ERI: Drones-Enabled Active Sensing System (DEASS) for Detection of Subsurface Defects in Civil Infrastructure through Synchronized Drone Swarming and Thermal Imaging

该奖项根据2021年美国救援计划法案（公法117-2）全部或部分资助。该工程研究启动（ERI）项目将探索由一对无人机携带的主动热成像系统，一个（领导者）用于热成像，另一个（追随者）用于主动加热（例如，微波），以使得能够在GPS拒绝环境中的民用基础设施的下一代检查中进行地下缺陷检测（例如，桥面下）。由于其有限的有效载荷和电池时间，单个无人机无法支持主动感测所需的额外有效载荷及其相关附件。因此，引入了两个领导者-追随者无人机的概念。该奖项支持通过同步无人机群集和热成像开发无人机启用的主动传感系统的基础研究。这项新技术将改变目前被动的表面劣化检测实践，这是受阳光条件的变化，到一个主动的热成像检测的地下条件恶化，如分层和水平裂缝的钢筋混凝土结构。除了基础设施的检查和维护之外，这项新技术还将通过协作机器人集群和主动传感影响多个前沿领域，例如交互式制造和医疗保健，以及科学和工程学术界。技术和研究成果将通过国家研讨会转移到工程专业人员手中。无人机启用的主动传感系统将通过软件/硬件在环仿真和实验测试进行研究。这项研究将克服两个技术挑战：（1）在节能策略中同步无人机群集和热成像，以及（2）利用分布在领导者-追随者无人机平台上的热源实现新的热成像可探测性，以刺激主动成像中的表面下缺陷的出现。为多机器人群体协作和主动感知的多领域研究奠定了基础。首先，它将开发一种新的无人机集群的领导者-跟随者编队策略，基于三维立体视觉和势场算法，在GPS拒绝环境中合作主动感知和检查地下缺陷期间进行视觉相对导航和避碰。其次，它将建立微波加热混凝土桥面的主动传感的优化特性，优化微波激励（能量源，频率和持续时间）和传感协议（测量灵敏度，帧速率和时间窗口）。第三，它将开发一种方法来评估和改进合作无人机群编队的性能，并对标记固体甲板混凝土桥梁和潜在的现场基础设施检查中的缺陷进行主动热成像。该奖项反映了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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).This Engineering Research Initiation (ERI) project will explore an active thermal imaging system carried by a pair of drones, one (leader) for thermal imaging and the other (follower) for active heating (e.g., microwave), to enable subsurface defect detection in the next-generation inspection of civil infrastructure in a GPS-denied environment (e.g., underneath bridge decks). Due to its limited payload and battery time, a single drone cannot support additional payload and its associated accessories required for active sensing. The concept of two leader-follower drones is thus introduced. This award supports fundamental research for the development of a drones-enabled active sensing system through synchronized drone swarming and thermal imaging. The new technique will transform the current passive surface deterioration inspection practice, which is subject to the change in sunlight conditions, into an active thermal imaging inspection for subsurface condition deterioration, such as delamination and horizontal cracks in reinforced concrete structures. In addition to infrastructure inspection and maintenance, the new technique will impact multiple frontiers with cooperative robot swarming and active sensing, such as interactive manufacturing and healthcare, and academic communities in science and engineering. The technique and research results will be transferred into the hands of engineering professionals through a national workshop.The drones-enabled active sensing system will be investigated via software/hardware-in-the-loop simulations and experimental tests. This research will overcome two technical challenges: (1) to synchronize drone swarming and thermal imaging in an energy-efficient strategy and (2) to enable new thermal imaging detectability with a heat source distributed on a leader-follower drone platform to stimulate the appearance of subsurface defects in active imaging. It will lay a foundation on cooperative robot swarming and active sensing research in multiple frontiers. First, it will develop a new strategy of leader-follower formation for drone swarming with visual relative navigation and collision avoidance based on a three-dimensional stereo vision and potential field algorithm during cooperative active sensing and inspection of subsurface defects in a GPS-denied environment. Second, it will establish optimal characteristics of active sensing of microwave heated concrete decks with optimized microwave excitations (energy source, frequency and duration) and sensing protocols (measurement sensitivity, frame rate and time window). Third, it will develop a method to evaluate and improve the performance of cooperative drone swarm formation and active thermal imaging of defects in a markup solid deck concrete bridge and a potential field infrastructure inspection.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.