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: 2233316
• 负责人: Daniel Szafir
• 金额: $ 119.41万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2233316&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（例如在野火战斗，搜救和救援）或飓风响应中收集可能危险或无法接近的环境中收集信息来增加紧急响应小组。例如，机器人可能会收集关键的视觉，映射和环境数据，以告知响应者未来的条件，以提高他们对操作环境的认识。这些数据将有助于计划和重新规划行动方案并增强现场决策。但是，响应团队目前几乎没有能力直接访问现场的机器人收集信息，尽管它的价值可以快速响应当地条件，因为当前系统通常通过中央命令帖子将数据路由。该项目的目标是设计系统，该系统支持急救人员进行更多直接访问和分析，同时不会通过使用错误的数据施加其他干扰或操作风险。通过与几个本地响应小组的合作，项目团队将更好地理解响应者的需求以及围绕机器人收集的数据，算法和可视化的疑虑，这些数据，算法和可视化措施使用增强现实技术以及与响应者实际工作实践良好整合的系统满足了这些需求。该项目还将根据旨在增加对科学的公众兴趣的项目目标，在包括高中生和计算机科学领域的代表性不足的群体中，制定一系列示范，外展活动和技术挑战。总体而言，这项研究将开发机器人技术和可视化方面的基本知识，从而导致新的方法和工具，使响应者能够在现场时利用机器人收集的数据。特别是，该项目将探讨如何通过开发一个允许紧急响应者与何时，何时以及如何获得该数据的网络上的信息进行交互，从而为机器人收集的数据提供现场分析，以便如何为机器人收集的数据提供直观且有力的媒介，以对机器人收集的数据进行现场分析。该团队将进行实证研究，以指导系统组件的设计，这些系统组件使响应者可以通过交互式可视化积极分析可用数据，被动地查看数字痕迹和机器人收集有关环境信息的“数据删除”，以及查询特定信息（例如相机供稿）。该团队还将开发用于3D场景重建和同时位置和映射的新颖算法，这将对各种应用程序有用。总体而言，该项目将对ARHMD可视化的不同因素如何影响数据解释，新颖的算法来估算，纠正和共享该现场的算法之间的新算法，并提供经验知识基金会的智力优点和更广泛的影响评论标准。

项目成果

A Mixed Reality Supervision and Telepresence Interface for Outdoor Field Robotics

• DOI: 10.1109/iros51168.2021.9636090
• 发表时间: 2021-09
• 期刊: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: M. Walker;Zhaozhong Chen;Matt Whitlock;David Blair;D. Szafir;C. Heckman;D. Szafir

Virtual, Augmented, and Mixed Reality for Human-robot Interaction: A Survey and Virtual Design Element Taxonomy

• DOI: 10.1145/3597623
• 发表时间: 2022-02
• 期刊: ACM Transactions on Human-Robot Interaction
• 影响因子: 5.1
• 作者: M. Walker;Thao Phung;Tathagata Chakraborti;T. Williams;D. Szafir