CPS: Medium: Bio-socially Adaptive Control of Robotics-Augmented Building-Human Systems for Infection Prevention by Cybernation of Pathogen Transmission

建筑物内微生物病原体的传播是一个紧迫的公共卫生问题。2019年冠状病毒病(新冠肺炎)的大流行增加了开发有效手段的紧迫性，以减少公共建筑中病原体的传播，并将对建筑功能的破坏降至最低。该项目的最终目标是开发健康的建筑，将传染病的风险降至最低，该项目将为建筑和辅助机器人开发智能控制策略，以减少病原体传播和居住者暴露。将开发新技术来监测和预测病原体传播，使建筑物通风自动化，实现对受污染物体的智能识别，并进行精确消毒，以减少病原体通过空气循环和表面接触传播。该项目的调查结果还将指导居住者和设施管理人员制定和实施有效的行为干预和卫生做法。如果成功，这项研究将彻底改变对建筑环境的控制，使其能够预防传染病，这将给国家带来巨大的公共健康和经济利益。该项目还将创造新的和独特的机会来激发学生的学术兴趣，并支持下一代劳动力的发展，这些劳动力充分配备了应对国家面临的挑战所需的跨学科计算和工程技能。本研究的目标是：1)增进对物理、生物和社会过程之间的联系的了解，这些过程驱动建筑环境中人与病原体相互作用的动态；以及2)开发集成监测、建筑控制、机器人适应和人在环路中的互动的新型网络物理系统，以减少传染病病原体的传播。这项研究开创了一种新的数字配对方法，该方法集成了建筑信息建模、隐私保护的物联网传感、时空分子和元基因组测序以及机器学习，以绘制建筑-人类-病原体的相互作用图，以预测多时空尺度上的污染和感染风险。将开发新的方法来连接和管理建筑物、居住者和机器人，以减少病原体负担。这些方法包括：基于模型和动态数据的建筑物通风优化控制；用于机器人识别污染点的学习算法；考虑行为因素的自适应消毒过程；以及具有实时传感的建筑物操作和机器人消毒的联合优化。此外，将开发以用户为中心的系统，以分析背景信息并建议卫生实践、组织运作和人群管理，以防止疾病传播和维护建筑物内的功能。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。

Microbial pathogen transmission in buildings is an urgent public health concern. The pandemic of coronavirus disease 2019 (COVID-19) adds to the urgency of developing effective means to reduce pathogen transmission in public buildings with minimal disruptions in building functions. With the ultimate goal to develop healthy buildings that minimize risks of infectious diseases, this project will develop smart control strategies for buildings and assistive robots to mitigate pathogen transmission and occupant exposure. New techniques will be developed to monitor and predict pathogen spreading, automate building ventilation, enable intelligent recognition of contaminated objects, and perform precision disinfection to reduce pathogen transmission through air circulation and surface contacts. Findings from this project will also guide occupants and facility managers to develop and implement effective behavioral interventions and hygiene practices. If successful, this research will revolutionize the control of built environments to enable protection against infectious diseases, which will have vast public health and economic benefits to the nation. This project will also create new and unique opportunities to stimulate the academic interests of students and support the development of next-generation workforce adequately equipped with interdisciplinary computing and engineering skills needed to address challenges facing the nation.The objectives of this research are: 1) Advance understanding of linkages among physical, biological, and social processes that drive the dynamics of human-pathogen interactions in building environments; and 2) Develop a novel cyber-physical system of integrated monitoring, building control, robot adaptation, and human-in-the-loop interactions to reduce the transmission of infectious pathogens. This research pioneers a novel digital-twinning approach that integrates building information modeling, privacy-preserving internet of things sensing, spatiotemporal molecular and metagenomic sequencing, and machine learning to map building-human-pathogen interactions for predicting contamination and infection risks at multiple spatiotemporal scales. New methods will be developed to connect and manage the buildings, occupants, and robots to reduce pathogen burdens. These methods include: model-based and dynamic-data-enabled optimal control of building ventilation; learning algorithms for robotic identification of contaminated spots; adaptive disinfection processes with behavioral considerations; and co-optimization of building operations and robotic disinfection with real-time sensing. In addition, user-centric systems will be developed to analyze contextual information and recommend hygiene practices, organizational operations, and crowd management to prevent disease spreading and maintain functionalities within buildings.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.