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.

先进空中交通 (AAM) 或以前的城市空中交通 (UAM) 的愿景是建立一个航空运输系统，利用革命性的新技术，在以前航空市场服务不足的地方（本地、区域、区域内、城市）之间运送人员和货物。电动垂直起降 (eVTOL) 飞机受到了联邦机构的高度关注，全球各地的公司都在竞相建造和测试 eVTOL 飞机，以确保 AAM 成为人们日常生活中不可或缺的一部分。巨大的经济潜力和社会影响，但其成功将取决于其在安全保证的情况下将运营规模扩大到预期的高需求的能力，具体而言，该项目为基于学习的安全和可扩展的规划和控制奠定了基础。项目将（i）重点关注可扩展的多代理飞机自主算法的进步，以实现实时间隔保证，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，以增强飞机的安全性；多智能体飞机自主； (iii) 为航班运营商和空域服务提供商设计协作交通流规划框架，以在 AAM 网络面临需求和容量不确定性时提高安全性和效率；以及 (iv) 整合已开发的模型和算法，构建自主的 AAM 生态系统；执行模拟/飞行测试和系统级验证的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。和可扩展的先进空中机动能力。它可能会对其他 CPS 工具的设计和实施方式产生影响，以支持民航、自动驾驶汽车/卡车和机器人技术中日益增强的自主性和无人驾驶操作。该项目在 (i) 学生创新竞赛、团队方面有一个综合教育计划。 (ii) 人工智能和航空航天领域的跨学科课程开发和改进；(iii) 将行业专家带入课堂；(iv) 国际学生研究交流，通过人工智能和机器学习吸引当选官员和政策制定者。播客系列，将提供该项目将通过人工智能和计算为航空航天领域的女性建立从高中到博士后的劳动力管道。该奖项符合 NSF 的法定使命，并被认为值得通过以下方式获得支持。使用基金会的智力价值和更广泛的影响审查标准进行评估。

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: 2024-01
• 期刊: American Institute of Aeronautics and Astronautics
• 作者: Binshuai Wang; James Pinto
• 通讯作者: James Pinto