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Guy Theraulaz

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

IROS Conference 2023 Conference Paper

Bio-Inspired 3D Flocking Algorithm with Minimal Information Transfer for Drones Swarms

Matthieu Verdoucq
Clément Sire
Ramón Escobedo
Guy Theraulaz
Gautier Hattenberger

This article introduces a bio-inspired 3D flocking algorithm for a drone swarm, built upon a previously established 2D model, which has proven to be effective in promoting stability, alignment, and distance variation between agents within large groups of agents. The study highlights how the incorporation of a vertical interaction between agents and the acquisition by each agent of a minimal amount of information about their most influential neighbor impacts the collective behavior of the swarm. Additionally, we present a comprehensive investigation of the impacts of the intensity of alignment and attraction interactions on the collective motion patterns that emerge at the group level. These results, mostly conducted in a validated simulator, have significant implications for designing efficient UAV swarm systems and using collective patterns, or phases, in operational contexts such as corridor tracking, surveillance, and exploration. Further research will explore the effectiveness and efficiency of this UAV swarm flocking algorithm, as well as its ability to ensure safe transitions between collective phases in different operational contexts.

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ICRA Conference 2013 Conference Paper

Human-friendly robot navigation in dynamic environments

Jérôme Guzzi
Alessandro Giusti
Luca Maria Gambardella
Guy Theraulaz
Gianni A. Di Caro

The vision-based mechanisms that pedestrians in social groups use to navigate in dynamic environments, avoiding obstacles and each others, have been subject to a large amount of research in social anthropology and biological sciences. We build on recent results in these fields to develop a novel fully-distributed algorithm for robot local navigation, which implements the same heuristics for mutual avoidance adopted by humans. The resulting trajectories are human-friendly, because they can intuitively be predicted and interpreted by humans, making the algorithm suitable for the use on robots sharing navigation spaces with humans. The algorithm is computationally light and simple to implement. We study its efficiency and safety in presence of sensing uncertainty, and demonstrate its implementation on real robots. Through extensive quantitative simulations we explore various parameters of the system and demonstrate its good properties in scenarios of different complexity. When the algorithm is implemented on robot swarms, we could observe emergent collective behaviors similar to those observed in human crowds.

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