I-Corps: Reducing the Threat from Active Shooters Using Miniature Air Vehicles

这个i-Corps项目的更广泛的影响/商业潜力是开发一种非致命性建筑防御系统，该系统将保证没有人能在没有立即被发现和残疾的情况下拔枪。在首次响应人员到达活跃的枪击现场所需的时间内，许多手无寸铁的平民的生命可能会被夺走。这项技术试图消除这种时间延迟，并使袭击者失去意识。这项拟议的技术有可能对社会产生重大影响，因为大规模杀害儿童和无辜平民会在几十年内留下深深的伤痕。这项技术将使人们在任何建筑中都感到更安全，减少压力和焦虑，并可能带来更高的生产力。应用包括礼拜场所、商店、住宅和商业企业。此外，拟议中的技术将允许那些负担不起保安费用的人免受枪手的伤害。对于那些负担得起保安费用的人来说，这项技术提供了更高水平的防御，也将有助于消除枪战。这个i-Corps项目是基于开发一种由两个组件组成的非致命性建筑防御系统，该系统由一个坚固、准确的目标检测和分类系统和一个高度机动的微型飞行器(MAV)组成。拟议的研究基于使用计算机视觉、人工智能和机器学习算法的武器检测，以开发一个多步骤和多层系统，该系统结合了多传感器数据流，这些数据流通过对象融合子系统提供实时动态世界模型，基于规则的系统用于改进，风险评估系统用于建议适当的行动，以及物理模型。这多个步骤和多个层次将增加正确识别武器和威胁情况的可能性。第二个组件的研究将集中在MAV的设计上，这种MAV足够小，足够快，很难防御，同时能够消除威胁。MAV将配备先进的目标跟踪和非致命性失效有效载荷。这项研究将促进对雷诺数低于50,000的MAV和空气动力学表面的科学和技术理解，以及对具有极低误报的自主系统的理解和实现。该奖项反映了NSF的法定使命，并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，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of a non-lethal building defense system that will guarantee that no one can draw a firearm without being immediately detected and disabled. In the time that it takes for first-time responders to arrive at an active shooting scene, the lives of many defenseless civilians may be taken. This technology seeks to eliminate this time delay and to render the assailant unconscious. The proposed technology has the potential of having high impact on society as mass killings of children and innocent civilians leave deep scars for many decades. This technology will allow people to feel safer in any building, decreasing stress and anxiety, and may result in greater productivity. Applications include houses of worship, stores, homes, and commercial businesses. In addition, the proposed technology will allow those who cannot afford security guards to be safe from shooters. For those who can afford security guards, this technology offers a higher level of defense that also will help eliminate shoot-outs. This I-Corps project is based on the development of a two-component, non-lethal building defense system composed of a robust, accurate target detection and classification system and a highly maneuverable Miniature Air Vehicle (MAV). The proposed research is based on weapon detection using computer vision, artificial intelligence, and machine learning algorithms to develop a multistep and multilayer system that combines multi-sensor data streams that feed a real-time dynamic world model through an object fusion subsystem, a rule-based system for refinement, a risk assessment system to advise appropriate actions, and physics models. These multiple steps and multiple layers will increase the likeliness of correctly identifying a weapon and a threating situation. Research for the second component will focus on the design of a MAV that will be small enough and fast enough to be hard to defend against, while capable of removing the threat. The MAV will be equipped with advanced target tracking with a non-lethal disabling payload. This research will advance scientific and technological understanding of MAV and aerodynamic surfaces operating at Reynolds numbers below 50,000 and the understanding and achievement of autonomous systems with exceptionally low false positives.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.