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

随着民用无人机系统（UAS）应用的不断发展，大量的各类无人机需要在低空空域安全运行。这些无人机还需要与载人航空交通（如通用航空和直升机）安全地共享这一空间。美国联邦航空局预测，到2020年，UAS的销售量将达到700万台（商业和业余爱好者）。该项目将研究人员的新操作概念和核心算法集成到智能UAS交通管理系统（UTM）中。该UTM系统还将打破新的UAS应用的市场可行性障碍，例如城市按需航空运输和UAS货物交付。最后，在拟议的UTM开发过程中获得的见解可能对其他以人为本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的影响，例如，空中交通基础设施、操作员地面支持系统、通信、导航和监视设备以及车辆技术。该研究提出了一种集成大数据架构和计算能力的智能UTM系统，该系统将协调出发前的UAS飞行计划，真实的时间检测潜在的碰撞，生成解决潜在冲突的建议，在紧急降落期间主动控制地面人员和物体的任何风险，并确定碰撞的原因。这些能力的目的是尽量减少碰撞次数，减轻每次事故的影响。这将通过大规模优化、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高级可视化技术来实现。建议的系统有一个起飞前的飞行计划协调模块，查询批准的飞行计划数据库，并执行一致性检查，为每个新请求的飞行计划，以实现无冲突的起飞前的交通协调。航路交通监控和警报模块接收实时飞机位置数据和主动飞行计划，对潜在碰撞进行自动预测，并生成解决碰撞的建议。紧急降落和应急管理模块查询地形图、障碍物数据、空域数据、公共安全数据和实时飞机位置数据等多个数据库，以建议紧急降落地点并计算相应的降落路径，以最大限度地降低对地面人员和物体的影响风险。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回路中的性能，以增强UAS交通控制器的能力。拟议中的系统将作为正在进行的NASA UTM的补充组成部分。 该研究计划分为三个阶段：（第一阶段）识别和综合智能UTM用户需求，（第二阶段）开发智能UTM核心算法和系统原型，以及（第三阶段）智能UTM测试，评估和集成。这个行业合作伙伴关系由多学科学术研究团队领导：爱荷华州州立大学（牵头机构），爱荷华州大学（爱荷华州市，IA）和密歇根大学（安阿伯，密歇根州），与主要工业合作伙伴罗克韦尔柯林斯（锡达拉皮兹，IA）和马赛克ATM（小型企业，弗吉尼亚州利斯堡）与更广泛的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）。合作伙伴还将收到来自FAA爱荷华州办公室和Uber Elevate的反馈。这种合作关系将确保拟议的UTM系统符合FAA法规、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.