Dans ce papier, nous étudions des modèles de flocking existants et proposons des extensions afin d’améliorer leurs performances dans des environnements ayant des obstacles impactant les communications ainsi que les trajectoires des agents. En effet, les contraintes imposées par les obstacles sont généralement la cause de coupures de communication menant souvent à la séparation de la flotte en plusieurs clusters. Dans ce contexte, nous étendons deux modèles standards afin de renforcer leurs capacités à rester connectés dans des environnements avec différentes distributions d’obstacles. En tenant compte de la propagation radio, nous modélisons comment les obstacles impactent les communications dans un simulateur que nous utilisons notamment pour optimiser les paramètres du flocking. Les résultats des simulations montrent l’efficacité des modèles proposés et la façon dont ils s’adaptent à ces nouvelles contraintes environnementales.
In this paper, we study existing flocking models and propose extensions to improve their abilities to deal with environments having obstacles impacting the communication quality as well as the trajectories of the agents. Indeed, the constraints induced by the obstacles usually lead to communication outages resulting in the separation of the flock in multiple clusters. In this context, we extend two standard models to improve their ability to stay connected while evolving in environments with different obstacles distributions. By taking into account the radio propagation, we model the obstacles’ impact on communications in a simulator that we use to optimize flocking parameters. The simulation results show the efficiency of the proposed models and how they adapt to different environmental constraints.
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Keywords: Flocking models, swarm robotics, simulation of commmunications
CC-BY 4.0
@article{ROIA_2023__4_2_123_0,
author = {Alexandre Bonnefond and Olivier Simonin and Isabelle Gu\'erin-Lassous},
title = {Mod\`eles de {Flocking} {Adapt\'es} aux {Environnements} avec {Obstacles} et {Communications} {D\'egrad\'ees}},
journal = {Revue Ouverte d'Intelligence Artificielle},
pages = {123--145},
publisher = {Association pour la diffusion de la recherche francophone en intelligence artificielle},
volume = {4},
number = {2},
year = {2023},
doi = {10.5802/roia.59},
language = {fr},
url = {https://roia.centre-mersenne.org/articles/10.5802/roia.59/}
}
TY - JOUR AU - Alexandre Bonnefond AU - Olivier Simonin AU - Isabelle Guérin-Lassous TI - Modèles de Flocking Adaptés aux Environnements avec Obstacles et Communications Dégradées JO - Revue Ouverte d'Intelligence Artificielle PY - 2023 SP - 123 EP - 145 VL - 4 IS - 2 PB - Association pour la diffusion de la recherche francophone en intelligence artificielle UR - https://roia.centre-mersenne.org/articles/10.5802/roia.59/ DO - 10.5802/roia.59 LA - fr ID - ROIA_2023__4_2_123_0 ER -
%0 Journal Article %A Alexandre Bonnefond %A Olivier Simonin %A Isabelle Guérin-Lassous %T Modèles de Flocking Adaptés aux Environnements avec Obstacles et Communications Dégradées %J Revue Ouverte d'Intelligence Artificielle %D 2023 %P 123-145 %V 4 %N 2 %I Association pour la diffusion de la recherche francophone en intelligence artificielle %U https://roia.centre-mersenne.org/articles/10.5802/roia.59/ %R 10.5802/roia.59 %G fr %F ROIA_2023__4_2_123_0
Alexandre Bonnefond; Olivier Simonin; Isabelle Guérin-Lassous. Modèles de Flocking Adaptés aux Environnements avec Obstacles et Communications Dégradées. Revue Ouverte d'Intelligence Artificielle, Volume 4 (2023) no. 2, pp. 123-145. doi : 10.5802/roia.59. https://roia.centre-mersenne.org/articles/10.5802/roia.59/
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