DSSE 1.1.7

The Drone Swarm Search project provides an environment for SAR missions built on PettingZoo, where agents, represented by drones, are tasked with locating targets identified as shipwrecked individuals.

Drone Swarm Search Environment (DSSE)

Welcome to the official GitHub repository for the Drone Swarm Search Environment (DSSE). This project is dedicated to providing a comprehensive simulation environment designed to develop, test, and refine drone swarm search strategies. Here, researchers and developers can access a versatile platform that supports a wide range of drone swarm simulations, enabling the exploration of complex behaviors and interactions within dynamic, real-world scenarios.

📚 Documentation Links

• Documentation Site: Access comprehensive documentation including tutorials, and usage examples for the Drone Swarm Search Environment (DSSE). Ideal for users seeking detailed information about the project's capabilities and how to integrate them into their own applications.

• Algorithm Details: Explore in-depth discussions and source code for the algorithms powering the DSSE. This section is perfect for developers interested in the technical underpinnings and enhancements of the search algorithms.

• PyPI Repository: Visit the PyPI page for DSSE to download the latest release, view release histories, and read additional installation instructions.

🎥 Visual Demonstrations

Above: A simulation showing how drones adjust their search pattern over a grid.

⚡Quick Start

⚙️ Installation

Install DSSE quickly with pip:

pip install DSSE

🎯 Outcome

If target is found	If target is not found

🛠️ Basic Env Usage

from DSSE import DroneSwarmSearch

env = DroneSwarmSearch(
    grid_size=40,
    render_mode="human",
    render_grid=True,
    render_gradient=True,
    vector=(1, 1),
    timestep_limit=300,
    person_amount=4,
    dispersion_inc=0.05,
    person_initial_position=(15, 15),
    drone_amount=2,
    drone_speed=10,
    probability_of_detection=0.9,
    pre_render_time=0,
)


def random_policy(obs, agents):
    actions = {}
    for agent in agents:
        actions[agent] = env.action_space(agent).sample()
    return actions


opt = {
    "drones_positions": [(10, 5), (10, 10)],
    "person_pod_multipliers": [0.1, 0.4, 0.5, 1.2],
}
observations, info = env.reset(options=opt)

rewards = 0
done = False
while not done:
    actions = random_policy(observations, env.get_agents())
    observations, rewards, terminations, truncations, infos = env.step(actions)
    done = any(terminations.values()) or any(truncations.values())

🛠️ Basic Coverage Usage

from DSSE import CoverageDroneSwarmSearch

env = CoverageDroneSwarmSearch(
    grid_size=40,
    drone_amount=3,
    dispersion_inc=0.1,
    vector=(1, 1),
    render_mode="human",
)

opt = {
    "drones_positions": [(0, 10), (10, 10), (20, 10)],
}
obs, info = env.reset(options=opt)

step = 0
while env.agents:
    step += 1
    actions = {agent: env.action_space(agent).sample() for agent in env.agents}
    observations, rewards, terminations, truncations, infos = env.step(actions)

print(infos["drone0"])

🆘 Support

If you encounter any issues or have questions, please file an issue on our GitHub issues page.

📖 How to cite this work

If you use this package, please consider citing it with this piece of BibTeX:

@misc{castanares2023dsse,
      title={DSSE: a drone swarm search environment}, 
      author={Manuel Castanares, Luis F. S. Carrete, Enrico F. Damiani, Leonardo D. M. de Abreu, José Fernando B. Brancalion and Fabrício J. Barth},
      year={2024},
      eprint={2307.06240},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      doi={https://doi.org/10.48550/arXiv.2307.06240}
}