TY - GEN
T1 - Group-Size Regulation in Self-organized Aggregation in Robot Swarms
AU - Firat, Ziya
AU - Ferrante, Eliseo
AU - Zakir, Raina
AU - Prasetyo, Judhi
AU - Tuci, Elio
PY - 2020
Y1 - 2020
N2 - In swarm robotics, self-organized aggregation refers to a collective process in which robots form a single aggregate in an arbitrarily chosen aggregation site among those available in the environment, or just in an arbitrarily chosen location. Instead of focusing exclusively on the formation of a single aggregate, in this study we discuss how to design a swarm of robots capable of generating a variety of final distributions of the robots to the available aggregation sites. We focus on an environment with two possible aggregation sites, A and B. Our study is based on the following working hypothesis: robots distribute on site A and B in quantities that reflect the relative proportion of robots in the swarm that selectively avoid A with respect to those that selectively avoid B. This is with an as minimal as possible proportion of robots in the swarm that selectively avoid one or the other site. We illustrate the individual mechanisms designed to implement the above mentioned working hypothesis, and we discuss the promising results of a set of simulations that systematically consider a variety of experimental conditions.
AB - In swarm robotics, self-organized aggregation refers to a collective process in which robots form a single aggregate in an arbitrarily chosen aggregation site among those available in the environment, or just in an arbitrarily chosen location. Instead of focusing exclusively on the formation of a single aggregate, in this study we discuss how to design a swarm of robots capable of generating a variety of final distributions of the robots to the available aggregation sites. We focus on an environment with two possible aggregation sites, A and B. Our study is based on the following working hypothesis: robots distribute on site A and B in quantities that reflect the relative proportion of robots in the swarm that selectively avoid A with respect to those that selectively avoid B. This is with an as minimal as possible proportion of robots in the swarm that selectively avoid one or the other site. We illustrate the individual mechanisms designed to implement the above mentioned working hypothesis, and we discuss the promising results of a set of simulations that systematically consider a variety of experimental conditions.
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U2 - 10.1007/978-3-030-60376-2_26
DO - 10.1007/978-3-030-60376-2_26
M3 - Conference contribution
AN - SCOPUS:85096473682
SN - 9783030603755
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 315
EP - 323
BT - Swarm Intelligence
A2 - Dorigo, Marco
A2 - Stützle, Thomas
A2 - Blesa, Maria J.
A2 - Blum, Christian
A2 - Hamann, Heiko
A2 - Heinrich, Mary Katherine
A2 - Strobel, Volker
PB - Springer Science and Business Media Deutschland GmbH
T2 - 12th International Conference on Swarm Intelligence, ANTS 2020
Y2 - 26 October 2020 through 28 October 2020
ER -