FMSG: Cyber: Distributed Surface Patterning Through a Cohort of Robots

FMSG：网络：通过一组机器人进行分布式表面图案化

• 批准号: 2229170
• 负责人: Ping Guo
• 金额: $ 49.63万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229170&HistoricalAwards=false
• 关键词: FMSG; Cyber; Distributed; Surface; Patterning

The understanding of designing structured surfaces for advanced functionality, such as friction reduction, antifouling, and hydrophobicity, has significantly progressed over the years; however, the critical technical barrier to the application of these structured surfaces is the scalability in manufacturing capability. The biggest challenge in surface patterning is the process scalability, which needs to reconcile the significant scale difference between the individual feature size down to the nano- or micro-level and the large surface-to-be-textured up to the meter level. The project will investigate one vision for future manufacturing -- distributed robotic manufacturing -- to achieve scalable patterning of micro-structured functional surfaces using a cohort of mini-robots. The project will not only push the knowledge boundaries in the scientific understanding of distributed physical intelligence, machine-material interactions, and swarm control, but may open up a new and interdisciplinary research field at the intersection of manufacturing, robotics, control, and cyberphysical systems. Additionally, the project includes outreach at the Museum of Science and Industry in Chicago, open-source software, and curricular innovations, including online classes and training modules. The research will build an educational and outreach platform to enable education and workforce development for STEM educators, next generation workforce, and technical engineers.The research objectives are to explore and answer three fundamental scientific questions that will enable the vision for future manufacturing in distributed robotic manufacturing. (1) Distributed physical intelligence: a new design framework will be established to distribute the intelligence among mechanical structures, analog circuits, and digital logics, as well as to design unconventional communication channels through both active and passive manners. (2) Unconventional machine-material interaction: new theoretical underpinnings will be established to investigate the machine-material interaction and the possible removal, deformation, and addition of material in this new paradigm, where the tools are extremely flexible and with significantly constrained power. (3) Swarm control: new fundamental knowledge will be generated from novel task decomposition and distribution paradigms to synthesis techniques capable of minimizing control effort, communication, and computation. Instead of top-down control of every individual mini-robot, novel methods will be established through which local rule specifications lead to global distributed pattern completion.This Future Manufacturing award is supported by the Division of Computer and Network Systems (CNS) of the Directorate for Computer and Information Science and Engineering (CISE), and by the Division of Civil, Mechanical and Manufacturing Innovation (CMMI) of the Directorate for Engineering (ENG).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.

多年来，人们对结构化表面设计以实现减摩、防污和疏水等高级功能的认识有了长足的进步；然而，这些结构化表面应用的关键技术障碍是制造能力的可扩展性。表面图案化中最大的挑战是工艺可伸缩性，这需要协调单个特征尺寸到纳米或微米级和大表面纹理到米级之间的显著比例差异。该项目将研究未来制造的一个愿景--分布式机器人制造--使用一组微型机器人实现微结构功能表面的可伸缩图案。该项目不仅将推动对分布式物理智能、机器-材料相互作用和群体控制的科学理解的知识边界，而且可能在制造、机器人学、控制和网络物理系统的交叉点开辟一个新的跨学科研究领域。此外，该项目还包括在芝加哥科学与工业博物馆的外联、开源软件和课程创新，包括在线课程和培训模块。这项研究将建立一个教育和推广平台，为STEM教育工作者、下一代劳动力和技术工程师提供教育和劳动力发展。研究目标是探索和回答三个基本的科学问题，这些问题将使分布式机器人制造中的未来制造成为可能。(1)分布式物理智能：将建立一个新的设计框架，将智能分布在机械结构、模拟电路和数字逻辑之间，并通过主动和被动两种方式设计非常规通信通道。(2)非传统的机器-材料相互作用：将建立新的理论基础，以研究机器-材料相互作用以及在这一新范式中可能出现的材料移除、变形和添加，其中工具非常灵活，动力受到显著限制。(3)群体控制：从新的任务分解和分配范例到能够最大限度地减少控制工作量、通信和计算的综合技术，将产生新的基础知识。未来制造业奖由计算机和信息科学与工程局(CEISE)的计算机与网络系统(CNS)和工程局(ENG)的土木、机械和制造创新部(CMMI)支持。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。