Szczegóły publikacji
Opis bibliograficzny
Biological basis of stigmergy as an inspiration for modeling the behavior of swarm robots / Andrzej KOT, Agata NAWROCKA // W: 27th International Carpathian Control Conference (ICCC) [Dokument elektroniczny] : 1-3 June 2026, Szilvásvárad, Hungary : proceedings. — Wersja do Windows. — Dane tekstowe. — Piscataway : IEEE, cop. 2026. — Print on Demand(PoD) ISBN: 979-8-3195-3321-0. — e-ISBN: 979-8-3195-3320-3. — S. 254–258. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 258, Abstr. — Publikacja dostępna online od: 2026-07-07
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 169800 |
|---|---|
| Data dodania do BaDAP | 2026-10-06 |
| Tekst źródłowy | URL |
| DOI | 10.1109/ICCC71363.2026.11593189 |
| Rok publikacji | 2026 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
Abstract
Stigmergy is one of the key mechanisms of self-organization observed in colonies of social insects and other biological systems in which individual interactions are mediated by environmental modifications. This phenomenon, based on indirect communication via chemical, physical, visual, or motor cues, enables the formation of complex collective structures and behaviors with minimal requirements for perception and communication between individuals. This article presents the biological basis of stigmergy and analyzes its application in swarm robotics, with particular emphasis on tasks such as route optimization, structure construction, spatial exploration, and movement coordination. A classification of stigmergy types and their equivalents in robotic systems is presented, encompassing solutions based on virtual pheromone fields and local structural patterns, as well as algorithms inspired by the dynamics of swarms and flocks. This comparison of nature and robotics reveals common organizational principles and differences resulting from technological limitations and safety requirements. The article highlights that integrating biological inspirations with formal mathematical models provides a foundation for designing scalable, autonomous, and resilient next-generation robotic systems.
