CPS:Medium:Interactive Human-Drone Partnerships in Emergency Response Scenarios

CPS：中：紧急响应场景中的交互式人机合作伙伴关系

• 批准号: 1931962
• 负责人: Jane Huang
• 金额: $ 118.64万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931962&HistoricalAwards=false
• 关键词: CPS; Medium; Interactive; Human; Drone

Small unmanned aerial, land, or submersible vehicles (drones) are increasingly used to support emergency response scenarios such as search-and-rescue, structural building fires, and medical deliveries. However, in current practice drones are typically controlled by a single operator thereby significantly limiting their potential. The proposed work will deliver a novel DroneResponse platform, representing the next generation of emergency response solutions in which semi-autonomous and self-coordinating cohorts of drones will serve as fully-fledged members of an emergency response team. Drones will play diverse roles in each emergency response scenario - for example, using thermal imagery to map the structural integrity of a burning building, methodically searching an area for a child lost in a cornfield, or delivering a life-saving device to a person caught in a fast-flowing river. The benefits of this project will be realized by urban and rural communities who will benefit from enhanced emergency response capabilities. Practical lessons learned from this work will broadly contribute to the conversation around best practices for drone deployment in the community including issues related to privacy, safety, and equity. Achieving the DroneResponse vision involves delivering novel scene recognition algorithms capable of recreating high-fidelity models of the environment under less than ideal environmental conditions. The work addresses non-trivial cyber-physical systems (CPS) research challenges associated with (1) scene recognition, including image merging, dealing with uncertainty, and geolocating objects; (2) exploring, designing, and evaluating human-CPS interfaces that provide situational awareness and empower users to define missions and communicate current mission objectives and achievements, (3) developing algorithms to support drone autonomy and runtime adaptation with respect to mission goals established by humans, (4) developing a framework for coordinating image recognition algorithms with real-time drone command and control, and finally (5) evaluating DroneResponse in real-world scenarios. Researchers will leverage user-centered design principles to develop human-CPS interfaces that support situational awareness designed to enable emergency responders to make informed decisions. The end goal is to empower human operators and drones to work collaboratively to save lives, minimize property damage, gather critical information, and contribute to the success of a mission across diverse emergency scenarios.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.

小型无人机、陆地或潜水器（无人机）越来越多地用于支持紧急响应场景，例如搜索救援、结构建筑火灾和医疗运送。然而，在目前的实践中，无人机通常由单个操作员控制，从而极大地限制了它们的潜力。拟议的工作将提供一个新颖的 DroneResponse 平台，代表下一代应急响应解决方案，其中半自主和自我协调的无人机群将作为应急响应团队的正式成员。 无人机将在每种紧急响应场景中发挥不同的作用，例如，使用热图像绘制燃烧建筑物的结构完整性图，有条不紊地搜索在玉米地中失踪的儿童的区域，或者向被抓获的人提供救生设备在一条湍急的河流中。 该项目的好处将由城市和农村社区实现，他们将从增强的应急响应能力中受益。 从这项工作中汲取的实践经验教训将广泛促进围绕社区无人机部署最佳实践的对话，包括与隐私、安全和公平相关的问题。 实现 DroneResponse 愿景需要提供新颖的场景识别算法，该算法能够在不太理想的环境条件下重新创建高保真环境模型。 这项工作解决了与以下相关的重要网络物理系统（CPS）研究挑战：（1）场景识别，包括图像合并、处理不确定性和地理定位对象； (2) 探索、设计和评估人机 CPS 界面，提供态势感知并帮助用户定义任务并传达当前任务目标和成就，(3) 开发算法以支持无人机自主性和运行时适应，以实现由人类，(4) 开发一个框架，用于协调图像识别算法与实时无人机命令和控制，最后 (5) 在现实场景中评估 DroneResponse。 研究人员将利用以用户为中心的设计原则来开发支持态势感知的人机交互界面，旨在使应急响应人员能够做出明智的决策。最终目标是使人类操作员和无人机能够协同工作，以拯救生命、最大限度地减少财产损失、收集关键信息，并为在各种紧急情况下成功完成任务做出贡献。该奖项反映了 NSF 的法定使命，并被认为是值得的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来提供支持。

项目成果

ProCon: An automated process-centric quality constraints checking framework

ProCon：以流程为中心的自动化质量约束检查框架

• DOI: 10.1016/j.jss.2023.111727
• 发表时间: 2023-08
• 期刊: Journal of Systems and Software
• 影响因子: 3.5
• 作者: Mayr;Vierhauser, Michael;Bichler, Stefan;Keplinger, Feli;Cleland;Egyed, Alexander;Mehofer, Thomas
• 通讯作者: Mehofer, Thomas

GRuM — A flexible model-driven runtime monitoring framework and its application to automated aerial and ground vehicles

GRuM – 灵活的模型驱动运行时监控框架及其在自动化空中和地面车辆中的应用

• DOI: 10.1016/j.jss.2023.111733
• 发表时间: 2023-09
• 期刊: Journal of Systems and Software
• 影响因子: 3.5
• 作者: Vierhauser, Michael;Garmendia, Antonio;Stadler, Marco;Wimmer, Manuel;Cleland
• 通讯作者: Cleland

Model-Driven Requirements for Humans-on-the-Loop Multi-UAV Missions

人类在环多无人机任务的模型驱动要求

• DOI: 10.1109/modre51215.2020.00007
• 发表时间: 2020-08
• 期刊: Model-Driven Requirements Engineering
• 作者: Agrawal, Ankit;Cleland;Steghofer, Jan
• 通讯作者: Steghofer, Jan

Requirements-driven configuration of emergency response missions with small aerial vehicles

小型飞行器应急响应任务的需求驱动配置

• DOI: 10.1145/3382025.3414950
• 发表时间: 2020-10-19
• 期刊: Proceedings of the 24th ACM Conference on Systems and Software Product Line: Volume A - Volume A
• 作者: J. Clel;Ankit Agrawal;M. N. A. Islam;Eric Tsai;Maxime Van Speybroeck;Michael Vierhauser
• 通讯作者: Michael Vierhauser

Engineering Challenges for AI-Supported Computer Vision in Small Uncrewed Aerial Systems

小型无人航空系统中人工智能支持的计算机视觉的工程挑战

• DOI: 10.1109/cain58948.2023.00033
• 发表时间: 2023-05-01
• 期刊: 2023 IEEE/ACM 2nd International Conference on AI Engineering – Software Engineering for AI (CAIN)
• 作者: Muhammed Tawfiq Chowdhury;J. Clel
• 通讯作者: J. Clel