CAREER: Safe and Scalable Learning-based Control for Autonomous Air Mobility

职业：安全且可扩展的基于学习的自主空中交通控制

• 批准号: 2047390
• 负责人: Peng Wei
• 金额: $ 50万
• 依托单位: George Washington University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047390&HistoricalAwards=false
• 关键词: CAREER; Safe; Scalable; Learning; based

The vision for Advanced Air Mobility (AAM) or formerly Urban Air Mobility (UAM) is to enable an air transportation system that moves people and cargo between places previously underserved by the current aviation market (local, regional, intraregional, urban) using revolutionary new electric vertical take-off and landing (eVTOL) aircraft. AAM has received significant attention from federal agencies. Companies around the globe are competing to build and test eVTOL aircraft to ensure the AAM will become an integral part of people’s daily life. The AAM has enormous economic potential and societal impact, but its success will depend on its ability to scale the operations to the expected high demand with safety guarantee. This project lays the foundation of safe and scalable learning-based planning and control for autonomous air mobility. Concretely, the project will (i) focus on algorithmic advances of scalable multi-agent aircraft autonomy for real-time separation assurance to increase the airspace capacity; (ii) develop and integrate the online safety guard and offline adaptive stress testing model to provide safety enhancement for the multi-agent aircraft autonomy; (iii) design the collaborative traffic flow planning framework for flight operators and the airspace service provider to improve safety and efficiency when facing demand and capacity uncertainties on the AAM network; and (iv) integrate the developed models and algorithms to build an autonomous AAM ecosystem testbed to perform simulation/flight tests and system level validation. The multidisciplinary approach is based on multi-agent reinforcement learning, safe reinforcement learning, multi-agent stochastic game, and bi-level robust optimization. The proposed effort has transformative impacts to enable safe and scalable advanced air mobility. It could have impact in the way that other CPS tools are designed and implemented to support increasing autonomy and unmanned operations in civil aviation, autonomous cars/trucks, and robotics. The project has an integrated education plan in (i) student innovation competitions, teams and clubs; (ii) interdisciplinary curriculum development and improvement for AI and autonomy in aerospace; (iii) bringing industry experts to students in classroom; and (iv) international student research exchange. The project will engage elected officials and policy makers in AI and machine learning via podcast series, which will provide basic knowledge and insights on legal, ethical, and societal implications of AI. The project will establish a workforce pipeline from high school to postdoc for women in in aerospace via AI and computing.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.

先进航空机动性(AAM)或以前的城市空中机动性(UAM)的愿景是使用革命性的新型电动垂直起降(EVTOL)飞机，使航空运输系统能够在目前航空市场服务不足的地方(当地、地区、区域内、城市)之间运送人员和货物。AAM得到了联邦机构的高度关注。全球各地的公司都在竞相制造和测试eVTOL飞机，以确保AAM将成为人们日常生活中不可或缺的一部分。AAM具有巨大的经济潜力和社会影响，但它的成功将取决于其在安全保证的情况下将业务规模扩大到预期的高需求的能力。该项目为自主空中机动性安全、可扩展的基于学习的规划和控制奠定了基础。具体地说，该项目将(I)专注于可扩展多智能体飞机自主的算法进展，以实现实时分离保证，以增加空域容量；(Ii)开发和集成在线安全防护和离线自适应压力测试模型，为多智能体飞机自主性提供安全增强；(Iii)为航班运营商和空域服务提供商设计协同交通流规划框架，以提高面对AAM网络需求和容量不确定性时的安全性和效率；以及(Iv)整合开发的模型和算法，以构建自主AAM生态系统试验台，进行模拟/飞行测试和系统级验证。该多学科方法基于多智能体强化学习、安全强化学习、多智能体随机博弈和双层稳健优化。拟议的努力对实现安全和可扩展的先进空中机动性具有变革性影响。它可能会对其他CPS工具的设计和实施产生影响，以支持民航、自动驾驶汽车/卡车和机器人领域日益增长的自主性和无人操作。该项目在以下方面有一个综合教育计划：(I)学生创新竞赛、团队和俱乐部；(Ii)人工智能和航空航天自治方面的跨学科课程开发和改进；(Iii)将行业专家带到学生课堂上；以及(Iv)国际学生研究交流。该项目将通过播客系列吸引民选官员和政策制定者参与人工智能和机器学习，该系列将提供关于人工智能的法律、伦理和社会影响的基本知识和见解。该项目将通过人工智能和计算机为女性在航空航天领域建立从高中到博士后的劳动力管道。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

Identifying Similar Thunderstorm Sequences for Airline Decision Support via Optimal Transport Theory

通过最优运输理论识别类似的雷暴序列以支持航空公司决策

• DOI: 10.2514/6.2024-2011
• 发表时间: 2024
• 期刊: American Institute of Aeronautics and Astronautics
• 作者: Binshuai Wang, James Pinto