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

CHS：中：与近端机器人进行数据介导的通信以进行紧急响应

• 批准号: 1764092
• 负责人: Daniel Szafir
• 金额: $ 119.41万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1764092&HistoricalAwards=false
• 关键词: CHS; Medium; Data; Mediated; Communication

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.

机器人可以通过在人类响应者可能危险或无法访问的环境中收集信息来增强应急响应团队，例如野火战斗，搜索和救援或飓风响应。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，从而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针，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机器人在现场收集的信息，尽管它在快速响应当地条件方面具有价值，因为当前的系统通常通过中央指挥所路由数据。该项目的目标是设计支持第一响应者更直接访问和分析的系统，同时不会通过使用错误数据来施加额外的干扰或操作风险。通过与几个当地响应小组的合作，项目团队将更好地了解响应者的需求和关注点，包括机器人收集的数据，使用增强现实技术满足这些需求的算法和可视化，以及与响应者实际工作实践相结合的系统。该项目还将根据旨在提高公众对科学的兴趣的项目目标，开发一系列演示，推广活动和技术挑战，包括高中生和计算机科学代表性不足的群体。总的来说，这项研究将发展机器人和可视化的基础知识，从而产生新的方法和工具，使响应者能够在现场利用机器人收集的数据。特别是，该项目将探索透视增强现实头戴式显示器（ARHMD）如何通过开发ARHMD系统为机器人收集的数据的现场分析提供直观而强大的媒介，该系统允许紧急救援人员在何处，何时以及如何获得数据的背景下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，使响应者能够通过交互式可视化主动分析可用数据，被动查看机器人收集环境信息时留下的数字痕迹和“数据丢失”，并按需查询特定信息，如摄像头馈送。该团队还将开发用于3D场景重建和同步定位和映射的新算法，这些算法将适用于各种各样的应用。总的来说，该项目将贡献关于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，校正和在现场自动联网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交互的信息。该奖项反映了NSF的法定使命，并被认为是值得通过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

Robust Pose Estimation Based on Normalized Information Distance

基于归一化信息距离的鲁棒位姿估计

• 发表时间: 2021
• 期刊: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
• 作者: Chen, Zhaozhong;Heckman, Christoffer
• 通讯作者: Heckman, Christoffer

Making Data Tangible: A Cross-disciplinary Design Space for Data Physicalization

让数据变得有形：数据物理化的跨学科设计空间

• DOI: 10.1145/3491102.3501939
• 发表时间: 2022
• 期刊: ACM SIGCHI Conference on Human Factors in Computing Systems (CHI
• 作者: Bae, S. Sandra;Zheng, Clement;West, Mary Etta;Do, Ellen Yi-Luen;Huron, Samuel;Szafir, Danielle Albers
• 通讯作者: Szafir, Danielle Albers

Exploring the Benefits and Challenges of Data Physicalization

探索数据物理化的好处和挑战

• 发表时间: 2022
• 期刊: Proceedings of ETIS 2022
• 作者: Bae, S. Sandra;Szafir, Danielle Albers;Do, Ellen Yi-Luen
• 通讯作者: Do, Ellen Yi-Luen

MRCAT: In Situ Prototyping of Interactive AR Environments

• DOI: 10.1007/978-3-030-49695-1_16
• 发表时间: 2020-07
• 作者: Matt Whitlock;J. Mitchell;N. Pfeufer;Brad Arnot;Ryan Craig;Bryce Wilson;Brian Chung;D. Szafir

Mediating Human-Robot Interactions with Virtual, Augmented, and Mixed Reality

通过虚拟、增强和混合现实调节人机交互

• 发表时间: 2019
• 期刊: International Conference on Human-Computer Interaction
• 作者: Szafir, Daniel