Bio-Inspired Energy Distribution for Programmable Matter
Bio-Inspired Energy Distribution for Programmable Matter
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DOI：
10.1145/3427796.3427835
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Joshua J. Daymude;A. Richa;Jamison Weber
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文献类型：
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作者：
Joshua J. Daymude;A. Richa;Jamison Weber
在有效的可编程物质系统中，单个模块需要持续的能量供应才能参与系统的集体行为。整个系统中的分销能量虽然努力解决了可编程硬件中电力管理的挑战方面在这项工作中，我们提出了一种在Amoebot模型中的能量分布的算法，该算法受到枯草芽孢杆菌生物膜的生长行为的启发。广播收到我们需要的营养。沟通以抑制饥饿的模块时的能量使用，使所有模块能够获得足够的能量来满足他们的需求，但我们扩展了Amoebot模型的跨越森林原始的跨度崩溃失败。有效地概括以前的工作，以考虑能量限制。
In systems of active programmable matter, individual modules require a constant supply of energy to participate in the system’s collective behavior. These systems are often powered by an external energy source accessible by at least one module and rely on module-to-module power transfer to distribute energy throughout the system. While much effort has gone into addressing challenging aspects of power management in programmable matter hardware, algorithmic theory for programmable matter has largely ignored the impact of energy usage and distribution on algorithm feasibility and efficiency. In this work, we present an algorithm for energy distribution in the amoebot model that is loosely inspired by the growth behavior of Bacillus subtilis bacterial biofilms. These bacteria use chemical signaling to communicate their metabolic states and regulate nutrient consumption throughout the biofilm, ensuring that all bacteria receive the nutrients they need. Our algorithm similarly uses communication to inhibit energy usage when there are starving modules, enabling all modules to receive sufficient energy to meet their demands. As a supporting but independent result, we extend the amoebot model’s well-established spanning forest primitive so that it self-stabilizes in the presence of crash failures. We conclude by showing how this self-stabilizing primitive can be leveraged to compose our energy distribution algorithm with existing amoebot model algorithms, effectively generalizing previous work to also consider energy constraints.