EAGER: Collaborative Research: Creation of Active Granular Materials and Study of Emergent Properties

EAGER：合作研究：活性颗粒材料的创造和新特性的研究

• 批准号: 1933283
• 负责人: Daniel Goldman
• 金额: $ 15万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1933283&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Creation; Active

Non-Technical Abstract:Imagine on a hike that on command your walking stick becomes a chair, and should you fall and break your arm, the chair becomes a splint. Such taskable "super-materials" are the stuff of science fiction and will require drastic advances in material science. Such materials will depend not only on the properties of constituent elements, but also the emergent dynamics governing them. Research in programmable matter and swarm robotics is increasingly popular, but has largely been in the domain of engineers and computer scientists. The bulk of active matter studies in physics have been conducted theoretically, and often fails to adequately capture real-world physical interactions between elements which may critically influence the overall behavior. The proposed work will focus on a simple and accessible framework combining experiments, simulations, and theory via active granular materials (entangled robots), and explore distributed control schemes by which such materials can sense, locomote, and change properties and morphologies both locally and globally. Technical Abstract:Combining insights from swarm robotics and experimental and soft matter physics, this project will focus on the physics principles by which entangled robot swarms, or "active granular matter", can alter local mechanical states to produce global emergent behaviors; i.e. where the physical interactions dominate explicit coordination between robots. While active matter physics is a vibrant area of research, there are few well-controlled experimental systems in which theoretical advances can be tested in controlled situations, where parameters like material shape, density, and activity can be rapidly varied, and even fewer that incorporate the role of sensing and feedback into the dynamics. Therefore, to enable discovery of new dynamics in dense soft matter physics this project involves 1) a scalable, granular robotics platform with 100+ individuals; 2) theoretical and experimental tools for analysis and validation, such as hopper flows, shear cells, and numerical multi-physics simulations; and 3) distributed algorithms for producing and transitioning between emergent behaviors and material properties such as motion, morphology, porosity, and yield strength.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

非技术摘要：想象一下，在一次徒步旅行中，你的拐杖按命令变成了一把椅子，如果你摔倒摔断了胳膊，椅子就变成了夹板。这种有任务的“超级材料”是科幻小说的典范，需要材料科学的巨大进步。这样的材料不仅取决于组成元素的性质，还取决于支配它们的紧急动力。可编程物质和群体机器人的研究越来越受欢迎，但主要是在工程师和计算机科学家的领域。物理学中的大部分活性物质研究都是从理论上进行的，往往无法充分捕捉到现实世界中元素之间的物理相互作用，这些相互作用可能对整体行为产生关键影响。建议的工作将集中在一个简单和可访问的框架，通过主动颗粒材料(纠缠机器人)结合实验、模拟和理论，并探索分布式控制方案，通过该方案，此类材料可以感知、移动和改变局部和全局的性质和形态。技术摘要：这个项目结合了群体机器人学以及实验和软物质物理学的见解，将重点放在纠缠的机器人群体，也就是“活跃的颗粒物质”可以改变局部机械状态以产生全局紧急行为的物理原理上，即在物理相互作用主导机器人之间显性协调的情况下。虽然活性物质物理学是一个充满活力的研究领域，但很少有受控良好的实验系统可以在受控情况下测试理论进步，在受控情况下，材料形状、密度和活性等参数可以迅速变化，将传感和反馈的作用融入动力学的更少。因此，为了能够在密集软物质物理中发现新的动力学，这个项目涉及1)一个可扩展的粒状机器人平台，有100多个人；2)用于分析和验证的理论和实验工具，如漏斗流动、剪切单元和数值多物理模拟；以及3)用于产生紧急行为和材料特性(如运动、形态、孔隙度和屈服强度)之间转换的分布式算法。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Air-Fluidized Aggregates of Black Soldier fly Larvae

黑水虻幼虫的空气流化聚集体

• DOI: 10.3389/fphy.2021.734447
• 发表时间: 2021
• 期刊: Frontiers in Physics
• 影响因子: 3.1
• 作者: Ko, Hungtang;Cassidy, Grace J.;Shishkov, Olga;Aydin, Enes;Hu, David L.;Goldman, Daniel I.
• 通讯作者: Goldman, Daniel I.

Emergent Collective Locomotion in an Active Polymer Model of Entangled Worm Blobs

• DOI: 10.3389/fphy.2021.734499
• 发表时间: 2021-09-30
• 期刊: FRONTIERS IN PHYSICS
• 影响因子: 3.1
• 作者: Nguyen, Chantal;Ozkan-Aydin, Yasemin;Peleg, Orit
• 通讯作者: Peleg, Orit

Low rattling: A predictive principle for self-organization in active collectives

• DOI: 10.1126/science.abc6182
• 发表时间: 2021-01-01
• 期刊: SCIENCE
• 影响因子: 56.9
• 作者: Chvykov, Pavel;Berrueta, Thomas A.;England, Jeremy L.
• 通讯作者: England, Jeremy L.

Collective dynamics in entangled worm and robot blobs

• DOI: 10.1073/pnas.2010542118
• 发表时间: 2021-02-09
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Ozkan-Aydin, Yasemin;Goldman, Daniel, I;Bhamla, M. Saad
• 通讯作者: Bhamla, M. Saad