Collaborative Research: RI: Medium: Living Architectures: From Army Ants to Self-Assembling Robot Swarms

合作研究:RI:媒介:活体建筑:从行军蚂蚁到自组装机器人群

基本信息

  • 批准号:
    1956019
  • 负责人:
  • 金额:
    $ 50万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-08-01 至 2024-07-31
  • 项目状态:
    已结题

项目摘要

Army ants are unlike any other ant species. They are nomadic and therefore do not build permanent nests. Instead, between two episodes of sometimes daily migration, they build temporary nests called “bivouacs” by attaching themselves to each other. In this way they build functional structures solely out of their interconnected bodies. Army ants can assemble and disassemble bivouacs of up to a million individuals in less than an hour and can do so in almost any condition in which they find themselves. These incredible feats of constructions are of great interest for engineers looking to create swarms of robots that could autonomously assemble themselves anywhere into any desired shape. Similar swarms could, for instance, self-assemble into habitats on Mars or in disaster areas without human intervention and regardless of the state of the local environment. This collaborative research brings together biologists and roboticists to achieve two goals: understand the principles of self-assembling construction in army ants, and adapt these principles to create a new generation of robots capable of self-assembling into any desired functional structure, even in unpredictable environments. The project will also give students from K12 to Ph.D. an opportunity to learn how biological structures build themselves out of smaller units, and how fundamental knowledge of natural processes can lead to new technological developments and applications in engineering. The project has three complementary components. In Component 1, the researchers will perform field experiments to determine the rules used by army ants to self-assemble into functional structures. These studies will combine computer vision-assisted behavioral observations to measure the individual behaviors of the ants and high-definition 3D imaging using a custom-designed CT-scanner to characterize the organization and dynamics of the structure under construction. In Component 2, the result of the field experiments will be used to generate a multi-agent mathematical model and a physics-based simulation of the ant behaviors. The focus will be to design generalizable agent abstractions that allow for mathematical analysis to determine what forms of individual rules lead to correct and efficient collective outcomes as determined by the functional goal (e.g. formation of bivouac), and how modification of individual sensing and coordination capabilities affect the colony capability. Finally, in Component 3, the researchers will design a self-assembling robotic swarm with at least 30 robots. The swarm will be capable of building functional structures in unknown environments, through climbing and attaching to each other. These robots will use similar principles as army ants for collective control and allow complex 3D “organic” self-assembled structures. The goal is not to mimic ant morphology, but instead demonstrate novel robot designs that use embodied intelligence and bio-inspired control, to achieve similarly adaptive structures while also allowing simplicity and large-scale manufacturability.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.
行军蚁不同于其他任何种类的蚂蚁。它们是游牧民族,因此不会建造永久的巢穴。相反,在有时每天迁徙的两个阶段之间,它们通过相互依附来建立临时的巢穴,称为“露营”。通过这种方式,它们仅从相互连接的身体中构建功能结构。行军蚁可以在不到一个小时的时间里组装和拆卸多达100万只的露营地,而且几乎可以在任何情况下这样做。这些令人难以置信的建造壮举引起了工程师的极大兴趣,他们希望制造出能够在任何地方自主组装成任何所需形状的机器人群。例如,类似的蜂群可以在没有人类干预的情况下自我组装成火星或灾区的栖息地,而不管当地环境的状况如何。这项合作研究汇集了生物学家和机器人学家,以实现两个目标:了解军队蚂蚁自组装结构的原理,并调整这些原理以创建新一代机器人,即使在不可预测的环境中,也能够自组装成任何所需的功能结构。该项目还将为从K12到博士的学生提供一个学习生物结构如何从更小的单元中构建自己的机会,以及自然过程的基础知识如何导致新的技术发展和工程应用。该项目有三个相辅相成的组成部分。在组件1中,研究人员将进行实地实验,以确定军蚁自组装成功能结构所使用的规则。这些研究将结合联合收割机计算机视觉辅助行为观察来测量蚂蚁的个体行为,并使用定制设计的CT扫描仪进行高清3D成像,以表征正在建设的结构的组织和动态。在组件2中,现场实验的结果将用于生成多代理数学模型和基于物理的蚂蚁行为模拟。重点将是设计可推广的代理抽象,允许数学分析,以确定什么形式的个人规则导致正确和有效的集体结果所确定的功能目标(如形成露营地),以及如何修改个人的传感和协调能力影响殖民地的能力。最后,在组件3中,研究人员将设计一个至少有30个机器人的自组装机器人群。蜂群将能够在未知的环境中通过攀爬和相互附着来构建功能结构。这些机器人将使用与行军蚁相似的原理进行集体控制,并允许复杂的3D“有机”自组装结构。该奖项的目标不是模仿蚂蚁形态,而是展示使用具身智能和生物启发控制的新型机器人设计,以实现类似的自适应结构,同时也允许简单和大规模的可制造性。该奖项反映了NSF的法定使命,并通过使用基金会的智力价值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

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
FireAntV3: A Modular Self-Reconfigurable Robot Toward Free-Form Self-Assembly Using Attach-Anywhere Continuous Docks
FireAntV3:模块化自重构机器人,使用随处附着连续坞站实现自由形式自组装
  • DOI:
    10.1109/lra.2023.3290796
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    5.2
  • 作者:
    Swissler, Petras;Rubenstein, Michael
  • 通讯作者:
    Rubenstein, Michael
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Michael Rubenstein其他文献

FireAnt: A Modular Robot with Full-Body Continuous Docks
FireAnt:具有全身连续坞站的模块化机器人
"Deformable Wheel"-A Self-recovering Modular Rolling Track
“变形轮”——自恢复模块化滚动轨道
Anatomy of a superorganism -- structure and growth dynamics of army ant bivouacs
超有机体的解剖——行军蚁营地的结构和生长动态
  • DOI:
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Thomas Bochynek;Florian Schiffers;A. Aichert;O. Cossairt;S. Garnier;Michael Rubenstein
  • 通讯作者:
    Michael Rubenstein
Error Cascades in Collective Behavior: A Case Study of the Gradient Algorithm on 1000 Physical Agents
集体行为中的错误级联:1000 个物理主体的梯度算法案例研究
FireAnt3D: a 3D self-climbing robot towards non-latticed robotic self-assembly
FireAnt3D:面向非格子机器人自组装的3D自爬升机器人

Michael Rubenstein的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Michael Rubenstein', 18)}}的其他基金

Collaborative Research: NRI: FND: Flying Swarm for Safe Human Interaction in Unstructured Environments
合作研究:NRI:FND:用于非结构化环境中安全人类互动的飞群
  • 批准号:
    2024615
  • 财政年份:
    2020
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant

相似国自然基金

Research on Quantum Field Theory without a Lagrangian Description
  • 批准号:
    24ZR1403900
  • 批准年份:
    2024
  • 资助金额:
    0.0 万元
  • 项目类别:
    省市级项目
Cell Research
  • 批准号:
    31224802
  • 批准年份:
    2012
  • 资助金额:
    24.0 万元
  • 项目类别:
    专项基金项目
Cell Research
  • 批准号:
    31024804
  • 批准年份:
    2010
  • 资助金额:
    24.0 万元
  • 项目类别:
    专项基金项目
Cell Research (细胞研究)
  • 批准号:
    30824808
  • 批准年份:
    2008
  • 资助金额:
    24.0 万元
  • 项目类别:
    专项基金项目
Research on the Rapid Growth Mechanism of KDP Crystal
  • 批准号:
    10774081
  • 批准年份:
    2007
  • 资助金额:
    45.0 万元
  • 项目类别:
    面上项目

相似海外基金

Collaborative Research: RI: Medium: Principles for Optimization, Generalization, and Transferability via Deep Neural Collapse
合作研究:RI:中:通过深度神经崩溃实现优化、泛化和可迁移性的原理
  • 批准号:
    2312841
  • 财政年份:
    2023
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
Collaborative Research: RI: Medium: Principles for Optimization, Generalization, and Transferability via Deep Neural Collapse
合作研究:RI:中:通过深度神经崩溃实现优化、泛化和可迁移性的原理
  • 批准号:
    2312842
  • 财政年份:
    2023
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
Collaborative Research: RI: Small: Foundations of Few-Round Active Learning
协作研究:RI:小型:少轮主动学习的基础
  • 批准号:
    2313131
  • 财政年份:
    2023
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
Collaborative Research: RI: Medium: Lie group representation learning for vision
协作研究:RI:中:视觉的李群表示学习
  • 批准号:
    2313151
  • 财政年份:
    2023
  • 资助金额:
    $ 50万
  • 项目类别:
    Continuing Grant
Collaborative Research: RI: Medium: Principles for Optimization, Generalization, and Transferability via Deep Neural Collapse
合作研究:RI:中:通过深度神经崩溃实现优化、泛化和可迁移性的原理
  • 批准号:
    2312840
  • 财政年份:
    2023
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
Collaborative Research: RI: Small: Deep Constrained Learning for Power Systems
合作研究:RI:小型:电力系统的深度约束学习
  • 批准号:
    2345528
  • 财政年份:
    2023
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
Collaborative Research: RI: Small: Motion Fields Understanding for Enhanced Long-Range Imaging
合作研究:RI:小型:增强远程成像的运动场理解
  • 批准号:
    2232298
  • 财政年份:
    2023
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
Collaborative Research: RI: Small: End-to-end Learning of Fair and Explainable Schedules for Court Systems
合作研究:RI:小型:法院系统公平且可解释的时间表的端到端学习
  • 批准号:
    2232055
  • 财政年份:
    2023
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
Collaborative Research: RI: Medium: Lie group representation learning for vision
协作研究:RI:中:视觉的李群表示学习
  • 批准号:
    2313149
  • 财政年份:
    2023
  • 资助金额:
    $ 50万
  • 项目类别:
    Continuing Grant
Collaborative Research: CompCog: RI: Medium: Understanding human planning through AI-assisted analysis of a massive chess dataset
合作研究:CompCog:RI:中:通过人工智能辅助分析海量国际象棋数据集了解人类规划
  • 批准号:
    2312374
  • 财政年份:
    2023
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了