Master data

Title: A Review of Swarmalators and Their Potential in Bio-Inspired Computing
Subtitle:
Abstract:

From fireflies to heart cells, many systems in Nature show the remarkable ability to spontaneously fall into synchrony. By imitating Nature's success at self-synchronizing, scientists have designed cost-effective methods to achieve synchrony in the lab, with applications ranging from wireless sensor networks to radio transmission. A similar story has occurred in the study of swarms, where inspiration from the behavior flocks of birds and schools of fish has led to low footprint algorithms for multi-robot systems. Here, we continue this `bio-inspired' tradition, by speculating on the technological benefit of fusing swarming with synchronization. The subject of recent theoretical work, minimal models of so called `swarmalator' systems exhibit rich spatiotemporal patterns, hinting at utility in `bottom-up' robotic swarms. We review the theoretical work on swarmalators, identify possible realizations in Nature, and discuss their potential applications in technology.

Keywords: swarmalator, synchronization, swarming, self-organization, dynamic system, biologically inspired, robotics, swarm robotics
Publication type: Article in Proceedings (Authorship)
Publication date: 13.05.2019 (Print)
Published by: Proc. SPIE Micro- and Nanotechnology Sensors, Systems, and Applications
Proc. SPIE Micro- and Nanotechnology Sensors, Systems, and Applications
to publication
 ( SPIE; )
Title of the series: -
Volume number: 10982
First publication: Yes
Version: -
Page: p. 109822E
Cover: Cover

Versionen

Keine Version vorhanden
Publication date:
ISBN (e-book): -
eISSN: -
DOI: http://dx.doi.org/10.1117/12.2518682
Homepage: -
Open access
  • Available online (not open access)
Publication date: 13.05.2019
ISBN: -
ISSN: -
Homepage: https://doi.org/10.1117/12.2518682

Assignment

Organisation Address
Fakultät für Technische Wissenschaften
 
Institut für Vernetzte und Eingebettete Systeme
Universitätsstraße 65-67
9020 Klagenfurt am Wörthersee
Austria
  -993640
   kornelia.lienbacher@aau.at
https://nes.aau.at/
To organisation
Universitätsstraße 65-67
AT - 9020  Klagenfurt am Wörthersee

Categorisation

Subject areas
  • 101027 - Dynamical systems
  • 101028 - Mathematical modelling
  • 101019 - Stochastics
  • 211905 - Bionics
  • 202022 - Information technology
  • 202035 - Robotics
  • 202041 - Computer engineering
Research Cluster
  • Self-organizing systems
Peer reviewed
  • Yes
Publication focus
  • Science to Science (Quality indicator: II)
Classification raster of the assigned organisational units:
working groups
  • Mobile Systems Group

Cooperations

Organisation Address
Massachusetts Institute of Technology
77 Massachusetts Avenue
United States of America
77 Massachusetts Avenue
US  
Massachusetts Institute of Technology, Sensable City Lab
77 Massachusetts Avenue
02139 Cambridge, MA
United States of America
77 Massachusetts Avenue
US - 02139  Cambridge, MA

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