Intra-Spacecraft RFID Localization
Presentation addresses machine learning approaches to RFID localization. Experiences with the REALM-1 system on ISS are shared, as are the expansions with REALM-2 and REALM-3.
RFID↗
SEARCH · Search NASA
Results for “RFID Localization”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Short Window Intra-Spacecraft RFID Localization
Logistics management has emerged as a key component to activities conducted in space. The RFID Enabled Autonomous Logistics Management (REALM) system has played a key role in providing cargo tracking capabilities in the noisy environment of the ISS. Currently, the inferencing engines used by REALM to predict the location of RFID tagged items operate on an hour of data. Movements aboard space stations occur on the scales of seconds. In this work we propose a new inferencing engine, that produces an embedding space that represents the location of RFID marked cargo on the scale of 30 seconds to 2 minutes of data, allowing for the categorization of movement of cargo, and predictions of a coarse location in less time than existing engines.
RFID↗
A Tool for the Automated Collection of Space Utilization Data: Three Dimensional Space Utilization Monitor
Space Human Factors and Habitability (SHFH) Element within the Human Research Program (HRP), in collaboration with the Behavioral Health and Performance (BHP) Element, is conducting research regarding Net Habitable Volume (NHV), the internal volume within a spacecraft or habitat that is available to crew for required activities, as well as layout and accommodations within that volume. NASA is looking for innovative methods to unobtrusively collect NHV data without impacting crew time. Data required includes metrics such as location and orientation of crew, volume used to complete tasks, internal translation paths, flow of work, and task completion times. In less constrained environments methods for collecting such data exist yet many are obtrusive and require significant post‐processing. Example technologies used in terrestrial settings include infrared (IR) retro‐reflective marker based motion capture, GPS sensor tracking, inertial tracking, and multiple camera filmography. However due to constraints of space operations many such methods are infeasible, such as inertial tracking systems which typically rely upon a gravity vector to normalize sensor readings, and traditional IR systems which are large and require extensive calibration. However multiple technologies have not yet been applied to space operations for these explicit purposes. Two of these include 3‐Dimensional Radio Frequency Identification Real‐Time Localization Systems (3D RFID‐RTLS) and depth imaging systems which allow for 3D motion capture and volumetric scanning (such as those using IR‐depth cameras like the Microsoft Kinect or Light Detection and Ranging / Light‐Radar systems, referred to as LIDAR).
Vos, Gordon A.↗
A Tool for the Automated Collection of Space Utilization Data: Three Dimensional Space Utilization Monitor
Space Human Factors and Habitability (SHFH) Element within the Human Research Program (HRP) and the Behavioral Health and Performance (BHP) Element are conducting research regarding Net Habitable Volume (NHV), the internal volume within a spacecraft or habitat that is available to crew for required activities, as well as layout and accommodations within the volume. NASA needs methods to unobtrusively collect NHV data without impacting crew time. Data required includes metrics such as location and orientation of crew, volume used to complete tasks, internal translation paths, flow of work, and task completion times. In less constrained environments methods exist yet many are obtrusive and require significant post-processing. Examplesused in terrestrial settings include infrared (IR) retro-reflective marker based motion capture, GPS sensor tracking, inertial tracking, and multi-camera methods Due to constraints of space operations many such methods are infeasible. Inertial tracking systems typically rely upon a gravity vector to normalize sensor readings,and traditional IR systems are large and require extensive calibration. However, multiple technologies have not been applied to space operations for these purposes. Two of these include: 3D Radio Frequency Identification Real-Time Localization Systems (3D RFID-RTLS) Depth imaging systems which allow for 3D motion capture and volumetric scanning (such as those using IR-depth cameras like the Microsoft Kinect or Light Detection and Ranging / Light-Radar systems, referred to as LIDAR)
Vos, Gordon A.↗
Ultra Long-Lived, Self-Surveying Autonomous Air Quality Sensing - Executive Summary
We set out to evolve ultra-low power air quality sensing technologies developed at JSC to add a highly accurate positioning sensor based on SBIR technology to give a self-surveying air quality monitoring platform with years-long lifetime on a small, disposable coin cell battery. Using Radio Frequency Identification (RFID) technology for data transport, the system can take advantage of RFID-based inventory management systems in place on lunar exploration assets to provide this capability with extremely small SWAP impacts. Years-long operational lifetimes enable flexible, autonomous environmental monitoring during lengthy intervals between and unprecedented situational awareness during crewed missions. Software integration of the localization system into the JSC RFID sensing platform was advanced, but the COVID-19 pandemic complicated and slowed maturation of the localization system SBIR product, and center closure indefinitely deferred a final hardware integration and system demonstration. In the meantime, progress was made to mature the air-quality sensing platform for flight, including hardware, software, antenna, and mechanical improvements. The underlying RFID sensing capability was also adapted to a drawer motion sensing system, which is currently (FY21) being taken toward an ISS flight demonstration as part of the RFID Enhanced Autonomous Logistics Management (REALM)-3 experiment.
Raymond Summers Wagner↗
An Analysis of the Effects of RFID Tags on Narrowband Navigation and Communication Receivers
The simulated effects of the Radio Frequency Identification (RFID) tag emissions on ILS Localizer and ILS Glide Slope functions match the analytical models developed in support of DO-294B provided that the measured peak power levels are adjusted for 1) peak-to-average power ratio, 2) effective duty cycle, and 3) spectrum analyzer measurement bandwidth. When these adjustments are made, simulated and theoretical results are in extraordinarily good agreement. The relationships hold over a large range of potential interference-to-desired signal power ratios, provided that the adjusted interference power is significantly higher than the sum of the receiver noise floor and the noise-like contributions of all other interference sources. When the duty-factor adjusted power spectral densities are applied in the evaluation process described in Section 6 of DO-294B, most narrowband guidance and communications radios performance parameters are unaffected by moderate levels of RFID interference. Specific conclusions and recommendations are provided.
LaBerge, E. F. Charles↗
Logistics Reduction: RFID Enabled Autonomous Logistics Management (REALM) (LR-REALM)
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 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. The second ISS demonstration, REALM-2, started late2019 and will continue for at least one year.
NASA TechPort↗
Robonaut 2 - Preparing for Intra-Vehicular Mobility on the International Space Station
Robonaut 2 (R2) has been undergoing experimental trials on board the International Space Station (ISS) for more than a year. This upper-body anthropomorphic robotic system shown in Figure 1 has been making steady progress after completing its initial checkout. R2 demonstrated free space motion, physically interacted with its human crew mates, manipulated interfaces on its task board and has even used its first tool. This steady growth in capability will lead R2 to its next watershed milestone. Developers are currently testing prototype robotic climbing appendages and a battery backpack in preparation of sending flight versions of both subsystems to the ISS in late 2013. Upon integration of its new components, R2 will be able to go mobile inside the space station with a twofold agenda. First, R2 will learn to maneuver in microgravity in the best possible laboratory for such a task. Second, it will start providing early payback to the ISS program by helping with intra-vehicular (IVA) maintenance tasks. The experience gained inside the ISS will be invaluable in reducing risk when R2 moves to its next stage and is deployed as an extra-vehicular (EVA) tool. Even on its current fixed base stanchion, R2 has already shown its capability of performing several maintenance tasks on the ISS. It has measured the air flow through one of the stations vents and provided previously unavailable real time flow data to ground operators. R2 has cleaned its first handrail, exciting some crew members that perhaps Saturday morning housekeeping on the station may someday become a task they can hand off to their robotic colleague. Other tasks, including using radio frequency identification (RFID) tools for inventory tasks or vacuuming air filters, have also been suggested and will be explored. Once mobile, R2 will take on these tasks and more to free up crew time for more important science and exploration pursuits. In addition to task exploration, research and testing is happening on orbit to prepare for R2 mobility operations. The current vision system in R2 s head is being used to identify and localize IVA handrails throughout the US Lab and ground control software is being updated and integrated in advance of supporting mobility operations.
Badger, Julia↗
Detection, Identification, Location, and Remote Sensing Using SAW RFID Sensor Tags
The Electromagnetic Systems Branch (EV4) of the Avionic Systems Division at NASA Johnson Space Center in Houston, TX is studying the utility of surface acoustic wave (SAW) radiofrequency identification (RFID) tags for multiple wireless applications including detection, identification, tracking, and remote sensing of objects on the lunar surface, monitoring of environmental test facilities, structural shape and health monitoring, and nondestructive test and evaluation of assets. For all of these applications, it is anticipated that the system utilized to interrogate the SAW RFID tags may need to operate at fairly long range and in the presence of considerable multipath and multiple-access interference. Towards that end, EV4 is developing a prototype SAW RFID wireless interrogation system for use in such environments called the Passive Adaptive RFID Sensor Equipment (PARSED) system. The system utilizes a digitally beam-formed planar receiving antenna array to extend range and provide direction-of-arrival information coupled with an approximate maximum-likelihood signal processing algorithm to provide near-optimal estimation of both range and temperature. The system is capable of forming a large number of beams within the field of view and resolving the information from several tags within each beam. The combination of both spatial and waveform discrimination provides the capability to track and monitor telemetry from a large number of objects appearing simultaneously within the field of view of the receiving array. In this paper, we will consider the application of the PARSEQ system to the problem of simultaneous detection, identification, localization, and temperature estimation for multiple objects. We will summarize the overall design of the PARSEQ system and present a detailed description of the design and performance of the signal detection and estimation algorithms incorporated in the system. The system is currently configured only to measure temperature (jointly with range and tag ID), but future versions will be revised to measure parameters other than temperature as SAW tags capable of interfacing with external sensors become available. It is anticipated that the estimation of arbitrary parameters measured using SAW-based sensors will be based on techniques very similar to the joint range and temperature estimation techniques described in this paper.
Barton, Richard J.↗