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.
|Title of host publication||Swarm Intelligence|
|Subtitle of host publication||12th International Conference, ANTS 2020, Barcelona, Spain, October 26–28, 2020, Proceedings|
|Editors||Marco Dorigo, Thomas Stutzle, Maria J. Blesa Aguilera, Christian Blum, Heiko Hamann, Mary K. Heinrich, Volker Strobel|
|Publication status||Published - 2020|
|Name||Lecture Notes in Computer Science|