• Battery Voltage/Current:
• Other sensor data: temperature, humidity, wind speed etc,.

Mission Details:

• Agent ID: (UUID)
• Status: (In Progress/Completed/Pending)
• Mission: (Task Name: Reaching to Destination/Avoiding/Stopped/Idling/Take-off/Land)
• Completion: (Percentage value for mission progress)
• Duration: (in seconds)

System Health and Alert:

• Battery Level: (%)
• GPS Accuracy: (%)
• Connection Strength/Quality: (dB/dBm)
• Communication Status: (Stable/Unstable/Lost)

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📝 File Structure

📦Swarm Squad
 ┣ 📂src                         // Source Website
 ┃ ┣ 📂assets
 ┃ ┣ 📂components
 ┃ ┣ 📂data
 ┃ ┣ 📄app.py
 ┃ ┣ 📄setup.py
 ┃ ┗ 📄__init__.py
 ┣ 📄.gitattributes
 ┣ 📄.gitignore
 ┗ 📄README.md