Convergence Accelerator Phase I (RAISE): Learning Environments with Advanced Robotics for Next-Generation Emergency Responders (LEARNER)

NSF融合加速器支持以团队为基础的多学科努力，解决国家重要性的挑战，并在不久的将来显示出可交付成果的潜力。“融合加速器”第一阶段项目的更广泛影响/潜在效益是产生基于技术的解决方案，以支持和增强应急响应（ER）人员的性能和安全性。学术研究人员、核心技术开发人员、利益相关者以及由行业和政府领导人组成的咨询委员会将齐聚一堂，评估与使用人类增强技术相关的机遇和挑战，这些技术可以改变ER工作中基础的、以使用为灵感的解决方案寻找过程，并以一种可转移到其他工作环境的方式。这将涉及技术原型的开发，包括半自动地面机器人、可穿戴机器人（动力外骨骼）和为急诊工作量身定制的增强现实接口；并构建和评估具有物理、增强和虚拟现实组件的混合现实学习环境，以便用户学习如何有效地使用多种增强技术。我们的努力也将有助于更好地概念化融合研究，并作为其他研究团体的模型，这些研究团体可以从跨越工程和计算机科学的传统学科界限的工作中受益。我们会透过开放源码的知识分享平台和适当的传播渠道，与急诊室社区和更广泛的世界分享我们的方法、经验和发现。该融合加速器第一阶段项目将通过自适应、个性化混合现实学习平台的开发和原型设计，显著推进急诊室的运营和培训，该平台能够在急诊室工作中集成用于人类增强的先进技术，并创建有原则的人机团队战略。我们的工作将通过使用启发技术设计和自适应人在环控制的开发来促进学习，从而大大提高外骨骼控制、人机交互和人机交互方面的知识和技术水平。此外，应用这些技术和开发有效学习平台的机会具有重大的变革潜力，因为半自动地面机器人、外骨骼和AR将使用户能够在个人和团队层面制定新的工作策略，而这些策略只能由他们新扩展的身体和感知能力提供。最后，我们的工作将通过创建一个可复制的平台来促进学习，该平台可以加快整合创新和新兴技术以培训未来工人的速度。我们的跨学科方法结合并增强了学习科学、计算机科学、虚拟和增强现实、人为因素、认知心理学和系统工程等学科的现有知识，创建了一个整合培训课程设计、创新和新兴技术实施以及新工作技术的框架。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。

The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The broader impact/potential benefit of this Convergence Accelerator Phase I project is to generate technology-based solutions that can support and augment the performance and safety of emergency response (ER) personnel. Academic researchers, core-technology developers, stakeholders and an advisory board constituted of leaders from industry and government will come together to assess opportunities and challenges related to the use of human augmentation technologies that can transform the process of foundational, use-inspired solution-finding for ER work, and in a way that is transferable to other work contexts as well. This will involve the development of technology prototypes including semi-autonomous ground robots, wearable robots (powered exoskeletons) and augmented reality interfaces tailored for ER work; and building and evaluating a mixed-reality learning environment with physical, augmented, and virtual reality components, for users to learn to work effectively with multiple augmentation technologies. Our effort will also contribute to better conceptualization of convergence research and serve as a model for other research communities that can benefit from working across traditional disciplinary boundaries in engineering and computer science. We will share our methods, learnings and findings with the ER community and the wider world through an open-source knowledge sharing platform and appropriate dissemination channels. This Convergence Accelerator Phase I project will significantly advance ER operations and training through the development and prototyping of an adaptive, personalized mixed-reality learning platform that enables integrating advanced technologies for human augmentation in ER work, and the creation of principled human-robot team strategies. Our work will substantially advance the knowledge and state-of-the-art in exoskeleton control, human-robot interaction, and human-computer interaction through use-inspired technology design and development of adaptive human-in-the-loop control to facilitate learning. Furthermore, an opportunity to field these technologies and develop effective learning platforms has significant transformative potential as semi-autonomous ground robots, exoskeletons and AR will enable users to formulate fundamentally new work strategies at the individual and team levels that are only afforded by their newly extended physical and perceptual capabilities. Finally, our work will advance learning by creating a replicable platform that increases the speed for the integration of innovative and emerging technologies for training future worker. Our transdisciplinary approach combines and enhances the existing knowledge from the disciplines of learning science, computer science, virtual and augmented realities, human factors, cognitive psychology, and systems engineering to create a framework that integrates training course design, innovative and emerging technology implementation, and new techniques of work.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.