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

先进空中交通（AAM）或以前的城市空中交通（UAM）的愿景是利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当前航空市场（本地，区域，区域内，城市）以前服务不足的地方之间运送人员和货物。AAM已经得到了联邦机构的高度关注。世界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。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障的情况下扩大运营规模以满足预期高需求的能力。该项目为自主空中机动的安全和可扩展的基于学习的规划和控制奠定了基础。具体而言，该项目将(i)专注于可扩展的多智能体飞机自治算法的进步，以确保实时分离，以增加空域容量；（ii）开发并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）为航班营运商和空域服务提供者设计协同交通流规划框架，以提高空管网络在面对需求和容量不确定性时的安全性和效率；（iv）集成开发的模型和算法，以建立一个自主的AAM生态系统测试平台，以执行模拟/飞行测试和系统级验证。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级鲁棒优化。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工具的设计和实施方式产生影响，以支持民用航空、自动驾驶汽车/卡车和机器人技术中越来越多的自主和无人操作。该项目在以下方面有一个综合教育计划：(1)学生创新竞赛、团队和俱乐部；（ii）航空航天领域人工智能和自主性的跨学科课程开发和改进；（三）将行业专家带入课堂；（四）国际学生研究交流。该项目将通过播客系列，让民选官员和政策制定者参与人工智能和机器学习，提供有关人工智能的法律、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技术，为航空航天领域的女性建立一条从高中到博士后的劳动力管道。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 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.