Beyond Bio-Inspired Robotics: How Multi-Robot Systems Can Support Research on Collective Animal Behavior

In the study of collective animal behavior, researchers usually rely on gathering empirical data from animals in the wild. While the data gathered can be highly accurate, researchers have limited control over both the test environment and the agents under study. Further aggravating the data gatherin...

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Main Authors: Nikolaj Horsevad, Hian Lee Kwa, Roland Bouffanais
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-06-01
Series:Frontiers in Robotics and AI
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/frobt.2022.865414/full
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author Nikolaj Horsevad
Hian Lee Kwa
Hian Lee Kwa
Roland Bouffanais
author_facet Nikolaj Horsevad
Hian Lee Kwa
Hian Lee Kwa
Roland Bouffanais
author_sort Nikolaj Horsevad
collection DOAJ
description In the study of collective animal behavior, researchers usually rely on gathering empirical data from animals in the wild. While the data gathered can be highly accurate, researchers have limited control over both the test environment and the agents under study. Further aggravating the data gathering problem is the fact that empirical studies of animal groups typically involve a large number of conspecifics. In these groups, collective dynamics may occur over long periods of time interspersed with excessively rapid events such as collective evasive maneuvers following a predator’s attack. All these factors stress the steep challenges faced by biologists seeking to uncover the fundamental mechanisms and functions of social organization in a given taxon. Here, we argue that beyond commonly used simulations, experiments with multi-robot systems offer a powerful toolkit to deepen our understanding of various forms of swarming and other social animal organizations. Indeed, the advances in multi-robot systems and swarm robotics over the past decade pave the way for the development of a new hybrid form of scientific investigation of social organization in biology. We believe that by fostering such interdisciplinary research, a feedback loop can be created where agent behaviors designed and tested in robotico can assist in identifying hypotheses worth being validated through the observation of animal collectives in nature. In turn, these observations can be used as a novel source of inspiration for even more innovative behaviors in engineered systems, thereby perpetuating the feedback loop.
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spelling doaj.art-fa174f9aeabb443cabd8e6fe6cfc779d2022-12-22T00:24:56ZengFrontiers Media S.A.Frontiers in Robotics and AI2296-91442022-06-01910.3389/frobt.2022.865414865414Beyond Bio-Inspired Robotics: How Multi-Robot Systems Can Support Research on Collective Animal BehaviorNikolaj Horsevad0Hian Lee Kwa1Hian Lee Kwa2Roland Bouffanais3University of Ottawa, Ottawa, ON, CanadaSingapore University of Technology and Design, Singapore, SingaporeThales Solutions Asia, Singapore, SingaporeUniversity of Ottawa, Ottawa, ON, CanadaIn the study of collective animal behavior, researchers usually rely on gathering empirical data from animals in the wild. While the data gathered can be highly accurate, researchers have limited control over both the test environment and the agents under study. Further aggravating the data gathering problem is the fact that empirical studies of animal groups typically involve a large number of conspecifics. In these groups, collective dynamics may occur over long periods of time interspersed with excessively rapid events such as collective evasive maneuvers following a predator’s attack. All these factors stress the steep challenges faced by biologists seeking to uncover the fundamental mechanisms and functions of social organization in a given taxon. Here, we argue that beyond commonly used simulations, experiments with multi-robot systems offer a powerful toolkit to deepen our understanding of various forms of swarming and other social animal organizations. Indeed, the advances in multi-robot systems and swarm robotics over the past decade pave the way for the development of a new hybrid form of scientific investigation of social organization in biology. We believe that by fostering such interdisciplinary research, a feedback loop can be created where agent behaviors designed and tested in robotico can assist in identifying hypotheses worth being validated through the observation of animal collectives in nature. In turn, these observations can be used as a novel source of inspiration for even more innovative behaviors in engineered systems, thereby perpetuating the feedback loop.https://www.frontiersin.org/articles/10.3389/frobt.2022.865414/fullcollective animal behaviorcollective decision-makingcollective roboticsmulti-robot systemsswarm intelligenceself-organization
spellingShingle Nikolaj Horsevad
Hian Lee Kwa
Hian Lee Kwa
Roland Bouffanais
Beyond Bio-Inspired Robotics: How Multi-Robot Systems Can Support Research on Collective Animal Behavior
Frontiers in Robotics and AI
collective animal behavior
collective decision-making
collective robotics
multi-robot systems
swarm intelligence
self-organization
title Beyond Bio-Inspired Robotics: How Multi-Robot Systems Can Support Research on Collective Animal Behavior
title_full Beyond Bio-Inspired Robotics: How Multi-Robot Systems Can Support Research on Collective Animal Behavior
title_fullStr Beyond Bio-Inspired Robotics: How Multi-Robot Systems Can Support Research on Collective Animal Behavior
title_full_unstemmed Beyond Bio-Inspired Robotics: How Multi-Robot Systems Can Support Research on Collective Animal Behavior
title_short Beyond Bio-Inspired Robotics: How Multi-Robot Systems Can Support Research on Collective Animal Behavior
title_sort beyond bio inspired robotics how multi robot systems can support research on collective animal behavior
topic collective animal behavior
collective decision-making
collective robotics
multi-robot systems
swarm intelligence
self-organization
url https://www.frontiersin.org/articles/10.3389/frobt.2022.865414/full
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