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。如果美国联邦航空管理局（FAA）允许无人驾驶飞行器（uav）进入国家民用空域，私营无人机行业预计将在10年内创造超过10万个高薪技术工作岗位，为美国经济贡献820亿美元。据预测，在美国，自动驾驶汽车每年可防止500万起事故和200万起伤害，节省70亿升燃料，挽救3万人的生命，并节省1900亿美元的医疗费用。这种全自动CPS的成功实现取决于拥有全面的态势感知能力，包括对自身位置的精确了解。目前的CPS远不具备这种能力，特别是在动态的、不确定的、模型不佳的环境中，GPS覆盖范围可能是零星的、模糊的，或者以其他方式受损。这就需要开发一个连贯的分析基础来处理这种新兴的CPS，其中态势感知和任务规划和执行是交织在一起的，必须同时考虑解决不确定性、模型不匹配和补偿潜在的GPS覆盖差距。该项目有四个主要目标：(1)分析由多个智能体组成的未知动态随机环境的可观测性。这种分析将建立关于环境和/或随机可观察性的代理所需的最小先验知识。(2)制定适应策略，在智能体构建时空地图的过程中，实时完善环境智能体模型。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)设计性能保证的最优、计算效率高的信息融合算法。这些算法将考虑物理上真实的非线性动力学和有色非高斯噪声观测，通常在CPS中遇到。(4)综合最优实时决策策略，平衡信息收集和任务实现的潜在冲突目标。这项研究将使自主CPS能够进行复杂的权衡，从而自主识别和采用最佳策略。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，在物理上或经济上无法直接控制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。该项目将让经济条件较差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。此外，研究成果将被注入到新的和现有的本科和研究生课程。该奖项反映了美国国家科学基金会的法定使命，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.