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

应用于协同制造的机器人必须与人一起进行手动工作。这种机器人提供了在许多不同任务上工作的灵活性，并通过提高小商店和便利制造高度定制产品的手动过程的质量和效率来改变制造业。然而，为了实现这一承诺，机器人必须有效地整合到现有的制造团队和实践中。为了实现这种集成，这个国家机器人计划（NRI）奖支持制造工程师根据任务要求和工人技能组建有效的人机团队所需的方法和工具的基础研究。这些方法还将使机器人能够适应工作流程的变化，以最大限度地提高安全性和效率。将协作机器人有效地整合到制造业中，有望改善许多尚未受益于机器人技术的行业。因此，这项研究的结果将有助于提高美国制造业的竞争力，并使美国经济和社会受益。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业组织中，并将研究融入工程学的本科和研究生课程。机器人技术的进步承诺使用协作机器人与人一起执行相互依赖的工作，以提高工业制造的质量，效率和安全性。然而，将协作机器人集成到这些过程中，并确保其有效运行提出了重大的研究挑战，包括基于任务要求和约束的工作的最优分配，人机团队的形成，以及团队合作对工作流程变化的动态适应。这项研究将解决这些研究挑战，使协作机器人无缝集成到这些过程中，并实现人类和机器人工人之间的高效和安全协作。该研究团队将根据任务约束和工人技能创建新的方法，以优化人类和机器人工人的任务分配，设计新的工具，利用这些方法来促进人类-机器人团队的工作流程设计，并开发新的机制，使机器人能够更有效，更安全地与人类工人在计划的制造操作中合作。这些方法和工具将在现实世界的制造业务中得到验证，并通过行业研讨会，工程课程和公共宣传计划进行传播。

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.