Intelligent Contingency Management for Urban Air Mobility The third aviation revolution is seeking to enable transportation where users have access to immediate and flexible air travel; the users dictate trip origin, destination and timing. One of the major components of this vision is urban air mobility (UAM) for the masses. UAM means a safe and efficient system for vehicles to move passengers and cargo within a city. In order to reach UAM’s full market potential the vehicle will have to be autonomous. One of the primary challenges of autonomous flight is dealing with off-nominal events, both common and unforeseen; thus, intelligent contingency management (ICM) is one of the enabling technologies. In this context, the vehicle has to be aware of its internal state and external environment at all times, ascertain its capability and make decisions about mission completion or modification. All of these functions require data to model and assess the environment and then take actions based on these models. Necessarily, there is uncertainty associated with the data and the models generated from it. Since we are dealing with safety-critical systems, one of the main challenges of ICM is to generate sufficient data and to minimize its uncertainty to enable practical and safe decision making. We propose an overall architecture that incorporates deterministic and learning algorithms together to assess vehicle capabilities, project these into the future and make decisions on mission management level. A layered approach allows for mature parts and technologies to be integrated into early highly automated vehicles before the final state of autonomy is reached.
Document ID
20205006970
Acquisition Source
Langley Research Center
Document Type
Conference Paper
Authors
Irene M Gregory (Langley Research Center Hampton, Virginia, United States)
Newton H Campbell (Science Applications International Corporation (United States) McLean, Virginia, United States)
Natasha A Neogi (Langley Research Center Hampton, Virginia, United States)
Jon B Holbrook (Langley Research Center Hampton, Virginia, United States)
Barton J Bacon (Langley Research Center Hampton, Virginia, United States)
Daniel D Moerder (Langley Research Center Hampton, Virginia, United States)
Benjamin M Simmons (Langley Research Center Hampton, Virginia, United States)
Michael J Acheson (Langley Research Center Hampton, Virginia, United States)
Patrick C Murphy (Langley Research Center Hampton, Virginia, United States)
Thomas C Britton (KBR (United States) Houston, Texas, United States)
Jacob W Cook (Langley Research Center Hampton, Virginia, United States)
Jared A Grauer (Langley Research Center Hampton, Virginia, United States)
Date Acquired
August 28, 2020
Publication Date
October 3, 2020
Publication Information
Subject Category
Aircraft Stability And Control
Meeting Information
Meeting: The Dynamic Data Driven Applications Systems (DDDAS)/InfoSymbiotics2020 (DDDAS2020) conference
Location: Virtual
Country: US
Start Date: October 2, 2020
End Date: October 4, 2020
Sponsors: Springer Publishing Company (United States)
Funding Number(s)
WBS: 109492.02.07.07.07
Distribution Limits
Public
Copyright
Public Use Permitted.
Technical Review
Single Expert
Keywords
data-driven systemsautonomydecision-makingUrban Air Mobilityintelligent contingency management