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

基本信息

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&amp INT Traffic Deconfliction Smart

项目摘要

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.

无人驾驶汽车（UAV）操作从包装交付，线性基础设施检查到大规模的环境监控，带来了可观的科学和社会利益。但是，没有空降的追逐飞机或地面观察员的联邦航空管理局（FAA）尚未允许自动无人机操作，以确保交通不足，这是复杂且昂贵的。拟议项目的目的是使给定的无人机能够预测其相邻无人机的轨迹，动态评估碰撞风险，并自动解决潜在的冲突，而无需持续的人类监督。该项目试图：1）实施能够以可能满足未来FAA法规的方式操作众多无人机的能力； 2）建立无人机流量的数学表示，可以缩放以处理复杂，异质和高维流量； 3）与现有的商业空中交通和谐相处的民用和商业应用的成熟无人机操作。该项目旨在开发一种实时的，分布式的决策算法，即基于动态的网络基于网络的概率路线策划者（DNPRP），用于在充血环境中多个无人机的自主操作。关键的技术组件将包括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）3）时间相关的贝叶斯非参数基于高斯流程回归基于基于的轨迹预测模型和4）依赖于时间的相关碰撞概率图，用于碰撞途径映射，用于无冲突途径无冲突途径。对于物理飞行测试，将使用几个在封闭的户外设施（即弗吉尼亚理工无人机公园）内运行的多个无人机无人机进行评估。实时操作中计算成本和解决方案最优性之间的权衡将通过可测量的指标进行严格评估，这将为大型复杂系统的扩展提供信息。将研究结果转换为开源或商业软件的快速途径将与工业合作伙伴密切合作。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。