CAREER: Human Factors and Task Scheduling for Multi-Human Multi-Robot Collaborative Manufacturing in Industry 5.0 Contexts

机器人在广泛的制造系统和任务中发挥着重要作用，特别是在新兴的工业5.0环境中，机器人的认知计算能力与人类工人的智慧和足智多谋以及协同操作的制造系统相结合。工业5.0是不可避免的下一次工业革命，除了工业4.0的自动化和效率特点外，还促进了个人的人性化。该学院早期职业发展计划（Career）奖支持的研究重点是通过利用人为因素的价值和最先进的机器人技术，在工业5.0环境下实现多人多机器人协同制造，从而推进人类工人的赋权、个性化和多样性。作为未来制造业的核心，在追求效率和生产力的最佳平衡时，人类的福祉被置于制造过程的中心。为了缓解这一挑战，该研究项目将开发一个协同计算框架，包括人为因素和任务调度的表征、建模、实施和评估，以促进人机合作伙伴关系中协作制造效率和人类福祉的基础和创造性进步。此外，这项研究将由一个可扩展的教育项目（包括大学预科教育、大学生指导和新课程开发）来补充，这将培养和授权下一代科学家和工程师，特别是那些来自代表性不足的群体的科学家和工程师，让他们参与学习和在现实世界中应用领先的机器人和制造技术。本研究的目的是通过建立一个基于人为因素的协同任务调度框架，推进工业5.0环境下的多人多机器人协同制造。需要解决的主要问题是：(1)如何对多人多机器人协作中的人为因素进行参数化和建模？(2)如何优化多人多机器人协同制造流程和任务调度？(3)如何在多人多机器人协作任务中验证和评估所开发的框架？为了解决这些问题，该项目涉及创建新的计算模型，使用多模式行为和生理生物识别信息定量分析人为因素。在此基础上，构建基于人因的协同任务调度框架，提高人机协作效率。用户研究将通过真实世界的多人多机器人协同制造任务来验证和评估开发的方法。本研究将通过利用机器人、自动化、制造、工程和认知工效学的跨学科观点，为制造系统集成中的人为因素、任务调度和机器人自主性提供新的知识和理解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Robotics plays a significant role in a wide range of manufacturing systems and tasks, especially in emerging Industry 5.0 contexts, where the robots’ cognitive computing capabilities are merged with human workers’ intelligence and resourcefulness as well as manufacturing systems in cooperative operations. Industry 5.0 is an inevitable next industrial revolution, in which the personal human touch is promoted in addition to automation and efficiency characterized in Industry 4.0. This Faculty Early Career Development Program (CAREER) award supports research that will focus on advancing the empowerment, personalization, and diversity of human workers by leveraging the value of human factors and state-of-the-art robotics technologies for multi-human multi-robot collaborative manufacturing in Industry 5.0 contexts. At the heart of the future of manufacturing, human wellbeing is placed at the center of the manufacturing process when pursuing the optimal balance of efficiency and productivity. To alleviate this challenge, the research project will develop a synergistic computational framework including the characterization, modeling, implementation, and assessment of human factors and task scheduling to facilitate fundamental and creative advances for both collaborative manufacturing efficiency and human wellbeing in human-robot partnerships. In addition, the research will be complemented by a scalable educational program (including pre-university education, college student mentoring, and new curriculum development), which will foster and empower next-generation scientists and engineers, especially those from underrepresented groups, by engaging them in learning and implementing leading-edge robotics and manufacturing technologies in real-world applications. The objective of this research is to advance multi-human multi-robot collaborative manufacturing in Industry 5.0 contexts by establishing a synergistic human factors-based task scheduling framework. The major questions to be addressed are (1) How can human factors in multi-human multi-robot collaboration be parameterized and modeled? (2) How can the multi-human multi-robot collaborative manufacturing processes and task scheduling be optimized? and (3) How will the developed framework be validated and assessed in multi-human multi-robot collaborative tasks? To address these, the project involves the creation of new computational models to quantitatively analyze human factors using multimodal behavioral and physiological biometrics information. Then a synergistic human factors-based task scheduling framework will be built for the interdependent human-robot partnerships to improve collaboration efficiency. User studies will be conducted to testify and evaluate the developed approaches through real-world multi-human multi-robot collaborative manufacturing tasks. This research will contribute toward new knowledge and understanding of human factors, task scheduling, and robot autonomy in manufacturing systems integration by leveraging interdisciplinary perspectives from robotics, automation, manufacturing, engineering, and cognitive ergonomics.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.