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

职业：动态不确定环境中自主系统的态势感知策略

• 批准号: 2240512
• 负责人: Zak Kassas
• 金额: $ 50万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2240512&HistoricalAwards=false
• 关键词: CAREER; Situational; Awareness; Strategies; Autonomous

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.

实现完全自主的网络物理系统(CP)的潜在经济和社会影响令人震惊。如果美国联邦航空管理局(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预计将在十年内创造超过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，自动驾驶汽车每年将防止500万起事故和200万人受伤，节省70亿升燃料，并挽救3万人的生命和1.9亿美元的医疗费用，与美国的事故相关。成功实现这种完全自动驾驶的CPS任务取决于拥有全面的态势感知，包括对自己位置的准确了解。目前的CP远远不具备这种能力，特别是在动态、不确定、建模不佳的环境中，GPS覆盖可能参差不齐、模糊或以其他方式受损。这就需要开发一个连贯的分析基础来处理这类新兴的CP，其中态势感知与任务规划和执行相互交织，必须同时考虑以解决不确定性、模型失配和补偿潜在的GPS覆盖差距。该项目有四个主要目标：(1)分析由多个代理组成的未知动态、随机环境的可观性。这一分析将建立关于环境和/或代理人的最低先验知识，以实现随机可观测性。(2)制定适应策略，在智能体构建时空地图时，实时改进环境的智能体模型。适应是至关重要的，因为假设代理人拥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带有有色、非高斯噪声的观测，这在CPS中很常见。(4)综合最佳的、实时的决策策略，以平衡信息收集和任务完成的潜在冲突目标。这项调查将使自主CP能够进行复杂的权衡，导致自主识别和采用最优策略。这项研究具有深远的影响-它将使自主CP从仅仅感知环境演变为理解环境，在物理上或经济上无法直接由人类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，涵盖了K-12、本科生和研究生。该项目将让经济困难的初中生和高中生在同一个无人机试验台上进行研究验证。此外，研究成果将被注入到新的和现有的本科生和研究生课程中。这一奖项反映了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。

项目成果

Autonomous Ground Vehicle Path Planning in Urban Environments Using GNSS and Cellular Signals Reliability Maps: Simulation and Experimental Results

• DOI: 10.1109/taes.2021.3054684
• 发表时间: 2021-08
• 期刊: IEEE Transactions on Aerospace and Electronic Systems
• 影响因子: 4.4
• 作者: S. Ragothaman;Mahdi Maaref;Z. Kassas

UAV Waypoint Opportunistic Navigation in GNSS-Denied Environments

• DOI: 10.1109/taes.2021.3103140
• 发表时间: 2021-08
• 期刊: IEEE Transactions on Aerospace and Electronic Systems
• 影响因子: 4.4
• 作者: Z. Kassas;Yanhao Yang;Joe J. Khalife;Joshua Morales

A Lower Bound for the Error Covariance of Radio SLAM with Terrestrial Signals of Opportunity

地面机会信号无线电 SLAM 误差协方差下界

• DOI: 10.33012/2021.18100
• 发表时间: 2021
• 期刊: The International Technical Meeting of the Satellite Division of The Institute of Navigation
• 作者: Nguyen, Alexander A.;Shadram, Zeinab;Kassas, Zaher M.
• 通讯作者: Kassas, Zaher M.

Autonomous Integrity Monitoring for Vehicular Navigation With Cellular Signals of Opportunity and an IMU

• DOI: 10.1109/tits.2021.3055200
• 发表时间: 2022-06
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: Mahdi Maaref;Z. Kassas

Tightly Coupled Inertial Navigation System With Signals of Opportunity Aiding

• DOI: 10.1109/taes.2021.3054067
• 发表时间: 2021-01
• 期刊: IEEE Transactions on Aerospace and Electronic Systems
• 影响因子: 4.4
• 作者: Joshua Morales;Z. Kassas