Welcome to the official GitHub repository for the Drone Swarm Search Environment (DSSE). This project offers a comprehensive simulation platform designed for developing, testing, and refining search strategies using drone swarms. Researchers and developers will find a versatile toolset supporting a broad spectrum of simulations, which facilitates the exploration of complex drone behaviors and interactions in dynamic, real-world scenarios.
In this repository, we have implemented two distinct types of environments. The first is a dynamic environment that simulates maritime search and rescue operations for shipwreck survivors. It models the movement of individuals in the sea using a dynamic probability matrix, with the objective for drones being to locate and identify these individuals. The second is a environment utilizing the Lagrangian particle simulation from the open-source Opendrift library, which incorporates real-world ocean and wind data to create a probability matrix for drone SAR tasks. In this scenario, drones are tasked with covering the full search area within the lowest time possible, while prioritizing higher probability areas.
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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.
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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.
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PyPI Repository: Visit the PyPI page for DSSE to download the latest release, view release histories, and read additional installation instructions.
Above: A simulation showing how drones adjust their search pattern over a grid.
| If target is found | If target is not found |
|---|---|
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Quickly install DSSE using pip:
pip install DSSEfrom 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],
"vector": (0.3, 0.3),
}
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())
Above: A simulation showing how drones adjust their search pattern over a grid.
Install DSSE with coverage support using pip:
pip install DSSE[coverage]from DSSE import CoverageDroneSwarmSearch
env = CoverageDroneSwarmSearch(
drone_amount=3,
render_mode="human",
disaster_position=(-24.04, -46.17), # (lat, long)
pre_render_time=10, # hours to simulate
)
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"])If you encounter any issues or have questions, please file an issue on our GitHub issues page.
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}
}
