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.

主要研究者：Daniela RusInstitution：马萨诸塞州理工学院、华盛顿大学、康奈尔大学、宾夕法尼亚大学提案编号：0735953 EFRI-ARESCI：控制自主重新配置工厂这个项目与来自马萨诸塞州理工学院、康奈尔大学、宾夕法尼亚大学和华盛顿大学的调查人员一起，旨在建立新的基础理论，以了解自主可重构系统的不确定性条件下。 自然系统具有非凡的能力，可以从各种各样的原材料中创造出确定性的结构和过程。 它们对原材料来源具有极强的鲁棒性，对它们的行为具有高度的适应性，部分原因是它们的随机性。 这些属性也是工程系统，如自动化工厂，协作机器人系统，网络计算系统所期望的。 然而，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，这使得它们在供应波动方面很脆弱，并且其结构重新配置和功能适应的能力有限。 这个项目的目标是探索，并在物理上证明，一个新的范例，从基本组件的物理系统的稳健建设和自适应重新配置，在不确定性和可变性的材料资源。 研究人员设想了一个制造过程，其中源和目标是间接定义的，它们之间的路径受到随机波动的影响，需要战略决策。 该项目致力于：（1）通过研究分布式算法、控制理论和统计物理方法来建模系统行为，从而重构系统的理论基础;（2）通过分析这些系统中的信息流和开发综合设计方法来分析和综合的方法;以及（3）通过使用研究者现有的和新的平台来演示构建和群集任务的实验验证。拟议系统的目标是建立在在灾难现场飞行和实例化，通过创建物理结构和促进第一响应者的信息流来提供支持。 该系统还可以在建筑和制造的背景下进行实例化，将制造过程带到新的定制化、鲁棒性和自动化水平。 这项研究可以更好地理解生物系统，这些系统在许多不同的层面上都是自组织的。 最后，所提出的方法来工程和分析随机自适应重新配置机器可能会产生神经科学家，心理学家和生物学家关于复杂系统中的适应性和鲁棒性的组织和算法性质的假设。