DSSE 1.0.3

An environment to train drones to search and find a shipwrecked person lost in the ocean using reinforcement learning.

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 drone is found	If drone 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 Covarage 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={Jorás Oliveira, Pedro Andrade, Ricardo Rodrigues, Renato Laffranchi,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}
}