I-Corps: Artificial Intelligence (AI)-Enabled and Digital Twin Interactive Robots for Facility Hygiene and Human Health

I-Corps项目的更广泛影响/商业潜力是开发一种移动消毒设备。2019冠状病毒病大流行、季节性流感和其他感染给社会带来沉重负担。这项提出的技术可以通过智能机器人系统改善当前的清洁和消毒实践。本方案旨在提高清洁消毒效果，增强个人卫生和公共卫生，减轻清洁消毒人员的高工作量和暴露风险。这些好处可以降低设施的运营成本以及设施内感染的机会成本，并产生积极的社会和经济影响。拟议的服务商业模式可能有助于采用消毒和服务机器人作为一种灵活的选择，在大流行后时代创造新的机会并为更广泛的领域提供服务。I-Corps项目的基础是开发一种支持人工智能（AI）的数字孪生交互式机器人，该机器人可适应环境、社会过程和建筑动态，实现自动化、连续、精确和社会友好型消毒。关键的创新包括使用基于数字孪生的模拟和深度学习环境来训练机器人预测和感知污染区域，以便在建筑物和房间层面进行精确消毒。本研究提高了表面消毒的效率和效果。由数据驱动的建模和仿真得出的病原体感染风险可能为机器人导航优先考虑消毒路线提供新的信息流。除了专门针对污染区域进行精确消毒外，机器人还将学习理解社会环境（例如，房间内的人类活动和隐私），以便在人类在场的情况下操作，而不会中断正常活动，也不会将紫外线等有害物质暴露在人类面前，从而实现安全和社会友好的消毒。通过高保真虚拟仿真，模仿学习将被开发，以训练机器人学习社会和物理约束，并根据环境和物体几何形状调整其消毒动作，从而实现完整、高效和安全的消毒。这项技术可以实现单一的智能解决方案，以保持环境卫生和人类健康。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 broader impact/commercial potential of this I-Corps project is the development of a mobile disinfection device. The COVID-19 pandemic, seasonal epidemics of flu, and other infections placed heavy burdens on society. This proposed technology may improve current cleaning and disinfection practices with an intelligent robotic system. The proposed solution is designed to improve cleaning and disinfection efficacy, enhance personal hygiene and public health, and relieve the high workload and exposure risks facing cleaning and disinfection workers. These benefits may reduce the facility operational costs as well as opportunity costs from infection within facilities, and generate positive societal and economic impacts. The proposed service business model may facilitate the adoption of disinfection and service robots as a flexible choice, creating new opportunities and serving broader areas in the post-pandemic age.This I-Corps project is based on the development of an artificial intelligence (AI)-enabled and digital-twin interactive robot that adapts to ambient environments, social processes, and building dynamics for automated, continuous, precise, and socially-friendly disinfection. The key innovations include using a digital-twin based simulation and deep learning environment to train the robots to predict and perceive contaminated areas for precision disinfection at both the building and room levels. This research improves surface disinfection efficiency and effectiveness. Pathogen infection risks derived from data-driven modeling and simulation may provide a new stream of information for robot navigation to prioritize disinfection routes. In addition to the precision disinfection that specifically targets contaminated areas, the robots will learn to understand the social contexts (e.g., human activity and privacy within a room) to operate in the presence of humans without interrupting normal activities and exposing harmful substances such as ultraviolet lights to humans, enabling safe and socially-friendly disinfection. Augmented by high-fidelity virtual simulations, imitation learning will be developed to train the robots to learn the social and physical constraints and adapt their disinfection maneuvers with respect to the context and object geometry enabling complete, efficient, and safe disinfection. The technology may enable a single smart solution to maintain environmental hygiene and human health.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.