Aggregate computing is a paradigm with over a decade of investigation and multiple programming frameworks available, which proved to be particularly suitable for the simulation of applications in challenging domains such as smart cities and robot swarms. This paper introduces a toolchain for practical multi-robot demonstrations based on aggregate computing principles, and validates it with a live interactive demo in an open-public event in the context of the European Researchers’ Night. More specifically, we show how we coordinated a team of mobile robots to form spatial patterns. We discuss the practical demonstration performed in an indoor environment, which exploits a camera system and ArUco markers for localization.
Aguzzi, G., Bacchini, L., Baiardi, M., Casadei, R., Cortecchia, A., Domini, D., et al. (2025). A Demonstrator for Self-organizing Robot Teams. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND : Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-95589-1_12].
A Demonstrator for Self-organizing Robot Teams
Aguzzi G.
;Baiardi M.;Casadei R.;Cortecchia A.;Domini D.;Farabegoli N.;Pianini D.;Viroli M.
2025
Abstract
Aggregate computing is a paradigm with over a decade of investigation and multiple programming frameworks available, which proved to be particularly suitable for the simulation of applications in challenging domains such as smart cities and robot swarms. This paper introduces a toolchain for practical multi-robot demonstrations based on aggregate computing principles, and validates it with a live interactive demo in an open-public event in the context of the European Researchers’ Night. More specifically, we show how we coordinated a team of mobile robots to form spatial patterns. We discuss the practical demonstration performed in an indoor environment, which exploits a camera system and ArUco markers for localization.| File | Dimensione | Formato | |
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paper_26.pdf
embargo fino al 17/06/2026
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Postprint / Author's Accepted Manuscript (AAM) - versione accettata per la pubblicazione dopo la peer-review
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7.49 MB
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