FW-HTF-P: Distributed Intelligent Assistant to Infrastructure Inspection Workers

FW-HTF-P：基础设施巡检人员分布式智能助手

• 批准号: 2026612
• 负责人: Xiong Yu
• 金额: $ 15万
• 依托单位: Case Western Reserve University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026612&HistoricalAwards=false
• 关键词: FW; HTF; Distributed; Intelligent; Assistant

Sustainable, safe infrastructure carries the prosperity of our society. The Nation faces a grave infrastructure challenges in maintaining and upgrading its infrastructure to deliver an adequate standard of service compatible to the expectations of its citizens. Besides, the prosperity of our communities requires to embrace and erect emerging types of service infrastructure (i.e., autonomous vehicles, connected vehicles, Internet of Things, etc.) to meet modern-day expectations of digital and connected world. Infrastructure inspection provides important data to support decisions on its management, rehabilitation, and reconstruction plan, which cost billions of dollars each year. Various infrastructure service sectors conduct routine inspections of their infrastructure conditions (such as road, bridges, culvert, transmission lines, etc.). The infrastructure inspection industry employs tens of thousands workers in the private sector. The public sectors also maintains in-house workforce to inspect public infrastructure such as interstate highway, bridges, levees, etc. While there are significant advancement in robotics and information technologies, there are major technical barriers to make them fully integrated to augment human inspection and judgment. Consequently, human inspection remains as the mainstream for information collection across a variety of infrastructure sectors. This planning grant aims to define the road map towards how robotics and AI-driven techniques can be seamlessly integrated with human inspectors to improve their efficiency, reliability, and safety. The hypothesis is that the nexus of inspection workers and technology will support sound data-driven infrastructure decisions, and therefore help improve the level of service of the existing infrastructure system as well as better target high payoff futuristic infrastructure investments. It therefore will explore the frontier of human-technology nexus in domain with high impacts on the welfare of the infrastructure inspection workers as well as on the general public.The planning activities by this project will implement a number of strategies to build a roadmap for human-robotics integration center upon infrastructure inspection workers. The integrated research theme is defined in the areas of Inspection efficiency and reliability, Inspector safety and cybersecurity, Decision machine in support of inspection decisions, Open framework for public engagement in infrastructure inspection. A number of planning activities will be implemented including the planning workshops, an interdisciplinary lecture series, regular teleconference meetings, and thrust leaders’ convocations. Stakeholders of infrastructure owners, inspection service providers, community colleges will be involved to provide inputs to refine the roadmap to advance convergence research agenda. Concerted efforts will be targeting at fostering workforce training and innovation culture. Besides providing extensive opportunities for dynamic interactions and transdisciplinary engagement, the planning activities will also provide participants with training of essential team science skills. The effectiveness of the planning activities will be measured by timely collecting feedbacks, which will be incorporated to further improve the planning activities for the best outcomes. This project will catalyze the integration of robotics and AI-driven techniques to help efficiency and safety of workers during infrastructure inspection, crucial to the sustainability and resilience of our infrastructure system.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.

可持续、安全的基础设施承载着我们社会的繁荣。 国家在维护和升级基础设施以提供符合公民期望的适当服务标准方面面临着严重的基础设施挑战。 此外，我们社区的繁荣需要拥抱和建立新兴类型的服务基础设施（即，自动驾驶汽车、联网汽车、物联网等）以满足当今对数字化和互联世界的期望。 基础设施检查提供了重要的数据，以支持其管理，恢复和重建计划的决策，这些计划每年花费数十亿美元。各基础设施服务部门对其基础设施状况（如道路、桥梁、涵洞、输电线路等）进行例行检查。基础设施检查行业在私营部门雇用了数万名工人。 公共部门还保持内部劳动力，以检查公共基础设施，如州际公路，桥梁，堤坝等，虽然机器人和信息技术有显着进步，但要使它们完全集成以增强人类的检查和判断存在重大技术障碍。因此，人工检查仍然是各种基础设施部门信息收集的主流。 该规划拨款旨在确定机器人和人工智能驱动技术如何与人类检查员无缝集成的路线图，以提高其效率，可靠性和安全性。 我们的假设是，检查人员和技术的联系将支持合理的数据驱动基础设施决策，从而有助于提高现有基础设施系统的服务水平，并更好地瞄准高回报的未来基础设施投资。 因此，该项目将探索对基础设施检查工作者和公众的福利具有高度影响的领域中的人-技术关系的前沿。该项目的规划活动将实施一系列战略，以基础设施检查工作者为基础，建立人机一体化中心的路线图。综合研究主题定义在检查效率和可靠性，检查员安全和网络安全，支持检查决策的决策机，基础设施检查公众参与的开放框架等领域。 将开展一些规划活动，包括规划研讨会、跨学科系列讲座、定期电话会议和重点领导人会议。 基础设施所有者、检查服务提供商和社区学院的利益相关者将参与进来，为完善路线图提供投入，以推进融合研究议程。 我们会共同努力，以培育劳动力培训和创新文化为目标。 除了为动态互动和跨学科参与提供广泛的机会外，规划活动还将为参与者提供基本团队科学技能的培训。 将通过及时收集反馈来衡量规划活动的有效性，并将纳入反馈，以进一步改进规划活动，取得最佳成果。 该项目将促进机器人和人工智能驱动技术的整合，以帮助工人在基础设施检查期间提高效率和安全性，这对我们基础设施系统的可持续性和弹性至关重要。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。