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 Rus机构：麻省理工学院、华盛顿大学、康奈尔大学、宾夕法尼亚大学提案编号：0735953EFRI-ARESCI：控制自主重新配置工厂该项目与来自麻省理工学院、康奈尔大学、宾夕法尼亚大学的研究人员合作，和华盛顿大学，致力于建立新的基础理论来理解不确定条件下的自主可重构系统。 自然系统具有利用各种各样的原材料创建确定性结构和过程的非凡能力。 它们在原材料来源方面具有极高的鲁棒性，在行为方面具有高度适应性，部分原因在于它们的随机性。 这些特性对于自动化工厂、协作机器人系统和网络计算系统等工程系统来说也是理想的。 然而，目前这些系统的设计和组装依赖于确定性流程和供应链，这使得它们在供应波动方面很脆弱，并且结构重新配置和功能适应的能力受到限制。 该项目的目标是探索并物理演示一种在物质资源的不确定性和可变性下从基本组件稳健构建和自适应重新配置物理系统的新范例。 研究人员设想了一种制造过程，其中源和目标是间接定义的，并且它们之间的路径会受到需要战略决策的随机波动的影响。 该项目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，解决（1）重新配置系统的理论基础； (2) 分析和综合方法，通过分析这些系统中的信息流并开发综合设计方法； (3) 通过使用研究者现有的和新的平台来演示构建和集群任务进行实验验证。所提出的系统的目标是在灾难现场即时构建并实例化，以通过创建物理结构和促进急救人员的信息流动。 该系统还可以在构建和制造的背景下进行实例化，将制造流程的定制、稳健性和自动化提升到新的水平。 这项研究可以帮助人们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，所提出的随机自适应重构机器的工程和分析方法可以为神经科学家、心理学家和生物学家产生关于复杂系统中适应和鲁棒性的组织和算法性质的假设。