EFRI-ARESCI:Controlling the Autonomously Reconfiguring Factory

PI: Daniela RusInstitution：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号：0735953efr - aresci：控制自主再配置工厂这个项目由来自麻省理工学院、康奈尔大学、宾夕法尼亚大学和华盛顿大学的研究人员组成，旨在建立新的基本理论，以理解不确定条件下的自主再配置系统。自然系统拥有一种非凡的能力，可以用大量不同的原材料创造出确定性的结构和过程。它们对原材料的来源具有极强的稳健性，对自己的行为具有高度的适应性，这部分是由于它们的随机性。这些特性也适用于工程系统，如自动化工厂、协作机器人系统和网络计算系统。然而，目前这些系统的设计和装配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，并且结构重构和功能适应能力有限。该项目的目标是探索和物理证明，在材料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构物理系统的新范式。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，它们之间的路径受随机波动的影响，需要战略决策。该项目涉及(1)通过检查分布式算法、控制理论和统计物理方法对系统行为建模来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和开发综合设计方法来分析和综合方法；(3)利用研究者现有平台和新平台对构建任务和蜂群任务进行实验验证。拟议系统的目标是在灾难现场即时建立和实例化，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环境中实例化，将制造过程提升到定制化、健壮性和自动化的新水平。这项研究可以更好地理解生物系统，生物系统在许多不同的层面上都是自组织的。最后，提出的工程和分析随机自适应重新配置机器的方法可以为神经科学家、心理学家和生物学家提供关于复杂系统中适应性和鲁棒性的组织和算法本质的假设。

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.