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.

先进空气移动性（AAM）或以前的城市空气流动性（UAM）的愿景是实现一种空中运输系统，该系统使用革命性的新电力垂直起飞和着陆（EVTOL）飞机在当前航空市场（本地，区域，区域内，城市内部，城市）以前服务的地方之间移动人们和货物。 AAM受到了联邦机构的极大关注。全球公司正在竞争建造和测试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，但其成功将取决于其将运营扩展到预期的高需求和安全保证的能力。该项目奠定了基于安全和可扩展的基于学习的计划和控制空气移动性的基础。具体而言，该项目（i）将重点介绍可扩展的多代理飞机自治的算法进步，以实时分离保证，以提高空域的能力； （ii）开发和整合在线保安和离线自适应压力测试模型，以为多机构飞机自治提供安全增强； （iii）在AAM网络上面对需求和容量不确定性时，为飞行运营商和领空服务提供商设计协作流量流计划框架，以提高安全性和效率； （iv）集成了开发的模型和算法，以构建一个自主AAM生态系统测试以执行仿真/飞行测试和系统级别验证。多学科方法基于多代理增强学习，安全的增强学习，多代理随机游戏和双层强大的优化。拟议的努力具有变革性的影响，以实现安全可扩展的先进空气移动性。它可能会影响其他CPS工具的设计和实施，以支持在民航，自动驾驶汽车/卡车和机器人技术中增加自治和无人操作。该项目制定了（i）学生创新竞赛，团队和俱乐部的综合教育计划； （ii）AI和航空航天自治的跨学科课程发展和改进； （iii）将行业专家带到课堂上的学生； （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加AI和机器学习，该系列将提供有关AI法律，道德和社会影响的基本知识和见解。该项目将通过AI和Computing建立从高中到航空航天妇女的劳动力管道。该奖项反映了NSF的法定任务，并使用基金会的智力优点和更广泛的影响审查标准，通过评估被认为是珍贵的支持。

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