CAREER: Toolkits for Digital/Physical Workflows

职业：数字/物理工作流程工具包

• 批准号: 2339273
• 负责人: Nadya Peek
• 金额: $ 71.31万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-06-15 至 2029-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339273&HistoricalAwards=false
• 关键词: CAREER; Toolkits; Digital; Physical; Workflows

This project contributes toolkits to support designers creating systems that include both hardware and software by lowering the threshold to using automation in their workflows. While automation can increase precision and efficiency, setting up new automated workflows can be costly, requiring expertise and significant up-front engineering time. Because of this cost, automation is largely used for tasks that are sufficiently frequently used that this up-front investment can be recouped, such as in the context of mass production. Domain experts working at smaller scales or with niche workflows cannot benefit in the same ways, hindering their progress. In contrast, this research prioritizes a broader community of fabrication practitioners whose varied requirements demand a broad diversity of automation strategies. This project will help practitioners identify viable opportunities for automation in their work, contribute open-source software and hardware toolkits with which practitioners can author their own automated digital/physical workflows, and organize communities using and maintaining these technologies. These outputs will provide assistance to a wide range of people developing innovative human-centered systems that use digital/physical workflows in their chosen application areas through a series of courses and workshops that will be taught using these technologies and through their open-source release.This research includes three main areas: formative research on the work of practitioners who span digital and physical spaces, such as fabricators or laboratory scientists; the development of novel devices and hardware modules for automating physical processes including motion control and active end effectors; and end-user software that scaffolds the authoring of automated digital/physical workflows, including modules that support the creation of task-specific artificial intelligence (AI) models for automated event detection. This human-centered approach—where users can refine their models and workflows as they collect additional data—leverages practitioners’ understanding of their own data during collection. By collaborating with experts across different domains through formative studies and design workshops, toolkits and systems from this research will generalize to multiple application areas. Other people will have opportunities to engage with this technology and learn about automation through courses and workshops in community spaces like Do-It-Yourself (DIY) biolabs and makerspaces. Furthermore, this research will contribute design recommendations for open-source hardware that is replicable and can be tailored by end-users. Together, these contributions comprise both valuable toolkits for automating digital/physical workflows and implications for future systems design.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.

该项目为工具包提供了贡献，以支持设计人员创建系统，包括硬件和软件，通过降低阈值以在其工作流程中使用自动化。虽然自动化可以提高精度和效率，但是建立新的自动化工作流程可能会很昂贵，需要专业知识和大量的前期工程时间。由于这一成本，自动化主要用于经常使用的任务，以至于可以收回这种前期投资，例如在群众生产的背景下。以较小的规模或利基工作流程工作的领域专家不能以相同的方式受益，从而阻碍了他们的进步。相比之下，这项研究优先考虑广泛的制造实践者社区，其各种需求需要广泛的自动化策略。该项目将帮助实践者确定其工作中可行的自动化机会，贡献开源软件和硬件工具包，实践者可以通过这些软件进行自动化的数字/物理工作流程，并使用这些技术组织和维护这些技术。这些输出将为各种各样的以人为中心的系统提供帮助的人提供帮助，这些系统通过一系列课程和研讨会在所选的应用程序领域中使用数字/物理工作流，这些课程和研讨会将使用这些技术进行教授，并通过其开放源释放进行教学。这项研究包括三个主要领域：关于跨越数字和实验室的实践者的形成性研究，例如，跨越数字和实验室的造型科学家或实验室科学家；开发新的设备和硬件模块，用于自动化物理过程，包括运动控制和主动最终效果；和最终用户软件，脚手架是自动数字/物理工作流程的创作，包括支持创建特定任务的人工智能（AI）模型以进行自动事件检测的模型。这种以人为本的方法（用户可以在收集其他数据收集其他数据时可以完善其模型和工作流程），这将在收集过程中对实践者对自己的数据的理解。通过通过形成性研究和设计研讨会与跨不同领域的专家合作，该研究的工具包和系统将概括为多个应用程序领域。其他人将有机会参与这项技术，并通过社区空间中的课程和讲习班来了解自动化（DIY）Biolabs和Makerspaces。此外，这项研究将为可复制的开源硬件提供设计建议，可以由最终用户量身定制。这些贡献共同包括自动化数字/物理工作流程的有价值的工具包，以及对未来系统设计的影响。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，通过评估被认为是珍贵的支持。