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

小型无人驾驶空中、陆地或潜水器(无人机)越来越多地用于支持搜救、结构性建筑火灾和医疗运送等紧急响应场景。然而，在目前的实践中，无人机通常由单一操作员控制，从而大大限制了其潜力。拟议的工作将提供一个新颖的无人机响应平台，代表下一代应急解决方案，其中半自主和自我协调的无人机队列将作为应急小组的正式成员。无人机将在每个应急场景中扮演不同的角色--例如，使用热像仪绘制着火建筑的结构完整性地图，有条不紊地在玉米地寻找失踪的儿童，或者向被困在湍急河流中的人运送救生设备。这一项目的好处将由城乡社区实现，他们将受益于增强的应急能力。从这项工作中吸取的实际经验教训将广泛有助于围绕社区无人机部署最佳做法的讨论，包括与隐私、安全和公平有关的问题。实现无人机响应愿景需要提供新的场景识别算法，能够在不太理想的环境条件下重建环境的高保真模型。这项工作针对与以下方面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、处理不确定性和对对象进行地理定位；(2)探索、设计和评估人-CPS界面，这些界面提供态势感知，并使用户能够定义任务并交流当前的任务目标和成就；(3)开发算法，以支持无人机自主和针对人类制定的任务目标进行运行时适应；(4)开发一个框架，用于协调图像识别算法与实时无人机指挥和控制，以及最后(5)在真实世界的场景中评估DroneResponse。研究人员将利用以用户为中心的设计原则来开发支持情景感知的人性化CPS界面，旨在使应急人员能够做出明智的决定。最终目标是使人类操作员和无人机能够协同工作，拯救生命，最大限度地减少财产损失，收集关键信息，并为各种紧急情况下的任务的成功做出贡献。这一奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.