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

这笔快速反应研究补助金(RAPID)将支持一个由佛罗里达州立大学、德克萨斯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州第一特遣部队的监督下，在佛罗里达州桑弗赛德的尚普兰大厦南公寓倒塌现场进行操作。该项目将利用无人驾驶航空系统(UAS)的图像和其他背景信息，解决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难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。尽管结构工程团队也在调查现场，但他们没有收集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拯救生命，从而造福社会，无论是在瓦砾中寻找幸存者，否则人类和狗都无法接近，或者通过减少人类响应人员进入不安全区域的需要。该团队是多元化的，由一名女性担任首席研究员，并将培训一组不同的学生进行灾难的机器人研究。该团队将：1)通过收集从响应到恢复的UAS坍塌图像来协助救援、恢复和取证结构团队，2)收集和分析与飞行、任务、数据处理和操作节奏有关的UAS性能数据，3)分析正射镶嵌和数字高程图像，以正式建模地面机器人在极端环境中的可穿越限制，包括比例、形状和表面属性等特征，4)整理通用图像并将其存档到德克萨斯数据仓库开放的DataAverse网站上，以及5)尝试通过减法和标记过程从逐渐暴露的地点创建瓦砾内部的空洞的3D可视化。这项研究将为从地面和空中机器人系统的角度分析灾害和一般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指示。这项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并提供对在什么条件下部署哪些类型的机器人的快速识别，来改进机器人在未来灾难中的使用。该项目由跨部门机器人基础研究计划支持，该计划由工程学指导委员会(ENG)和计算机与信息科学与工程指导委员会(CEISE)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优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.