With the REALM-1 on-orbit system having been fully transitioned from Payload status to System on ISS, the CEP/machine learning approaches remain a central focus going forward.
The REALM-1 core system was considered sufficiently matured in FY19 and was transitioned to an operational ISS system midway between FY19 and mid-FY20, so that ISS became responsible for sustaining engineering of flight and ground REALM-1 assets. A ground-based CEP center receives data from the ISS open-air readers and provides operational intelligence that infers item locations. REALM-1 infrastructure was developed and evaluated on ISS, with RFID open-air readers and antennas deployed in ISS Node 1, U.S. The REALM task is divided into five sub-technology projects: REALM-1, -2, -3, -6DoF, and REALM-RFID Sensing. 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. Sparse zone technologies typically cover greater volume per reader, but are more apt to miss tags because the radiated power density is typically lower in comparison. For example, dense zone technologies can be made highly accurate but typically entail greater mass compared to sparse zone 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. Mission details might drive a specific combination of one or more of these three technologies. With both dense and sparse zones, guaranteed real-time, on-demand reads may 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. These technologies include fixed-zone readers, steered-beam antenna readers, and mobile readers such as robotic elements, crew-held readers, or crew-worn readers. Sparse zone technologies address all areas exclusive of the dense zones, including the open areas of a habitat module. To address these complex problems, associated RFID technologies are partitioned into three classes:ĭense Zone technologies pertain to regions of high stowage densities where sufficient signal penetration from external antennas is unlikely. The problem of locating all mission items within and around a vehicle is 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. The third demonstration, REALM-3, is currently in development and is expected to launch in May of 2022. The second ISS demonstration, REALM-2, was commissioned in late 2021 and will continue through 2022. The first ISS demonstration, REALM-1, started in February 2017 and was completed at the end of FY19 when it was transitioned to the ISS Program for sustaining operations.
#Nasa iss real time tracking series
The REALM project is conducting a series of ISS technology demonstrations. 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 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.
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