NRI/Collaborative Research: Models and Instruments for Integrating Effective Human-Robot Teams into Manufacturing

NRI/协作研究：将有效的人机团队集成到制造中的模型和工具

• 批准号: 1426799
• 负责人: Julie Shah
• 金额: $ 30万
• 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: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1426799&HistoricalAwards=false
• 关键词: NRI; Collaborative; Research; Models; Instruments

Robots for application in collaborative manufacturing must perform manual work side-by-side with people. Such robots offer the flexibility to work on many different tasks and promise to transform manufacturing by improving the quality and efficiency of manual processes in small shops and in facilitates that manufacture highly customized products. However, in order to meet this promise, robots must be effectively integrated into existing manufacturing teams and practices. To enable this integration, this National Robotics Initiative (NRI) award supports fundamental research on the methods and instruments that manufacturing engineers will need to form effective human-robot teams based on task requirements and worker skills. These methods will also enable robots to adapt to changes in workflow to maximize safety and efficiency. The effective integration of collaborative robots into manufacturing promises improvements in many industries that have not yet benefited from robotic technology. Therefore, results from this research will contribute to the competitiveness of U.S. manufacturing and benefit the U.S. economy and society. The research will involve contributions from multiple disciplines, including robotics, human factors, computer science, and manufacturing, and by academic and industry collaborators. These collaborations will help the dissemination of research results into manufacturing organizations and the integration of research into undergraduate and graduate curriculum in engineering.Advancements in robotics promise the use of collaborative robots that perform interdependent work with people in order to improve quality, efficiency, and safety in industrial manufacturing. However, integrating collaborative robots into these processes and ensuring their efficient operation pose significant research challenges, including the optimal allocation of work based on task requirements and constraints, the formation of human-robot teams, and the dynamic adaptation of teamwork to workflow changes. This research will address these research challenges, enabling the seamless integration of collaborative robots into these processes and achieving efficient and safe collaboration between human and robot workers. The research team will create novel methods for optimal allocation of tasks to human and robot workers based on task constraints and worker skills, design new tools that utilize these methods to facilitate workflow design for human-robot teams, and develop novel mechanisms that enable robots to more efficiently and safely collaborate with human workers in the planned manufacturing operations. These methods and instruments will be validated in real-world manufacturing operations and disseminated through industry workshops, engineering curricula, and a public outreach program.

用于协作制造业应用程序的机器人必须与人并排手动工作。这样的机器人可以灵活地处理许多不同的任务，并有望通过提高小商店的手动流程的质量和效率以及制造高度定制产品的便利性来转变制造业。但是，为了实现这一承诺，必须有效地将机器人纳入现有的制造团队和实践中。为了实现这一融合，该国家机器人倡议（NRI）奖支持对制造工程师需要根据任务要求和工人技能组成有效的人类机器人团队的方法和工具的基础研究。这些方法还将使机器人适应工作流程的变化，以最大程度地提高安全性和效率。将协作机器人的有效整合到制造业中有望在许多尚未受益于机器人技术中受益的行业的改进。因此，这项研究的结果将有助于美国制造业的竞争力，并使美国的经济和社会受益。该研究将涉及多个学科的贡献，包括机器人技术，人为因素，计算机科学和制造业，以及学术和行业合作者。这些合作将有助于将研究成果传播到制造组织中，并将研究整合到工程学中的本科和研究生课程中。机器人的预测有望使用协作机器人的使用，这些机器人可以与人们进行相互依存的工作，以提高工业制造业的质量，效率和安全性。但是，将协作机器人整合到这些过程中，并确保其有效的运营构成了重大的研究挑战，包括根据任务要求和约束的最佳作品分配，人类机器人团队的形成以及团队合作对工作流程变化的动态改编。这项研究将解决这些研究挑战，从而使协作机器人无缝整合到这些过程中，并实现人与机器人工人之间的高效和安全协作。研究团队将根据任务限制和工人技能，设计使用这些方法来促进人类机器人团队的工作流程设计的新工具，创建新的方法，以最佳分配给人和机器人工人，并设计新的工具，并开发新的机制，使机器人能够在计划中更有效，更安全地与人工合作进行计划的制造业务。这些方法和工具将在现实世界的制造运营中得到验证，并通过行业研讨会，工程课程和公共宣传计划进行传播。