FW-HTF-RM: Collaborative Research: Assistive Intelligence for Cooperative Robot and Inspector Survey of Infrastructure Systems (AI-CRISIS)

FW-HTF-RM：协作研究：协作机器人辅助智能和基础设施系统检查员调查 (AI-CRISIS)

• 批准号: 2026357
• 负责人: Genda Chen
• 金额: $ 80.77万
• 依托单位: Missouri University of Science and Technology
• 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=2026357&HistoricalAwards=false
• 关键词: FW; HTF; RM; Collaborative; Research

The U.S. civil infrastructure faces the prospect of rapid future deterioration. For example, 39% of the over 600,000 highway bridges in the U.S. have exceeded their design life of 50 years. To ensure the safety and welfare of citizens, state Departments of Transportation are required to inspect bridges every two years. To make inspection and reporting objective and consistent, robots such as unmanned aerial vehicles have recently been introduced to perform autonomous surveys of bridges. These robots, equipped with cameras, will rapidly provide a large set of survey data that can be used to aid inspectors in assessing the condition of bridges. To transform these multidisciplinary topics into a new integrated bridge inspection capability, this multi-university team will develop and implement a cooperative robot-inspector system with assistive intelligence (AI) in order to make the future bridge inspection significantly faster, cheaper, safer, and more consistent. A robotic platform equipped with infrared cameras and a central processing unit with intelligent algorithms will operate in both flying and crawling modes, travel in proximity to various parts/elements of a bridge, and collect high-fidelity images of the entire bridge. Inspectors will be instrumented and monitored with a suite of wearable sensors to enable optimal robot-inspector cooperation during each bridge inspection. AI algorithms will be developed in modules to analyze the big data from human sensors and cameras in support of the pre-inspection workforce training, during-inspection element defect detection, and post-inspection condition evaluation of bridges. The robot-inspector-AI system will be integrated and validated at six bridge sites in collaboration with stakeholders.This multidisciplinary project aims to explore and develop the scientific knowledge and underling methods of AI for a cooperative inspector-robot survey of bridges. The scope of work includes: (1) to develop an optimal screening-to-probing inspection strategy using fast and accurate detection of both surface and internal defects in reinforced concrete slabs (representative bridge elements) from active thermal and visible light imaging; (2) to create a causal model of inspectors’ task performance to support an optimal job design, effective training, and onsite operation; (3) to develop adaptive AI algorithms with human inspectors in the loop for a rapid adaptation to future work contexts, and efficient and reliable data analyses; and (4) to enable a spatial correlation among the elements of an entire bridge with a maximum use of domain expertise, and thus improve the effectiveness and applicability of adaptive AI. The methods and technologies developed in this study will be transferred into the hands of stakeholders, researchers, policy makers, and end users (bridge inspectors) through publications, presentations, field demonstrations, and training. In particular, the world’s first bridge benchmark dataset of videos, labeled objects, and metadata established from real-world bridges, upon approval by stakeholders, will be shared with the international community via an AI-enabled bridge inspection competition.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.

美国民用基础设施面临着未来迅速恶化的前景。例如，美国60多万座公路桥梁中有39%已经超过了50年的设计寿命。为了确保公民的安全和福利，州交通部必须每两年检查一次桥梁。为了使检查和报告客观和一致，最近引入了无人驾驶飞行器等机器人来执行桥梁的自主调查。这些配备摄像头的机器人将迅速提供大量调查数据，可用于帮助检查人员评估桥梁状况。为了将这些多学科课题转化为新的综合桥梁检测能力，这个多所大学的团队将开发和实施一个具有辅助智能（AI）的协作机器人检测系统，以使未来的桥梁检测更快，更便宜，更安全，更一致。配备红外摄像头和具有智能算法的中央处理器的机器人平台将在飞行和爬行模式下运行，在桥梁的各个部分/元件附近行驶，并收集整座桥梁的高保真图像。检查员将配备一套可穿戴传感器，以确保每次桥梁检查期间机器人与检查员的最佳合作。人工智能算法将在模块中开发，以分析来自人类传感器和摄像头的大数据，以支持检查前的劳动力培训，检查期间的元件缺陷检测和桥梁检查后的状况评估。机器人-检查员-人工智能系统将在六个桥梁现场与利益相关者合作进行集成和验证。这个多学科项目旨在探索和发展人工智能的科学知识和基础方法，用于桥梁检查员-机器人合作调查。工作范围包括：（1）发展一套最佳的从筛选至探测的检验策略，以快速及准确地检测钢筋混凝土板的表面及内部缺陷（2）创建检查员任务绩效的因果模型，以支持最佳的工作设计、有效的培训和现场操作;（3）开发自适应人工智能算法，人工检查员参与其中，以快速适应未来的工作环境，并进行高效可靠的数据分析;以及（4）在最大限度地使用领域专门知识的情况下实现整个桥的元件之间的空间相关性，从而提高自适应人工智能的有效性和适用性。本研究中开发的方法和技术将通过出版物、演示、现场演示和培训等方式，传递给利益相关者、研究人员、政策制定者和最终用户（桥梁检查员）。特别是，世界上第一个桥梁基准数据集的视频，标记的对象和元数据建立从现实世界的桥梁，经利益相关者批准，将通过一个人工智能桥梁检查比赛与国际社会共享。这一奖项反映了NSF的法定使命，被认为是值得支持的评估使用基金会的智力价值和更广泛的影响审查标准。

项目成果

A Virtual-Reality-Based Training and Assessment System for Bridge Inspectors With an Assistant Drone

• DOI: 10.1109/thms.2022.3155373
• 发表时间: 2022-03-25
• 期刊: IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
• 影响因子: 3.6
• 作者: Li, Yu;Karim, Muhammad Monjurul;Qin, Ruwen
• 通讯作者: Qin, Ruwen

Toward Explainable Artificial Intelligence for Early Anticipation of Traffic Accidents

迈向可解释的人工智能以早期预测交通事故

• DOI: 10.1177/03611981221076121
• 发表时间: 2022
• 期刊: Transportation Research Record: Journal of the Transportation Research Board
• 作者: Karim, Muhammad Monjurul;Li, Yu;Qin, Ruwen
• 通讯作者: Qin, Ruwen