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: 1426824
• 负责人: Bilge Mutlu
• 金额: $ 59万
• 依托单位: University of Wisconsin-Madison
• 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 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1426824&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.

协同制造中应用的机器人必须与人肩并肩地进行体力劳动。这种机器人可以灵活地完成许多不同的任务，并有望通过提高小商店手工流程的质量和效率来改变制造业，并促进高度定制产品的生产。然而，为了实现这一承诺，机器人必须有效地集成到现有的制造团队和实践中。为了实现这种整合，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仪器的基础研究。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。协作机器人与制造业的有效整合有望改善许多尚未受益于机器人技术的行业。因此，本研究的结果将有助于提高美国制造业的竞争力，有利于美国经济和社会。这项研究将涉及多个学科的贡献，包括机器人、人为因素、计算机科学和制造业，以及学术界和工业界的合作者。这些合作将有助于将研究成果传播到制造组织，并将研究成果整合到工程专业的本科和研究生课程中。机器人技术的进步保证了协作机器人的使用，协作机器人可以与人进行相互依赖的工作，从而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高效运行，提出了重大的研究挑战，包括基于任务需求和约束的工作优化分配，人-机器人团队的形成以及团队对工作流程变化的动态适应。本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，实现人类和机器人工人之间高效、安全的协作。研究小组将根据任务限制和工人技能，为人类和机器人工人的任务优化分配创造新的方法，设计利用这些方法促进人机团队工作流设计的新工具，并开发新的机制，使机器人能够在计划的制造操作中更有效、更安全地与人类工人合作。这些方法和工具将在现实世界的制造操作中得到验证，并通过工业研讨会、工程课程和公共推广计划进行传播。

项目成果

Effective task training strategies for human and robot instructors

人类和机器人教练的有效任务训练策略

• DOI: 10.1007/s10514-015-9461-0
• 发表时间: 2015
• 期刊: Autonomous Robots
• 影响因子: 3.5
• 作者: Sauppé, Allison;Mutlu, Bilge
• 通讯作者: Mutlu, Bilge

Designing Interface Aids to Assist Collaborative Robot Operators in Attention Management

设计界面辅助工具以协助协作机器人操作员进行注意力管理

• 发表时间: 2021
• 期刊: IEEE International Conference on Robot and Human Interactive Communication
• 作者: Henrichs, Curt;Zhao, Fangyun;Mutlu, Bilge
• 通讯作者: Mutlu, Bilge

Authr: A Task Authoring Environment for Human-Robot Teams

Authr：人机团队的任务创作环境

• DOI: 10.1145/3379337.3415872
• 发表时间: 2020
• 期刊: UIST '20: Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology
• 作者: Schoen, Andrew;Henrichs, Curt;Strohkirch, Mathias;Mutlu, Bilge
• 通讯作者: Mutlu, Bilge

Optimizing Makespan and Ergonomics in Integrating Collaborative Robots Into Manufacturing Processes

将协作机器人集成到制造流程中优化完工时间和人体工程学

• DOI: 10.1109/tase.2018.2789820
• 发表时间: 2018
• 期刊: IEEE Transactions on Automation Science and Engineering
• 影响因子: 5.6
• 作者: Pearce, Margaret;Mutlu, Bilge;Shah, Julie;Radwin, Robert
• 通讯作者: Radwin, Robert