RAPID/Collaborative Research: Data Collection for Robot-Oriented Disaster Site Modeling at Champlain Towers South Collapse

快速/协作研究：尚普兰塔南倒塌的面向机器人的灾难现场建模数据收集

• 批准号: 2140573
• 负责人: David Merrick
• 金额: $ 7.23万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2140573&HistoricalAwards=false
• 关键词: RAPID; Collaborative; Research; Data; Collection

This Grant for Rapid Response Research (RAPID) will support a collaborative team of researchers from Florida State University, Texas A&M University, and Carnegie Mellon University to operate under the supervision of the Miami Dade Fire Rescue Department and Florida Task Force 1 at the site of the Champlain Towers South condominium collapse in Surfside, Florida. This project will address the need for a robot-oriented model of rubble by using unmanned aerial system (UAS) imagery and other contextual information. The lack of a robot-oriented model of rubble is a major barrier to the design and manufacture of effective, economical, and reliable ground robots for disasters and other extreme environments. Although structural engineering teams are also investigating the site, they do not capture data about the factors that impact whether a robot can navigate the interior of a building collapse. This project will benefit society by facilitating the design and deployment of robots to save lives, either to find survivors in rubble otherwise inaccessible to humans and dogs or by reducing the need for human responders to enter unsafe areas. The team is diverse, with a woman as the principal investigator, and will train a diverse set of students to conduct robotics research for disasters.The team will: 1) assist rescue, recovery, and forensic structural teams by collecting UAS images of the collapse from response through recovery, 2) collect and analyze data on UAS performance relating to flights, missions, data processing, and operations tempo, 3) analyze orthomosaic and digital elevation imagery to formally model traversability constraints for ground robots in extreme environments, including features such as scale, shape, and surface properties, 4) curate images for general use and archive on the Texas Data Repository open source dataverse site, and 5) attempt to create a 3D visualization of the voids in the interior of the rubble from the progressively uncovered site via a subtractive and labeling process. The research will create a new fundamental research methodology for analyzing disasters, and extreme environments in general, from the perspective of ground and aerial robotic systems. The image datasets may also enable the computer vision machine learning communities to recognize structural conditions and indications of survivors. The results of the study will be made freely available, including a workshop, and will improve use of robots in future disasters by formalizing design features and offering a rapid recognition of which robot types to deploy for what conditions. This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.

这项快速反应研究（RAPID）拨款将支持来自佛罗里达州立大学、德克萨斯州农工大学和卡内基梅隆大学的研究人员组成的合作团队，在迈阿密戴德消防救援部门和佛罗里达第1工作队的监督下，在佛罗里达萨夫赛德的尚普兰塔南公寓楼倒塌现场开展工作。该项目将利用无人机系统图像和其他背景信息，满足对面向机器人的瓦砾模型的需求。缺乏面向机器人的瓦砾模型是设计和制造用于灾害和其他极端环境的有效，经济和可靠的地面机器人的主要障碍。虽然结构工程团队也在调查现场，但他们没有捕捉到影响机器人能否在建筑物倒塌的内部导航的因素的数据。该项目将通过促进机器人的设计和部署来造福社会，以拯救生命，无论是在人和狗无法进入的瓦砾中寻找幸存者，还是减少人类救援人员进入不安全地区的需要。该团队由一名女性担任首席研究员，并将培训一批不同的学生进行灾难机器人研究。该团队将：1）通过收集从响应到恢复的倒塌的UAS图像来协助救援、恢复和法医结构团队，2）收集和分析关于与飞行、任务、数据处理和操作克里思相关的UAS性能的数据，3）分析正射和数字高程图像，以正式建模极端环境中地面机器人的可通行性约束，包括比例、形状和表面属性等特征，4）策划图像以供一般使用并在德克萨斯州数据库开源dataverse网站上存档，以及5）尝试通过减影和标记过程从逐渐暴露的位置创建碎石内部的空隙的3D可视化。该研究将为从地面和空中机器人系统的角度分析灾害和极端环境创造一种新的基础研究方法。图像数据集还可以使计算机视觉机器学习社区能够识别幸存者的结构状况和迹象。该研究的结果将免费提供，包括一个研讨会，并将通过正式确定设计特征和快速识别在什么条件下部署哪种机器人类型来改善机器人在未来灾难中的使用。该项目由跨部门的机器人基础研究项目支持，该项目由工程部（ENG）和计算机与信息科学与工程部（CISE）共同管理和资助。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。