EFRI-ARESCI:Controlling the Autonomously Reconfiguring Factory

EFRI-ARESCI：控制自主重构工厂

• 批准号: 0735953
• 负责人: Daniela Rus
• 金额: $ 200万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0735953&HistoricalAwards=false
• 关键词: EFRI; ARESCI; Controlling; Autonomously; Reconfiguring

PI: Daniela RusInstitution: Massachusetts Institute of Technology, University of Washington,Cornell University, University of PennsylvaniaProposal Number: 0735953EFRI-ARESCI: Controlling the Autonomously Reconfiguring FactoryThis project, with investigators from the Massachusetts Institute of Technology, Cornell University, the University of Pennsylvania, and the University of Washington, seeks to establish new fundamental theories for understanding autonomously reconfigurable systems under conditions of uncertainty. Natural systems possess the remarkable ability to create deterministic structures and processes out of a huge variety of raw materials. They have extreme robustness with respect to the source of raw materials and high adaptability with respect to their behaviors, due in part to their stochastic nature. These properties are also desirable for engineered systems such as automated factories, cooperative robotic systems, and networked computational systems. However, currently the design and assembly of these systems relies on deterministic processes and supply chains, which makes them fragile with respect to fluctuations in supply and limited in their ability for structural reconfiguration and functional adaptation. The goal of this project is to explore, and physically demonstrate, a novel paradigm for robust construction and adaptive reconfiguration of physical systems from elementary components, under uncertainty and variability of material resources. The investigators envision a manufacturing process where the source and target are defined indirectly, and the path between them is subject to stochastic fluctuations requiring strategic decisions. The project addresses (1) the theoretical foundations of reconfiguring systems by examining distributed algorithms, control theory, and statistical physics approaches to modeling system behavior; (2) methods for analysis and synthesis by analyzing the information flow in these systems and the development of a synthetic design methodology; and (3) experimental validation by using the investigator's existing and new platforms to demonstrate construction and swarming tasks.The goal of the proposed system is to be built on-the-fly and instantiated at a disaster site to provide support by creating physical structures and facilitating information flow for first responders. The system can also be instantiated in the context of construction and fabrication, bringing manufacturing processes to new levels of customization and robustness and automation. This study can lead to a better understanding of biological systems, which are self-organizing at many different levels. Finally, the proposed approaches to engineering and analyzing stochastic adaptive reconfiguring machines may generate hypotheses for neuroscientists, psychologists and biologists regarding the organizational and algorithmic nature of adaptation and robustness in complex systems.

PI：Daniela Rusinstitution：马萨诸塞州华盛顿大学，康奈尔大学，宾夕法尼亚大学康奈尔大学，康奈尔大学大学的编号：0735953efri-Aresci：控制该工厂的自主重新配置工厂项目，从马萨诸塞州康奈尔大学，宾夕法尼亚大学的研究人员，宾夕法尼亚大学，宾夕法尼亚大学的研究人员在不确定性条件下，理解自主可重构系统的基本理论。 天然系统具有从多种原材料中创建确定性结构和过程的非凡能力。 它们在原材料的来源和相对于其行为的高适应性方面具有极大的鲁棒性，部分原因是它们的随机性质。 这些属性对于工程系统（例如自动化工厂，合作机器人系统和网络计算系统）也是可取的。 但是，目前，这些系统的设计和组装依赖于确定性的过程和供应链，这使它们在供应波动方面脆弱，并且其结构重新配置和功能适应的能力有限。 该项目的目的是在物理系统的不确定性和材料资源的变异性下探索并在物理上证明是一种新颖的范式，用于从基本组件中对物理系统的强大结构和适应性重新配置。 研究人员设想了一个间接定义来源和目标的制造过程，它们之间的路径可能会受到需要战略决策的随机波动。 该项目通过检查分布式算法，控制理论和统计物理学方法来建模系统行为来解决重新配置系统的理论基础； （2）通过分析这些系统中的信息流以及合成设计方法的开发来分析和合成的方法； （3）通过使用研究者的现有和新平台来展示构建和蜂群的实验验证。拟议系统的目标将在灾难现场建造并实例化，以通过创建物理结构并促进第一响应者的信息流来提供支持。 该系统也可以在施工和制造的背景下进行实例化，从而使制造过程达到了新的自定义，鲁棒性和自动化的水平。 这项研究可以使对生物系统有更好的了解，生物系统在许多不同的层面上进行自组织。 最后，提议的工程和分析随机适应性重新配置机的方法可能会为神经科学家，心理学家和生物学家提供有关复杂系统适应性和鲁棒性的组织和算法性质的假设。