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Logistics Reduction: RFID Enabled Autonomous Logistics Management (REALM)

Metadata Updated: November 12, 2020

The Advanced Exploration Systems (AES) Logistics Reduction (LR) project Radio-frequency identification (RFID) Enabled Autonomous Logistics Management (REALM) task focuses on the subset of autonomous logistics management functions pertaining to automated localization and inventory of all physical assets pertaining to, or within, a vehicle utilizing RFID technologies. REALM technology can provide detailed data to enable autonomous operations such as automated crew procedure generation and robotic interaction with logistics and deep space habitats; this is especially of value where communication delays with Earth drive the need for self-reliance.  The REALM project will conduct a series of ISS technology demonstrations.  The first demonstration, REALM-1, started in February 2017.The problem of locating all mission items within and around a vehicle are complicated by many factors, including the desire to rely only on passive tags, restrictions on RF transmit power, layered storage of logistics, the challenging RF scattering environment of vehicles, and metallic storage enclosures. To address this complex problem, associated RFID technologies are partitioned into three classes:Dense Zone technologiesSparse Zone technologiesComplex Event ProcessingDense Zone technologies pertain to enclosures with conductive, or shielded, boundaries and an integrated RFID reader to interrogate the items contained therein. Sparse zone technologies address all areas exclusive of the dense zones, including the open areas of a habitat module in addition to crevices, for example, behind a rack. These technologies include fixed-zone readers, steered-beam antenna readers, and mobile readers such as robotic elements, crew-held readers, or crew-worn readers. With both dense and sparse zones, guaranteed real-time, on-demand reads are not possible, so “smart” applications, e.g., Complex Event Processing (CEP), are required to infer item locations based on context from the sparse and dense zone technologies.Mission details might drive a different combination of these three technologies. Therefore, in addition to maturing these individual technology areas, the LR REALM team will learn which combinations of technologies are best suited for specific missions. For example, dense zone technologies can be made highly accurate but entail greater mass compared to sparse zone technologies. Sparse zone technologies typically cover greater volume per reader, but are more apt to miss tags because they cover a larger area. They still require readers, cables, and antennas to accomplish their function. The operational intelligence provided by CEP can likely be traded for the size, weight, and power associated with dense and sparse zone technologies, but the extent, and specific implementation, remain as knowledge gaps to be addressed by this effort.The REALM task is divided into three sub-technology projects: REALM-1, 2, and 3.REALM-1REALM-1 infrastructure will be developed and evaluated on ISS, with RFID hatch readers and antennas deployed in ISS Node 1, U.S. Laboratory, and Node 2. A ground- based CEP center will receive data from the ISS hatch readers and will provide operational intelligence that infers item locations. This effort is in collaboration with the ISS program payloads office and the ISS vehicle office, both of which provide cost sharing for development.  In FY15, manufacturing of the hatch readers, known as EMBER (EMBEdded RFID Reader), began, along with resident software development. In parallel, the CEP center was established, and the CEP team, including a university partner, began tailoring prior CEP work to NASA’s REALM goals. The REALM Test Bed was utilized for testing CEP concepts of operation prior to the processing of ISS REALM-1 data in succeeding years. In FY16, the hatch readers, antennas, and RF cables were developed

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Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Metadata Created Date November 12, 2020
Metadata Updated Date November 12, 2020

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date November 12, 2020
Metadata Updated Date November 12, 2020
Publisher Space Technology Mission Directorate
Unique Identifier Unknown
Identifier TECHPORT_93175
Data First Published 2020-09-01
Data Last Modified 2020-01-29
Public Access Level public
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Harvest Source Title NASA Data.json
Homepage URL
Program Code 026:027
Source Datajson Identifier True
Source Hash 6fa1cedd678d8bd74ee0678010783ac253b49f1d
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