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AEGIS: Autonomous Entity Global Intelligence System for Urban Air MobilityThis paper presents a global intelligence system that synthesizes aerial vehicles’ real-time physical data, planned actions, and historical behavior into engineered data frames representing the collective state of the airspace and suitable for efficient machine learning consumption. These data frames are then learnt by a deep neural net to build a prediction model that estimates the expected evolution path of the current state, thereby identifying potential future conflicts. This approach lends itself to an automated early warning system that the aerial vehicles can implement onboard with a suitable edge computing module more efficiently and effectively than non-AI methods, and eventually take preventive or corrective measures towards self/collaborative resolution of the issues. Contrary to a centralized early warning system where all vehicles’ task-space eventually converges to a global optimum state, the presented distributed global intelligence system brings in a balance between local utility functions of each vehicle and the global operating framework. This contributes to effectively handle the potential massive scaling in urban air mobility in the near future.
Document ID
20205001942
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Aditya Das
(Ames Research Center Mountain View, California, United States)
Kristina Marotta
(Ames Research Center Mountain View, California, United States)
Husni Idris
(Ames Research Center Mountain View, California, United States)
Date Acquired
May 7, 2020
Subject Category
Air Transportation And Safety
Meeting Information
Meeting: Aviation 2020
Location: Virtual
Country: US
Start Date: June 15, 2020
End Date: June 19, 2020
Sponsors: American Institute of Aeronautics and Astronautics
Funding Number(s)
PROJECT: 533127
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
Technical Review
NASA Technical Management
Keywords
Artificial Intelligence
Distributed Control
Autonomous Air Mobility
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