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: 2140451
• 负责人: Robin Murphy
• 金额: $ 5.77万
• 依托单位: Texas A&M Engineering Experiment Station
• 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 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2140451&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.

这项快速响应研究赠款（Rapid）将支持来自佛罗里达州立大学，得克萨斯州农工大学和卡内基·梅隆大学的研究人员的合作团队，以在迈阿密达德消防救援部和佛罗里达特遣部队的监督下进行操作。尚普兰（Champlain）在佛罗里达州苏尔菲斯（Surfside）的南公寓倒塌。该项目将通过使用无人驾驶系统（UAS）图像和其他上下文信息来满足以机器人为导向的瓦砾模型的需求。缺乏面向机器人的瓦砾模型是设计和制造有效，经济和可靠的地面机器人为灾难和其他极端环境设计的主要障碍。尽管结构工程团队也在调查该站点，但他们并未捕获有关影响机器人是否可以导航建筑物内部崩溃的因素。该项目将通过促进机器人的设计和部署来挽救生命，从而使社会受益，要么在瓦砾中找到幸存者，否则人类和狗无法接近，或者通过减少人类响应者进入不安全地区的需求。该团队多样化，女性是主要的调查员，将培训一系列学生为灾难进行机器人研究研究。该团队将：1）通过收集崩溃的UAS图像来协助救援，康复和法医结构团队从恢复的响应到恢复，2）收集和分析与飞行，任务，数据处理和操作节奏有关的UAS性能的数据，3）分析正组菌和数字高程图像，以正式建模极端环境中地面机器人的遍历性约束，包括包括此类功能作为尺度，形状和表面特性，4）策划图像，用于在德克萨斯州数据存储库开源数据源站点上进行一般使用和存档，以及5）尝试从逐渐发现的rob虫内部的空隙中创建3D可视化。通过减法和标签过程站点。该研究将创建一种新的基本研究方法，用于分析灾难和极端环境，从地面和空中机器人系统的角度来看。图像数据集还可以使计算机视觉机器学习社区能够识别结构状况和幸存者的指示。该研究的结果将免费提供，包括研讨会，并通过形式化设计功能并在未来的灾难中改善机器人的使用，并提供快速认识到哪种机器人类型在哪种条件下进行部署。该项目得到了机器人技术计划的跨指导基础研究的支持，该项目由工程局（ENG）以及计算机和信息科学与工程（CISE）共同管理和资助。该奖项反映了NSF的法定任务，并被认为是值得的。通过基金会的智力优点和更广泛的影响评估标准通过评估来支持。

项目成果

A Comparison of Point Cloud Registration Techniques for On-Site Disaster Data from the Surfside Structural Collapse

表面结构倒塌现场灾害数据点云配准技术比较

• 发表时间: 2022
• 期刊: IEEE International Symposium on Safety Security Rescue Robotics
• 作者: Bal, A.;Ladig, R.;Goyal, P.;Galeotti, J.;Choset, H.;Merrick, D.;Murphy, R.
• 通讯作者: Murphy, R.

Harnessing AI and robotics in humanitarian assistance and disaster response

在人道主义援助和灾难应对中利用人工智能和机器人技术

• DOI: 10.1126/scirobotics.adj2767
• 发表时间: 2023
• 期刊: Science Robotics
• 影响因子: 25
• 作者: Manzini, Thomas;Murphy, Robin R.;Heim, Eric;Robinson, Caleb;Zarrella, Guido;Gupta, Ritwik
• 通讯作者: Gupta, Ritwik

Analysis of Interior Rubble Void Spaces at Champlain Towers South Collapse

尚普兰塔南倒塌内部碎石空隙空间分析

• 发表时间: 2022
• 期刊: IEEE International Symposium on Safety Security Rescue Robotics
• 作者: Rao, A.;Murphy, R.;Merrick, D.;Choset, H.
• 通讯作者: Choset, H.