The hybrid bio-robotic swarm as a powerful tool for collective motion research: a perspective

Amir Ayali, Gal A. Kaminka

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Swarming or collective motion is ubiquitous in natural systems, and instrumental in many technological applications. Accordingly, research interest in this phenomenon is crossing discipline boundaries. A common major question is that of the intricate interactions between the individual, the group, and the environment. There are, however, major gaps in our understanding of swarming systems, very often due to the theoretical difficulty of relating embodied properties to the physical agents—individual animals or robots. Recently, there has been much progress in exploiting the complementary nature of the two disciplines: biology and robotics. This, unfortunately, is still uncommon in swarm research. Specifically, there are very few examples of joint research programs that investigate multiple biological and synthetic agents concomitantly. Here we present a novel research tool, enabling a unique, tightly integrated, bio-inspired, and robot-assisted study of major questions in swarm collective motion. Utilizing a quintessential model of collective behavior—locust nymphs and our recently developed Nymbots (locust-inspired robots)—we focus on fundamental questions and gaps in the scientific understanding of swarms, providing novel interdisciplinary insights and sharing ideas disciplines. The Nymbot-Locust bio-hybrid swarm enables the investigation of biology hypotheses that would be otherwise difficult, or even impossible to test, and to discover technological insights that might otherwise remain hidden from view.

Original languageEnglish
Article number1215085
JournalFrontiers in Neurorobotics
Volume17
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
Copyright © 2023 Ayali and Kaminka.

Keywords

  • bio-inspired
  • collective motion
  • locusts
  • robot collective behavior
  • vision-based
  • visual perception

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