S&AS: INT: Traffic Deconfliction for Smart and Autonomous Unmanned Aircraft Systems in Congested Environments

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• 批准号: 1849300
• 负责人: Xi Chen
• 金额: $ 80万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-15 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1849300&HistoricalAwards=false
• 关键词: S& INT; Traffic; Deconfliction; Smart

Unmanned air vehicle (UAV) operations bring considerable scientific and societal benefits with its applications ranging from package delivery, linear infrastructure inspection, to large-scale environmental monitoring. However, Autonomous UAV operation is not yet allowed by the Federal Aviation Administration (FAA) without airborne chase aircraft or ground observers to ensure traffic deconfliction, which is complicated and costly. The goal of the proposed project is to enable a given UAV to predict the trajectories of its neighboring UAVs, dynamically assess collision risks, and automatically resolve potential conflicts without the need for constant human supervision. This project seeks to: 1) implement the ability to operate a multitude of UAVs without direct human oversight in a way that might satisfy future FAA regulations; 2) establish a mathematical representation of a UAV traffic that can be scaled to handle a complex, heterogeneous, and high-dimensional traffic; and 3) mature UAV operations for civil and commercial applications in harmony with existing commercial air traffic.The project aims at developing a real-time, distributed decision-making algorithm, the Dynamic Network-based Probabilistic Route Planner (DNPRP), for autonomous operation of multiple UAVs in congested environments. The key technical components will include 1) a cloud-sourced database system for UAV data sharing, 2) an algorithm for virtual and dynamic UAV network discovery, 3) time-dependent, Bayesian nonparametric Gaussian process regression-based trajectory prediction models, and 4) a time-dependent collision probability map for conflict-free route generation. For physical flight testing, DNPRP will be evaluated by using a couple of multirotor UAVs that operate within an enclosed outdoor facility (i.e., the Virginia Tech Drone Park) for hours to days without human intervention. Trade-offs between computational cost and solution optimality in real-time operation will be assessed rigorously with measurable metrics, which will inform possible extensions to large-scale complicated systems. A rapid path for transitioning the research results to open-source or commercial software will be formed in close collaboration with an industrial partner.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.

无人驾驶飞行器(UAV)的应用范围从包裹递送、线性基础设施检查到大规模环境监测，带来了可观的科学和社会效益。然而，美国联邦航空管理局(FAA)尚未允许自主无人机在没有机载追逐机或地面观察员的情况下运行，以确保交通疏导，这是复杂和昂贵的。拟议项目的目标是使给定的无人机能够预测其邻近无人机的轨迹，动态评估碰撞风险，并自动解决潜在冲突，而不需要持续的人工监督。该项目旨在：1)实现在没有直接人类监督的情况下以一种可能满足未来FAA规则的方式操作大量无人机的能力；2)建立可缩放的无人机交通的数学表示，以处理复杂、异质和高维的交通；3)成熟的民用和商用无人机操作与现有的商业空中交通相协调。该项目旨在开发一种实时、分布式决策算法--基于动态网络的概率路径规划(DNPRP)，用于在拥堵环境中的多个无人机的自主操作。关键技术组件将包括1)用于无人机数据共享的云源数据库系统，2)用于虚拟和动态无人机网络发现的算法，3)基于时间依赖的、基于贝叶斯非参数高斯过程回归的轨迹预测模型，以及4)用于无冲突路线生成的依赖于时间的碰撞概率图。对于物理飞行测试，DNPRP将使用几架多旋翼无人机进行评估，这些无人机在封闭的户外设施(即弗吉尼亚理工大学无人机公园)内运行数小时至数天，而不需要人为干预。实时操作中的计算成本和解的最优性之间的权衡将用可测量的指标进行严格的评估，这将为大规模复杂系统的可能扩展提供信息。通过与行业合作伙伴的密切合作，将形成一条将研究成果转化为开源或商业软件的快速途径。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。

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