Boldini Alain, Civitella Martina, Porfiri Maurizio
Department of Mechanical and Aerospace Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA.
Center for Urban Science and Progress, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA.
R Soc Open Sci. 2024 Sep 4;11(9):240845. doi: 10.1098/rsos.240845. eCollection 2024 Sep.
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.
自催化作用,即群体中的个体通过动态改变环境进行的间接通信,是动物群体的一种基本自组织机制。工程师们从自催化作用中获得灵感,以建立协调机器人群体以及机器人与动物混合群体的策略。目前,所有自催化作用模型都是算法形式的,表现为在个体层面实施的行为规则。理解自催化行为并将自催化作用转化为工程应用面临的一个关键挑战是,缺乏一种整体方法来确定环境的哪些改变对于实现群体的期望行为是必要的。在此,我们提出一个数学框架,该框架严格描述了环境改变与群体行为之间的关系。基于连续化技术最近取得的进展,我们将群体和环境改变建模为连续统。这种方法使我们能够设计出群体按期望方式行动所需的环境改变。通过对一维和二维示例的解析推导和数值模拟,我们表明我们的框架能得出实现期望队形所需的轨迹分布。这种方法为从机器人群体到机器人与动物混合群体等不同实施平台提供了一个可适应的框架。