TY - GEN
T1 - Small unmanned aircraft system (sUAS) trajectory modeling in support of UAS traffic management (UTM)
AU - Ren, Liling
AU - Castillo-Effen, Mauricio
AU - Yu, Han
AU - Johnson, Eric
AU - Nakamura, Takuma
AU - Yoon, Yongeun
AU - Ippolito, Corey A.
N1 - Funding Information:
This work is partially supported by the NASA under contract NNA16BE13C. This work is partially supported by the NASA under contract NNA16BE13C. The authors would like to thank Dr. Parimal Kopardekar and Dr. Jaewoo Jung of NASA Ames for their support and guidance during this research. The authors would also like to thank Dr. Marcus A. Johnson of NASA Ames and Dr. William Premerlani, of GE Global Research for insightful discussions.
Publisher Copyright:
© 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
PY - 2017
Y1 - 2017
N2 - Small unmanned aircraft system (sUAS) as defined by the Federal Aviation Administration (FAA) refers to a small unmanned aircraft weighing less than 55 pounds on takeoff, and its associated elements that are required for the safe and efficient operation of the small unmanned aircraft in the national airspace system. The unmanned aircraft system (UAS) traffic management (UTM) system is envisioned by the National Aeronautics and Space Administration (NASA) to enable civilian low-altitude airspace and UAS operations by providing services such as airspace design, corridors, dynamic geofencing, severe weather and wind avoidance, congestion management, terrain avoidance, route planning and re-routing, separation management, sequencing and spacing, and contingency management. Trajectory modeling and prediction methods are foundational capabilities in support of UTM to achieve its goals. This paper presents a framework for the development and validation of trajectory modeling and prediction methods for diverse types of sUASs under nominal environment and under a variety of realistic potential hazards, including adverse environmental conditions, and vehicle and system failures. Results from initial analysis of major components of the framework are also presented. Detailed results from the development and validation will be reported in subsequent papers as the research progresses.
AB - Small unmanned aircraft system (sUAS) as defined by the Federal Aviation Administration (FAA) refers to a small unmanned aircraft weighing less than 55 pounds on takeoff, and its associated elements that are required for the safe and efficient operation of the small unmanned aircraft in the national airspace system. The unmanned aircraft system (UAS) traffic management (UTM) system is envisioned by the National Aeronautics and Space Administration (NASA) to enable civilian low-altitude airspace and UAS operations by providing services such as airspace design, corridors, dynamic geofencing, severe weather and wind avoidance, congestion management, terrain avoidance, route planning and re-routing, separation management, sequencing and spacing, and contingency management. Trajectory modeling and prediction methods are foundational capabilities in support of UTM to achieve its goals. This paper presents a framework for the development and validation of trajectory modeling and prediction methods for diverse types of sUASs under nominal environment and under a variety of realistic potential hazards, including adverse environmental conditions, and vehicle and system failures. Results from initial analysis of major components of the framework are also presented. Detailed results from the development and validation will be reported in subsequent papers as the research progresses.
UR - http://www.scopus.com/inward/record.url?scp=85070259201&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85070259201&partnerID=8YFLogxK
U2 - 10.2514/6.2017-4268
DO - 10.2514/6.2017-4268
M3 - Conference contribution
AN - SCOPUS:85070259201
SN - 9781624105081
T3 - 17th AIAA Aviation Technology, Integration, and Operations Conference, 2017
BT - 17th AIAA Aviation Technology, Integration, and Operations Conference, 2017
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - 17th AIAA Aviation Technology, Integration, and Operations Conference, 2017
Y2 - 5 June 2017 through 9 June 2017
ER -