CAREER: Situational Awareness Strategies for Autonomous Systems in Dynamic Uncertain Environments

实现完全自主的网络物理系统（CPS）的潜在经济和社会影响是惊人的。如果联邦航空管理局（FAA）允许将无人机（UAV）整合到国家民用空域，私营部门的无人机行业估计将在十年内创造超过10万个高薪技术工作岗位，并为美国经济贡献820亿美元。预计自动驾驶汽车每年将防止500万起事故和200万起伤害，节省70亿升燃料，并在美国挽救3万人的生命和1900亿美元的医疗费用。目前的CPS还远没有具备这种能力，特别是在动态的，不确定的，建模不好的环境中，GPS覆盖可能是不稳定的，模糊的，或以其他方式受损。这就需要发展一个连贯的分析基础来处理这类新兴的CPS，其中态势感知和使命规划和执行交织在一起，必须同时考虑到解决不确定性，模型失配，并补偿潜在的GPS覆盖gaps.This项目是有四个主要目标：（1）分析未知的动态，随机环境，包括多个代理的可观测性。这种分析将建立随机可观测性所需的关于环境和/或代理的最小先验知识。(2)制定适应策略，以完善环境的代理模型，在飞行中，作为代理建立时空地图。适应是至关重要的，因为假设代理具有描述环境的高保真模型是不切实际的。(3)设计最佳的、计算效率高的信息融合算法，并保证性能。这些算法将考虑物理上现实的非线性动力学和有色，非高斯噪声，在CPS中经常遇到的意见。(4)综合最佳的实时决策制定策略，以平衡信息收集和使命履行的潜在冲突目标。这项调查将使自主CPS导航复杂的权衡，导致自主识别和采用的最佳策略。这项研究具有深远的影响-它将使自主CPS从仅仅感知环境发展到理解环境，在人类直接控制在物理上或经济上不可能的环境中带来新的能力。该项目有一个垂直整合的教育计划，涵盖K-12，本科生和研究生。该项目将让经济困难的初中和高中学生参与用于研究验证的同一无人机试验台。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。

The potential economic and societal impacts of realizing fully autonomous cyber-physical systems (CPS) are astounding. If the Federal Aviation Administration (FAA) allows integration of unmanned aerial vehicles (UAVs) into the national civilian airspace, the private-sector drone industry is estimated to generate more than 100K high-paying technical jobs over a ten-year span and contribute $82B to the U.S. economy. Self-driving cars are predicted to annually prevent 5M accidents and 2M injuries, conserve 7B liters of fuel, and save 30K lives and $190B in healthcare costs associated with accidents in the U.S. Successful mission pursuit of such fully autonomous CPS hinges on possessing full situational awareness including precise knowledge of its own location. Current CPS are far from possessing this capability, particularly in dynamic, uncertain, poorly modeled environments where GPS coverage may be spotty, obscured, or otherwise impaired. This necessitates developing a coherent analytical foundation to deal with this emerging class of CPS, in which situational awareness and mission planning and execution are intertwined and must be considered simultaneously to address uncertainty, model mismatch, and compensate for potential GPS coverage gaps.This project is has four main objectives: (1) Analyze the observability of unknown dynamic, stochastic environments comprising multiple agents. This analysis will establish the minimum a priori knowledge needed about the environment and/or agents for stochastic observability. (2) Develop adaptation strategies to refine the agents models of the environment, on-the-fly, as the agents build spatiotemporal maps. Adaptation is crucial, since it is impractical to assume that agents have high-fidelity models describing the environment. (3) Design optimal, computationally efficient information fusion algorithms with performance guarantees. These algorithms will consider physically realistic nonlinear dynamics and observations with colored, non-Gaussian noise, commonly encountered in CPS. (4) Synthesize optimal, real-time decision making strategies to balance the potentially conflicting objectives of information gathering and mission fulfillment. This investigation will enable autonomous CPS to navigate complex tradeoffs, leading to autonomous identification and adoption of the optimal strategy. This research has far-reaching impact- it will evolve autonomous CPS from merely sensing the environment to making sense of the environment, bringing new capabilities in environments where direct human control is not physically or economically possible. The project has a vertically-integrated education plan spanning K-12, undergraduate, and graduate students. The project will engage economically disadvantaged middle and high school students in the same UAV testbed used for research verification. Also, research outcomes will be infused into new and existing undergraduate and graduate courses.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.