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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.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Optoelectronic Sensor System for Guidance in Docking

The Video Guidance Sensor (VGS) system is an optoelectronic sensor that provides automated guidance between two vehicles. In the original intended application, the two vehicles would be spacecraft docking together, but the basic principles of design and operation of the sensor are applicable to aircraft, robots, vehicles, or other objects that may be required to be aligned for docking, assembly, resupply, or precise separation. The system includes a sensor head containing a monochrome charge-coupled- device video camera and pulsed laser diodes mounted on the tracking vehicle, and passive reflective targets on the tracked vehicle. The lasers illuminate the targets, and the resulting video images of the targets are digitized. Then, from the positions of the digitized target images and known geometric relationships among the targets, the relative position and orientation of the vehicles are computed. As described thus far, the VGS system is based on the same principles as those of the system described in "Improved Video Sensor System for Guidance in Docking" (MFS-31150), NASA Tech Briefs, Vol. 21, No. 4 (April 1997), page 9a. However, the two systems differ in the details of design and operation. The VGS system is designed to operate with the target completely visible within a relative-azimuth range of +/-10.5deg and a relative-elevation range of +/-8deg. The VGS acquires and tracks the target within that field of view at any distance from 1.0 to 110 m and at any relative roll, pitch, and/or yaw angle within +/-10deg. The VGS produces sets of distance and relative-orientation data at a repetition rate of 5 Hz. The software of this system also accommodates the simultaneous operation of two sensors for redundancy

Howard, Richard T.↗
DOE OSTI2023

Sensor Fusion-Based Vehicle Detection and Tracking Using a Single Camera and Radar at a Traffic Intersection

Recent advancements in sensor technologies, in conjunction with signal processing and machine learning, have enabled real-time traffic control systems to adapt to varying traffic conditions. This paper introduces a new sensor fusion approach that combines data from a single camera and radar to achieve cost-effective and efficient vehicle detection and tracking. Initially, vehicles are independently detected and classified using the camera and radar. Then, the constant-velocity model within a Kalman filter is employed to predict vehicle locations, while the Hungarian algorithm is used to associate these predictions with sensor measurements. Finally, vehicle tracking is accomplished by merging kinematic information from predictions and measurements through the Kalman filter. A case study conducted at an intersection demonstrates the effectiveness of the proposed sensor fusion method for traffic detection and tracking, including performance comparisons with individual sensors.

47 OTHER INSTRUMENTATION↗
NASA NTRSDate not supplied

Space Launch System: SLS Real Time Rollout Monitoring for MPCV

The NASA Space Launch System (SLS) rocket, Orion spacecraft, and Mobile Launcher (ML) are transported to Pad 39B from the Vehicle Assembly Building on a large, tracked vehicle known as the Crawler Transporter (CT). The CT is a holdover from the Apollo and Space Shuttle programs but has been extensively upgraded to accommodate the SLS. The Dynamic Rollout Test (DRT) on March 17, 2022 marked the first full rollout of the SLS and ML. Analysis indicated that loads and vibrations would be well below design limits, but out of an abundance of caution it was decided to monitor the response in real time during the rollout to assure that loads remained within predicted limits. This first rollout was outfitted with extensive test instrumentation such as accelerometers, pressure sensors, and strain gauges. This instrumentation remained in place following the integrated vehicle modal test and was being used for additional Operational Modal Analysis (OMA) during the rollout in addition to the real-time monitoring. This presentation discusses the specific loads and accelerations monitored during the rollout, how and why those responses were chosen, and how the system was implemented. Finally, a post-rollout loads reconstruction is used to assess the process and the analysis tools used for the rollout analysis.

SLS↗
NASA NTRS2013

Cratos: The Evolution of a Robotic Vehicle

Cratos was originally designed to evaluate a small footprint, low-power, tracked vehicle for traversing a crater face and for scraping regolith from a crater basin on the lunar surface. These activities require a power-to-weight ratio such that the vehicle is able to pull its entire mass vertically off the ground with the power from one track. Further complicating matters, the available power-units that fit the vehicles approximate one cubic meter volume, restrict power consumption to 100 watts. Lastly, the vehicle is required to telemeter data from an array of sensors to a remote control station for further study. This unique mix of design constraints lead to the creation of a versatile mobile sensor platform, described in this paper, capable of performing a myriad of functions beyond the scope of its original purpose.

Greer, Lawrence C.↗
NASA NTRS1991

Office of Spaceflight Standard Spaceborne Global Positioning System (GPS) user equipment project

The Global Positioning System (GPS) provides the following: (1) position and velocity determination to support vehicle GN&C, precise orbit determination, and payload pointing; (2) time reference to support onboard timing systems and data time tagging; (3) relative position and velocity determination to support cooperative vehicle tracking; and (4) attitude determination to support vehicle attitude control and payload pointing.

Saunders, Penny E.↗
NASA NTRS1989

Design of an autonomous teleoperated cargo transporting vehicle for lunar base operations

At the turn of the century NASA plans to begin construction of a lunar base. The base will likely consist of developed areas (i.e., habitation, laboratory, landing and launching sites, power plant) separated from each other due to safety considerations. The Self-Repositioning Track Vehicle (SRTV) was designed to transport cargo between these base facilities. The SRTV operates by using two robotic arms to raise and position segments of track upon which the vehicle travels. The SRTV utilizes the semiautonomous mobility (SAM) method of teleoperation; actuator-controlled interlocking track sections; two robotic arms each with five degrees of freedom; and these materials: titanium for structural members and aluminum for shell members, with the possible use of light-weight, high-strength composites.

Holt, James↗
NASA NTRS1978

State-of-the-art assessment of electric and hybrid vehicles

Data are presented that were obtained from the electric and hybrid vehicles tested, information collected from users of electric vehicles, and data and information on electric and hybrid vehicles obtained on a worldwide basis from manufacturers and available literature. The data given include: (1) information and data base (electric and hybrid vehicle systems descriptions, sources of vehicle data and information, and sources of component data); (2) electric vehicles (theoretical background, electric vehicle track tests, user experience, literature data, and summary of electric vehicle status); (3) electric vehicle components (tires, differentials, transmissions, traction motors, controllers, batteries, battery chargers, and component summary); and (4) hybrid vehicles (types of hybrid vehicles, operating modes, hybrid vehicles components, and hybrid vehicles performance characteristics).

Source record↗
NASA NTRS1984

Technology for Smoother Transit

Langley Research Center has developed a portable, self contained ride quality meter for accessing ride quality during vehicle operation. System is applicable to air, sea, road, or track vehicles for estimating passenger ride comfort in the presence of complex vibrations and interior noise. Meter has three components: a package of three vibration sensors, a microphone, and a computer. Installed on the floor of the vehicle being tested, sensors measure vibrations in five different axes, microphone measures noise level, and computer processes input from both and gives user a printed readout including a number of options for assessing ride quality.

Source record↗
NASA NTRS1966

Surveyor mission operations system

Functional description of launch vehicle, tracking and data acquisition, spacecraft, and mission operations of Surveyor mission

LAUNCH VEHICLE↗
NASA NTRS2005

A Framework for Integration of IVHM Technologies for Intelligent Integration for Vehicle Management

As a part of the overall goal of developing Integrated Vehicle Health Management (IVHM) systems for aerospace vehicles, the NASA Faculty Fellowship Program (NFFP) at Marshall Space Flight Center has performed a pilot study on IVHM principals which integrates researched IVHM technologies in support of Integrated Intelligent Vehicle Management (IIVM). IVHM is the process of assessing, preserving, and restoring system functionality across flight and ground systems (NASA NGLT 2004). The framework presented in this paper integrates advanced computational techniques with sensor and communication technologies for spacecraft that can generate responses through detection, diagnosis, reasoning, and adapt to system faults in support of IIVM. These real-time responses allow the IIVM to modify the effected vehicle subsystem(s) prior to a catastrophic event. Furthermore, the objective of this pilot program is to develop and integrate technologies which can provide a continuous, intelligent, and adaptive health state of a vehicle and use this information to improve safety and reduce costs of operations. Recent investments in avionics, health management, and controls have been directed towards IIVM. As this concept has matured, it has become clear the IIVM requires the same sensors and processing capabilities as the real-time avionics functions to support diagnosis of subsystem problems. New sensors have been proposed, in addition, to augment the avionics sensors to support better system monitoring and diagnostics. As the designs have been considered, a synergy has been realized where the real-time avionics can utilize sensors proposed for diagnostics and prognostics to make better real-time decisions in response to detected failures. IIVM provides for a single system allowing modularity of functions and hardware across the vehicle. The framework that supports IIVM consists of 11 major on-board functions necessary to fully manage a space vehicle maintaining crew safety and mission objectives: Guidance and Navigation; Communications and Tracking; Vehicle Monitoring; Information Transport and Integration; Vehicle Diagnostics; Vehicle Prognostics; Vehicle mission Planning; Automated Repair and Replacement; Vehicle Control; Human Computer Interface; and Onboard Verification and Validation. Furthermore, the presented framework provides complete vehicle management which not only allows for increased crew safety and mission success through new intelligence capabilities, but also yields a mechanism for more efficient vehicle operations. The representative IVHM technologies for IIVH includes: 1) robust controllers for use in re-usable launch vehicles, 2) scaleable/flexible computer platform using heterogeneous communication, 3) coupled electromagnetic oscillators for enhanced communications, 4) Linux-based real-time systems, 5) genetic algorithms, 6) Bayesian Networks, 7) evolutionary algorithms, 8) dynamic systems control modeling, and 9) advanced sensing capabilities. This paper presents IVHM technologies developed under NASA's NFFP pilot project. The integration of these IVHM technologies forms the framework for IIVM.

Paris, Deidre E.↗
NASA NTRS2005

Intelligent Vehicle Health Management

As a part of the overall goal of developing Integrated Vehicle Health Management systems for aerospace vehicles, the NASA Faculty Fellowship Program (NFFP) at Marshall Space Flight Center has performed a pilot study on IVHM principals which integrates researched IVHM technologies in support of Integrated Intelligent Vehicle Management (IIVM). IVHM is the process of assessing, preserving, and restoring system functionality across flight and ground systems (NASA NGLT 2004). The framework presented in this paper integrates advanced computational techniques with sensor and communication technologies for spacecraft that can generate responses through detection, diagnosis, reasoning, and adapt to system faults in support of INM. These real-time responses allow the IIVM to modify the affected vehicle subsystem(s) prior to a catastrophic event. Furthermore, the objective of this pilot program is to develop and integrate technologies which can provide a continuous, intelligent, and adaptive health state of a vehicle and use this information to improve safety and reduce costs of operations. Recent investments in avionics, health management, and controls have been directed towards IIVM. As this concept has matured, it has become clear the INM requires the same sensors and processing capabilities as the real-time avionics functions to support diagnosis of subsystem problems. New sensors have been proposed, in addition, to augment the avionics sensors to support better system monitoring and diagnostics. As the designs have been considered, a synergy has been realized where the real-time avionics can utilize sensors proposed for diagnostics and prognostics to make better real-time decisions in response to detected failures. IIVM provides for a single system allowing modularity of functions and hardware across the vehicle. The framework that supports IIVM consists of 11 major on-board functions necessary to fully manage a space vehicle maintaining crew safety and mission objectives: Guidance and Navigation; Communications and Tracking; Vehicle Monitoring; Information Transport and Integration; Vehicle Diagnostics; Vehicle Prognostics; Vehicle mission Planning; Automated Repair and Replacement; Vehicle Control; Human Computer Interface; and Onboard Verification and Validation. Furthermore, the presented framework provides complete vehicle management which not only allows for increased crew safety and mission success through new intelligence capabilities, but also yields a mechanism for more efficient vehicle operations. The representative IVHM technologies for computer platform using heterogeneous communication, 3) coupled electromagnetic oscillators for enhanced communications, 4) Linux-based real-time systems, 5) genetic algorithms, 6) Bayesian Networks, 7) evolutionary algorithms, 8) dynamic systems control modeling, and 9) advanced sensing capabilities. This paper presents IVHM technologies developed under NASA's NFFP pilot project and the integration of these technologies forms the framework for IIVM.

Paris, Deidre E.↗
NASA NTRS2007

Modular Filter and Source-Management Upgrade of RADAC

In an upgrade of the Range Data Acquisition Computer (RADAC) software, a modular software object library was developed to implement required functionality for filtering of flight-vehicle-tracking data and management of tracking-data sources. (The RADAC software is used to process flight-vehicle metric data for realtime display in the Wallops Flight Facility Range Control Center and Mobile Control Center.)

Lanzi, R. James↗
DOE OSTI2022

Profile Images and Annotations for Vehicle Re-identification Algorithms (PRIMAVERA)

This dataset contains 636,246 profile images of vehicles representing 13,963 unique vehicles. The data was collected by a set of roadside sensors over the course of three years. Each time a vehicle passed by one of the sensors, a series of images was collected. The images were processed to detect and localize each vehicle, and a license plate reader collocated with the sensor was used to provide a unique ID for the vehicle. Actual license plate numbers have been obfuscated by replacing with an arbitrary numerical ID for each vehicle. After localizing the vehicle in each image, the original RGB image was rotated, scaled, and shifted to produce a new RGB image of size 234x234 pixels such that the outermost two wheels are located at predetermined pixel locations in the image. In this way, all vehicle images are aligned to one another. This registration process occasionally results in a portion of certain vehicles being cutoff at the edges of the image. The dataset has been partitioned into two sets called training and validation. The two partitions no common vehicles, i.e., a vehicle present in one partition is guaranteed not to be present in the other. In this way, an algorithm can be validated against a set of new vehicles that were not seen during the training process. The training set contains 543,926 images from 64,440 vehicle passes representing 11,918 unique vehicles, while the validation set contains 92,320 images from 10,991 vehicle passes representing 2,045 unique vehicles. Vehicle images are organized by directories corresponding to unique vehicles. The file naming scheme is as follows: veh_{vehID}_tr_{passID}_{frameID}_{elevation}_{timeofday}.jpg where {vehID} is the vehicle ID (unique across the entire dataset), {passID} is an identifier for each tracked vehicle pass (unique across the entire dataset), {frameID} is the index of the frame within the given vehicle pass starting at 0, {elevation} is a two-letter string indicating whether the sensor was elevated (el) or at ground-level (gl), and {timeofday} is a two-letter string indicating whether the image was captured during daytime (dt) or nighttime (nt).

image↗
NASA NTRS2005

IVHM Framework for Intelligent Integration for Vehicle Health Management

Integrated Vehicle Health Management (IVHM) systems for aerospace vehicles, is the process of assessing, preserving, and restoring system functionality across flight and techniques with sensor and communication technologies for spacecraft that can generate responses through detection, diagnosis, reasoning, and adapt to system faults in support of Integrated Intelligent Vehicle Management (IIVM). These real-time responses allow the IIVM to modify the affected vehicle subsystem(s) prior to a catastrophic event. Furthermore, this framework integrates technologies which can provide a continuous, intelligent, and adaptive health state of a vehicle and use this information to improve safety and reduce costs of operations. Recent investments in avionics, health management, and controls have been directed towards IIVM. As this concept has matured, it has become clear that IIVM requires the same sensors and processing capabilities as the real-time avionics functions to support diagnosis of subsystem problems. New sensors have been proposed, in addition to augment the avionics sensors to support better system monitoring and diagnostics. As the designs have been considered, a synergy has been realized where the real-time avionics can utilize sensors proposed for diagnostics and prognostics to make better real-time decisions in response to detected failures. IIVM provides for a single system allowing modularity of functions and hardware across the vehicle. The framework that supports IIVM consists of 11 major on-board functions necessary to fully manage a space vehicle maintaining crew safety and mission objectives. These systems include the following: Guidance and Navigation; Communications and Tracking; Vehicle Monitoring; Information Transport and Integration; Vehicle Diagnostics; Vehicle Prognostics; Vehicle Mission Planning, Automated Repair and Replacement; Vehicle Control; Human Computer Interface; and Onboard Verification and Validation. Furthermore, the presented framework provides complete vehicle management which not only allows for increased crew safety and mission success through new intelligence capabilities, but also yields a mechanism for more efficient vehicle operations.

Paris, Deidre↗