Stigmergy: from mathematical modelling to control.

IF 2.9 3区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Royal Society Open Science Pub Date : 2024-09-04 eCollection Date: 2024-09-01 DOI:10.1098/rsos.240845
Alain Boldini, Martina Civitella, Maurizio Porfiri
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Abstract

Stigmergy, the indirect communication between agents of a swarm through dynamic environmental modifications, is a fundamental self-organization mechanism of animal swarms. Engineers have drawn inspiration from stigmergy to establish strategies for the coordination of swarms of robots and of mixed societies of robots and animals. Currently, all models of stigmergy are algorithmic, in the form of behavioural rules implemented at an individual level. A critical challenge for the understanding of stigmergic behaviour and translation of stigmergy to engineering is the lack of a holistic approach to determine which modifications of the environment are necessary to achieve desired behaviours for the swarm. Here, we propose a mathematical framework that rigorously describes the relationship between environmental modifications and swarm behaviour. Building on recent strides in continuification techniques, we model the swarm and environmental modifications as continua. This approach allows us to design the environmental modifications required for the swarm to behave as desired. Through analytical derivations and numerical simulations of one- and two-dimensional examples, we show that our framework yields the distribution of traces required to achieve a desired formation. Such an approach provides an adaptable framework for different implementation platforms, from robotic swarms to mixed societies of robots and animals.

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Stigmergy: from mathematical modelling to control.
群聚力(Stigmergy)是指群聚体之间通过动态环境变化进行的间接交流,是动物群的一种基本自组织机制。工程师们从 "滞空 "中汲取灵感,为机器人群和机器人与动物混合群的协调制定了策略。目前,所有的滞空模型都是算法模型,即在个体层面上实施的行为规则。要理解呆滞行为并将呆滞转化为工程技术,一个关键的挑战是缺乏一种整体方法来确定哪些环境变化是实现机器人群所需行为所必需的。在这里,我们提出了一个数学框架,它能严格描述环境改变与蜂群行为之间的关系。基于最近在连续化技术方面取得的进展,我们将蜂群和环境改变作为连续体建模。通过这种方法,我们可以设计蜂群行为所需的环境变化。通过对一维和二维实例的分析推导和数值模拟,我们表明,我们的框架可以得出实现理想队形所需的痕迹分布。这种方法为从机器人群到机器人与动物混合群等不同的实施平台提供了一个适应性强的框架。
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来源期刊
Royal Society Open Science
Royal Society Open Science Multidisciplinary-Multidisciplinary
CiteScore
6.00
自引率
0.00%
发文量
508
审稿时长
14 weeks
期刊介绍: Royal Society Open Science is a new open journal publishing high-quality original research across the entire range of science on the basis of objective peer-review. The journal covers the entire range of science and mathematics and will allow the Society to publish all the high-quality work it receives without the usual restrictions on scope, length or impact.
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