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Emerging Industry 5.0环境中，机器人的认知计算能力与人类工人的智能和足智多谋以及协作操作中的制造系统相结合。工业5.0是一场必然的下一次工业革命，除了工业4.0所特有的自动化和效率外，还将提升个人的人性化。该学院早期职业发展计划(Career)奖支持的研究将侧重于通过利用人的因素和最先进的机器人技术在工业5.0环境中进行多人多机器人协作制造来促进人类工人的赋权、个性化和多样性。作为制造业未来的核心，在追求效率和生产率的最佳平衡时，人类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，包括人的因素和任务调度的表征、建模、实施和评估，以促进人-机器人合作伙伴关系中协作制造效率和人类福祉的根本性和创造性进展。此外，这项研究还将得到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)的补充，该项目将通过让下一代科学家和工程师参与学习并在现实世界的应用中实施尖端机器人和制造技术来培养和增强他们的能力，特别是那些来自代表不足的群体的科学家和工程师。本研究的目的是通过建立基于协同人因的任务调度框架来推进工业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的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过程和任务调度。(3)如何在多人多机器人协作任务中对所开发的框架进行验证和评估？为了解决这些问题，该项目涉及创建新的计算模型，以使用多模式行为和生理生物识别信息对人类因素进行量化分析。然后为相互依赖的人-机器人伙伴关系构建一个基于人的因素的协同任务调度框架，以提高协作效率。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所开发的方法。这项研究将通过利用机器人学、自动化、制造、工程学和认知人机工程学的跨学科视角，帮助人们对制造系统集成中的人为因素、任务调度和机器人自主性有新的认识和理解。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.