PFI:BIC: Pre-Departure Dynamic Geofencing, En-Route Traffic Alerting, Emergency Landing and Contingency Management for Intelligent Low-Altitude Airspace UAS Traffic Management

随着大量民用无人机应用的发展，大量的各类无人机需要在低空安全飞行。这些无人机还需要与通用航空和直升机等载人航空交通安全共享这一空间。美国联邦航空局预测，到2020年，UAS的销量将达到700万(商业和业余爱好者的总和)。该项目提出并集成了研究人员的新的操作概念和核心算法，形成了智能UAS交通管理系统(UTM)。这一UTM系统还将打破新的UAS应用的市场可行性障碍，如城市按需航空运输和UAS货物交付。最后，在开发拟议的UTM期间获得的见解可能会对其他以人为中心的智能服务系统和支持民用航空的网络物理系统的设计和实施产生深远影响，例如空中交通基础设施、运营商地面支持系统、通信、导航和监视设备以及车辆技术。这项研究提出了一个集成了大数据架构和计算能力的智能UTM系统，该系统将协调起飞前的UAS飞行计划，实时检测潜在的碰撞，生成解决潜在冲突的建议，主动控制紧急降落期间地面人员和物体的任何风险，并确定碰撞的原因。这些能力的目的是将碰撞次数降至最低，并减轻每次事故的影响。这将利用大规模优化、飞机制导和控制、预测建模、系统验证和确认以及用于信息呈现和决策支持的先进可视化技术来实现。拟议的系统有一个起飞前飞行计划协调模块，该模块查询已批准的飞行计划数据库，并对每个新请求的飞行计划进行一致性检查，以实现无冲突的起飞前交通协调。途中交通监控和警报模块接收实时飞机位置数据和活动飞行计划，执行对潜在碰撞的自动预测，并生成解决碰撞的建议。紧急着陆和应急管理模块查询多个数据库，如地形图、障碍物数据、空域数据、公共安全数据和实时飞机位置数据，建议紧急着陆地点并计算相应的着陆路径，以将对地面人员和物体的影响风险降至最低。最后，先进的人机界面将以直观的方式提供信息可视化和决策支持，以减少认知效率低下并最大限度地提高人在环路中的性能，以增强UAS交通控制器的能力。拟议的系统将作为正在进行的NASA UTM的补充部分。研究计划分为三个阶段：(第一阶段)智能UTM用户需求的识别和综合，(第二阶段)智能UTM核心算法和系统原型的开发，以及(第三阶段)智能UTM的测试、评估和集成。这种学术和产业合作伙伴关系由一个多学科学术研究团队领导：爱荷华州立大学(牵头机构)、爱荷华州大学(爱荷华市)和密歇根大学(密歇根州安娜堡)，以及主要工业合作伙伴罗克韦尔柯林斯(Cedar Rapids，IA)和Mosaic ATM(Small Business，Leesburg，弗吉尼亚州)与更广泛的上下文合作伙伴联邦航空管理局威廉·J·休斯技术中心(FAA技术中心)(政府机构，新泽西州蛋港镇)。合作伙伴还将收到美国联邦航空局爱荷华州办事处和Uber Elevate的反馈。这一合作关系将确保拟议的UTM系统符合美国联邦航空局的规定、用户要求和市场需求。

With the development of numerous civilian Unmanned Aerial System (UAS) applications, a large number of unmanned aircraft of various types need to be safely operated in low-altitude airspace. These UAS also need to safely share this space with manned aviation traffic, such as general aviation and helicopters. The FAA forecasts 7 million UAS sales (commercial and hobbyist combined) by 2020. This project advances and integrates the investigators' novel concepts of operations and core algorithms into an intelligent UAS Traffic Management system (UTM). This UTM system would also break market feasibility barriers for new UAS applications such as urban on-demand air transportation and UAS cargo delivery. Finally, the insights gained during the development of the proposed UTM could have profound impact on design and implementation of other human-centered smart service systems and cyber-physical systems that support civil aviation, e.g., air traffic infrastructures, operator ground support systems, communication, navigation and surveillance devices, and vehicle technologies. This research proposes an intelligent UTM system integrating big data architecture and computation power that will coordinate pre-departure UAS flight plans, detect potential collisions in real time, generate recommendations to resolve potential conflictions, proactively control any risk to people and objects on the ground during an emergency landing, and identify the cause of collisions. The aim of these capabilities is to minimize the number of collisions and mitigate the impact of each accident. This will be achieved using large-scale optimization, aircraft guidance and control, predictive modeling, system verification and validation, and advanced visualization techniques for information presentation and decision support. The proposed system has a pre-departure flight plan coordination module that queries the approved flight plan database and performs conformance checking for every newly requested flight plan to achieve conflict-free pre-departure traffic coordination. An en route traffic monitoring and alerting module receives real-time aircraft position data and active flight plans, performs automated prediction for potential collision, and generate recommendations to resolve collisions. An emergency landing and contingency management module queries multiple databases such as terrain maps, obstacle data, airspace data, public safety data and real-time aircraft position data to suggest emergency landing site and calculate the corresponding landing path to minimize the impact risk to people and objects on the ground. Finally, the advanced human machine interface will provide information visualization and decision support in an intuitive way to reduce cognitive inefficiencies and maximize human-in-the-loop performance to augment UAS traffic controller capabilities. The proposed system will serve as a complementary component of an ongoing NASA UTM. The research plan has three phases: (Phase 1) Identification and synthesis of intelligent UTM user requirements, (Phase 2) Development of the intelligent UTM core algorithms and system prototype, and (Phase 3) intelligent UTM testing, evaluation, and integration.This academe-industry partnership is lead by a multidisciplinary academic research team: Iowa State University (lead institution), University of Iowa (Iowa City, IA),and University of Michigan (Ann Arbor, MI),) with primary industrial partners Rockwell Collins (Cedar Rapids, IA) and Mosaic ATM (small business, Leesburg, VA) together with broader context partners the Federal Aviation Administration William J. Hughes Technical Center (FAA Tech Center) (government agency, Egg Harbor Township, NJ). The partners will also receive feedback from the FAA Iowa office and Uber Elevate. This partnership will ensure that the proposed UTM system meets FAA regulations, user requirements, and market needs.