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

Iowa State University, Ames IA

Investigators

Abstract

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.