Improved UAS Robustness through Augmented Onboard Intelligence, Phase I

Metadata Updated: February 28, 2019

This work will focus on the development of a highly capable avionics subsystem and machine learning algorithms to provide early warning of potential failures of critical subsystems on small UAS. This modular system will consist of networked onboard monitoring nodes capable of observing operations and providing notification of off-nominal conditions to the autopilot as well as the operator, mitigating the risk of failure, and providing critical information regarding required maintenance. The boards, while computationally powerful will be limited in size, weight and power to avoid significantly impacting the performance of current vehicles and simplify its installation. Furthermore, the networked devices will be able to communicate with each other as well as the autopilot, allowing for vehicle wide information to contribute to a high degree of awareness of the vehicle's well-being. The primary objectives are:

1.Determination of a set of subsystems commonly employed by UAS whose failure would cause a system critical issue. 2.The identification of a set of sensors and machine learning algorithms capable of providing the necessary inputs to detect the health and status of its associated subsystem, and determining the probability of a fault occurring in the near future. 3.The design of a monitoring node capable of interfacing to the required set of sensors and implementing the machine learning algorithms. The nodes will also be limited to a size and weight that will allow for them to be installed on most UAS without impacting the vehicle's performance. 4.The design of an onboard network capable of supporting communications between all smart monitoring nodes on the aircraft. Each node can then communicate any potential failures to the autopilot and/or operator as well as share information that will allow for the implementation of distributed machine learning algorithms between the nodes and recognition of cross-correlation between systems.

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Public: This dataset is intended for public access and use. License: U.S. Government Work

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Metadata Created Date August 1, 2018
Metadata Updated Date February 28, 2019

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date August 1, 2018
Metadata Updated Date February 28, 2019
Publisher Space Technology Mission Directorate
Unique Identifier TECHPORT_93550
Maintainer Email
Public Access Level public
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Harvest Object Id 08ad733b-ee76-46a0-bf5c-da6438581b71
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2017-12-01
Homepage URL
Data Last Modified 2018-07-19
Program Code 026:027
Source Datajson Identifier True
Source Hash 7e682e83ff1ce9c754f78b27c8601f65b42a47a4
Source Schema Version 1.1

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