Impact of the Sampling Time on the Aggregation of Robotic Swarms through Informed Robots

Antoine SION, Andreagiovanni Reina, Mauro Birattari, Elio Tuci

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution


Self-organised aggregation is one of the basic collective behaviours studied in swarm robotics. In this paper, we investigate an aggregation problem occurring on two different sites. Previous studies have shown that a minority of robots, informed about the site on which they have to aggregate, can control the final distribution of the entire robot swarm on the sites. We reproduce this strategy by adapting the previous probabilistic finite-state machine to a new simulated robotic platform: the Kilobot. Our simulation results highlight that the update time (i.e., the amount of time a robot waits before making a decision on leaving a site) impacts the dynamics of the aggregation process. Namely, a longer update time lowers the number of robots wandering in the arena, but can slow down the dynamics when the target final distribution is far from the one initially formed. To ensure a low number of wandering robots while maintaining a quick convergence towards the target final distribution of the swarm, we introduce the concept of a dynamic update time increasing during the aggregation process.
Original languageEnglish
Title of host publicationProceedings of the 16th International Conference on the Simulation of Adaptive Behavior
Subtitle of host publicationSAB2022
EditorsLola Canamero, Philippe Gaussier, Myra Wilson, Sofiane Baucenna, Nicolas Couperlier
Number of pages12
ISBN (Electronic)978-3-031-16770-6
ISBN (Print)978-3-031-16769-0
Publication statusPublished - Sept 2022
Event16th International Conference on Simulation of Adaptive Behavior - Cergy-Pontoise, France
Duration: 20 Sept 202223 Sept 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13499 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Conference on Simulation of Adaptive Behavior
Abbreviated titleSAB 2022


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