CHS: Medium: Data-Mediated Communication with Proximal Robots for Emergency Response

机器人可以通过在人类响应人员可能危险或无法到达的环境中收集信息来增强应急团队，例如在野火扑救、搜索和救援或飓风响应中。例如，机器人可能会收集关键的视觉、地图和环境数据，以告知响应者前方的情况，这些情况可能会提高他们对操作环境的认识。这些数据将有助于规划和重新规划行动方案，并加强实地决策。然而，响应团队目前几乎没有能力直接访问现场机器人收集的信息，尽管它在快速响应当地条件方面有价值，因为目前的系统通常通过中央指挥所发送数据。该项目的目标是设计系统，支持对急救人员进行更直接的访问和分析，同时不会因为使用错误数据而造成额外的分心或操作风险。通过与几个当地应急小组的合作，项目组将更好地了解应急人员的需求和对机器人收集的数据、算法和可视化的担忧，这些数据、算法和可视化使用增强现实技术来满足这些需求，以及与应急人员的实际工作实践很好地结合的系统。该项目还将根据项目目标开发一系列演示、外联活动和技术挑战，旨在提高公众对科学的兴趣，包括在高中生和计算机科学领域代表性不足的群体中。总体而言，这项研究将发展机器人学和可视化的基础知识，导致新的方法和工具，使应急者能够在现场利用机器人收集的数据。特别是，该项目将探索透视式增强现实头盔显示器(ARHMD)如何通过开发ARHMD系统提供直观和强大的媒介，用于现场分析机器人收集的数据，该系统允许应急人员在何时何地以及如何获得数据的上下文中与机器人收集的信息进行交互。该团队将进行实证研究，以指导系统组件的设计，这些组件允许响应人员通过交互式可视化主动分析可用的数据，被动查看机器人在收集环境信息时留下的数字痕迹和“数据滴漏”，并按需查询特定信息，如摄像机视频。该团队还将开发用于3D场景重建以及同步定位和地图绘制的新算法，这些算法将对各种应用程序有用。总体而言，该项目将提供关于ARHMD可视化的不同因素如何影响数据解释的经验知识，用于估计、校正和在现场断断续续联网的代理之间共享地图的新算法，以及关于来自并置机器人的数据如何调解人-机器人交互的信息，特别是在紧急响应的背景下。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。

Robots may augment emergency response teams by collecting information in environments that may be dangerous or inaccessible for human responders, such as in wildfire fighting, search and rescue, or hurricane response. For example, robots might collect critical visual, mapping, and environmental data to inform responders of conditions ahead that could improve their awareness of the operational environment. These data would assist in planning and re-planning courses of action and enhance in-the-field decision making. However, response teams currently have little ability to directly access robot-collected information in the field, despite its value for rapidly responding to local conditions, because current systems typically route the data through a central command post. This project's goal is to design systems that support more direct access and analysis for first responders while not imposing additional distractions or operational risks through using faulty data. Through collaboration with several local response groups, the project team will develop better understandings of responders' needs and concerns around robot-collected data, algorithms and visualizations that meet those needs using augmented reality technologies, and systems that integrate well with responders' actual work practices. The project will also develop a series of demonstrations, outreach activities, and technology challenges based on the project goals aimed at increasing public interest in science, including among high school students and underrepresented groups in computer science. Overall, this research will develop fundamental knowledge in robotics and visualization, leading to new methods and tools that enable responders to take advantage of robot-collected data while in the field. In particular, this project will explore how see-through augmented reality head-mounted displays (ARHMDs) might offer an intuitive and powerful medium for in situ analysis of robot-collected data through developing an ARHMD system that allows emergency responders to interact with robot-collected information in the contexts of where, when, and how that data was obtained. The team will conduct empirical studies to guide the design of system components that allow responders to actively analyze available data through interactive visualization, passively view digital traces and "data drops" left by robots as they collect information about the environment, and query specific information such as camera feeds on-demand. The team will also develop novel algorithms for 3D scene reconstruction and simultaneous location and mapping that will be useful for a broad variety of applications. Overall, the project will contribute empirical knowledge of how different factors of ARHMD visualizations influence data interpretation, novel algorithms for estimating, correcting, and sharing maps between intermittently-networked agents in the field, and information regarding how data from collocated robots can mediate human-robot interactions, particularly within the context of emergency response.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.