FW-HTF-RM: Collaborative Research: Supervise It! Optimizing Intelligent Robot Integration Through Feedback to Workers and Supervisors

FW-HTF-RM：协作研究：监督！

• 批准号: 2026467
• 负责人: Heather Keathley-Herring
• 金额: $ 104.27万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026467&HistoricalAwards=false
• 关键词: FW; HTF; RM; Collaborative; Research

Limitations in achievable performance and programmability are obstacles to realizing productivity gains from the full automation of manufacturing operations requiring a large variety of low-volume tasks. The use of collaborative robots to assist human workers is a promising approach to overcoming these obstacles, without displacing human jobs. Current efforts in this area focus on the worker-robot partnership and overlook the critical role of the supervisor in managing workloads and allocating tasks. By considering the larger context of supervised work teams, this Future of Work at the Human-Technology Frontier (FW-HTF) research aims to enhance productivity and improve worker quality of life by increasing the effectiveness of workers operating in partnership with robots. It provides a framework for analyzing readiness, assessing adoption, and evaluating performance of collaborative robotics in industrial settings. Partnerships and interactions with companies in the Southeastern USA will promote realistic research efforts that translate to practice, benefitting small-to-medium manufacturing companies in the USA. Efforts and findings will be promoted to the public to attract the next generation of workers and researchers to science and engineering fields.This project explores two hypotheses. The first working hypothesis is that, when workers view robots as partners, imperfection will be tolerated if the worker can successfully manage the robot to complete the task faster than their self-conceived rate. The determining factor regarding the value of a worker-robot collaborative partnership is hypothesized to be the worker’s ability to allocate the task workload between the robot and themselves towards an optimal partnership. The second working hypothesis is that the introduction of a supervisor to guide and promote task allocation will further contribute to enhanced worker-robot performance. This hypothesis builds on the observation that line supervisors interact with multiple workers, and thus are a repository of holistic institutional knowledge regarding good practice. To confirm these hypotheses and arrive at the anticipated framework, called the Worker-Robot Supervisor Effectiveness Model, requires a mixed-methods research design. First, a grounded theory study will establish assessment criteria related to worker, robot, and supervisor technology adoption and performance to develop an instrument for measuring both. Second, within a simulated manufacturing work cell environment, a set of experiments will investigate factors linked to successful technology adoption and work cell performance. Third, the findings from these studies will inform the creation of the model, which will provide guidance for effective integration, adoption, and supervision of worker-robot partnerships. Fourth, to confirm the applicability of the derived model, it will be used to field and integrate a robot within the existing processes of a real-world company. Field deployment will provide empirical evidence to validate and refine the model.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.

可实现的性能和编程的局限性是实现生产力提高的障碍，从制造运营的完整自动化中，需要各种各样的小批量任务。使用协作机器人来协助人工工人是克服这些障碍的一种有前途的方法，而无需取代人类工作。该领域的当前努力集中在工人机器人的伙伴关系上，并忽略了主管在管理工作负载和分配任务中的关键作用。通过考虑监督工作团队的更大背景，人类技术前沿（FW-HTF）研究的工作未来旨在通过提高与机器人合作运作的工人的有效性来提高生产力并提高工人生活质量。它提供了一个框架，用于分析准备就绪，评估采用和评估工业环境中协作机器人技术的性能。与美国东南部的公司的伙伴关系以及与公司的互动将促进现实的研究工作，这些研究工作转化为实践，从而使美国的小型制造公司受益。努力和发现将促进公众吸引下一代工人和研究人员进入科学和工程领域。该项目探讨了两个假设。第一个工作假设是，当工人将机器人视为合作伙伴时，如果工人能够成功管理机器人以比自我构想的速度更快地完成任务，就会容忍不完美。确定因素涉及工人 - 机器人协作伙伴关系的价值，认为这是工人在机器人和自己之间分配任务工作量的能力，以实现最佳合作伙伴关系。第二个工作假设是，引入指导和促进任务分配的主管将进一步有助于增强的工人射击绩效。这一假设是基于线主管与多名工人互动的观察，因此是关于良好实践的整体机构知识的存储库。为了确认这些假设并到达预期的框架（称为工人机器人主管的有效性模型）需要进行混合方法研究设计。首先，一项扎根的理论研究将建立与工人，机器人和主管技术采用和绩效相关的评估标准，以开发一种衡量两者的工具。其次，在模拟的制造工作单元环境中，一组实验将研究与成功采用技术和工作单元性能有关的因素。第三，这些研究的发现将为该模型的创建提供信息，该模型将为有效的整合，采用和监督工人机器人伙伴关系提供指导。第四，为了确认派生模型的适用性，它将用于将机器人置于现实世界公司的现有过程中。现场部署将提供经验证据来验证和完善该模型。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为是通过评估来获得珍贵的支持。