Search NASA⌕ Search

SEARCH · Search NASA

Results for “onboard”

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.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Onboard autonomy on the Three Corner Sat mission

In 2003, the student-built three satellite constellation Three Corner Sat (3CS) Mission will demonstrate onboard autonomy including: science data validation and prioritization, mission re-planning, and robust execution. Future observations will be planned onboard based on the quality of aquired science, available memory and power, and anticipated downlinks. These capabilities will allow 3CS to aquire additional science data if resources are available and to return only the highest quality science data.

Planning scheduling autonomy↗
NASA NTRS2001

Onboard autonomy on the Three Corner Sat Mission

Three Corner Sat (3CS) is a mission of three university nanosatellites scheduled for launch on September 2002. The 3CS misison will utilize significan onboard autonomy to perform onboard science data validation and replanning.

scheduling planning resources model based systems↗
NASA NTRS2004

DEMO: the Autonomous Sciencecraft Experiment onboard the EO-1 spacecraft

The Autonomous Sciencececraft Experiment (ASE), currently flying onboard the Earth Observing-1 (EO-1) spacecraft, integrates several autnomoy software technologies enabling autnomous science analysis and mission planning. The experiment demonstrates the potential for future space missions to use onboard decision-making to respond autonomously to capture short-lived science phenomena. The AAAI software demonstration will consist of two sections: a real-time display of an ASE-commanded ground contact from the EO-1 spacecraft, and a simulation of the full ASE autonomous science-response scenario.

EO-1 spacecraft↗
NASA NTRS2004

The autonomous sciencecraft experiment onboard the EO-1 spacecraft

The Autonomous Sciencecraft Experiment (ASE), currently flying onboard the Earth Observing-1 (EO-I) spacecraft, integrates several autonomy software technologies enabling autonomous science analysis and mission planning. The experiment demonstrates the potential for future space missions to use onboard decision-making to respond autonomously to capture short-lived science phenomena. The AAAI software demonstration will consist of two sections: a real-time display of an ASE-commanded ground contact from the EO-1 spacecraft, and a simulation of the full ASE autonomous science-response scenario.

Earth Observatory (EO)↗
NASA NTRS2003

Standardized Spacecraft Onboard Interfaces

The Consultative Committee for Space Data Systems (CCSDS), an international organization of national space agencies, is branching out to provide new standards to enhanced reuse of onboard spacecraft equipment and software. These Spacecraft Onboard Interface (SOIF) standards will be, in part, based on the well-known Internet protocols. This paper will provide a description of the SOIF work by describing three orthogonal views: the Services View that describes data communications services, the Interoperability view shows how to exchange data and messages between different spacecraft elements, and the Protocol view, that describes the SOIF protocols and services. We will also provide a description of the present state of the services that will be provided to SOIF users, and are the basis of the utility of these standards.

space interfaces↗
NASA NTRS2006

Onboard Classifiers for Science Event Detection on a Remote Sensing Spacecraft

Typically, data collected by a spacecraft is downlinked to Earth and pre-processed before any analysis is performed. We have developed classifiers that can be used onboard a spacecraft to identify high priority data for downlink to Earth, providing a method for maximizing the use of a potentially bandwidth limited downlink channel. Onboard analysis can also enable rapid reaction to dynamic events, such as flooding, volcanic eruptions or sea ice break-up. Four classifiers were developed to identify cryosphere events using hyperspectral images. These classifiers include a manually constructed classifier, a Support Vector Machine (SVM), a Decision Tree and a classifier derived by searching over combinations of thresholded band ratios. Each of the classifiers was designed to run in the computationally constrained operating environment of the spacecraft. A set of scenes was hand-labeled to provide training and testing data. Performance results on the test data indicate that the SVM and manual classifiers outperformed the Decision Tree and band-ratio classifiers with the SVM yielding slightly better classifications than the manual classifier.

classification↗
NASA NTRS2007

Improved Path Planning Onboard the Mars Exploration Rovers

A revised version of the AutoNav (autonomous navigation with hazard avoidance) software running onboard each Mars Exploration Rover (MER) affords better obstacle avoidance than does the previous version. Both versions include GESTALT (Grid-based Estimation of Surface Traversability Applied to Local Terrain), a navigation program that generates local-terrain models from stereoscopic image pairs captured by onboard rover cameras; uses this information to evaluate candidate arcs that extend across the terrain from the current rover location; ranks the arcs with respect to hazard avoidance, minimization of steering time, and the direction towards the goal; and combines the rankings in a weighted vote to select an arc, along which the rover is then driven. GESTALT works well in navigating around small isolated obstacles, but tends to fail when the goal is on the other side of a large obstacle or multiple closely spaced small obstacles. When that occurs, the goal seeking votes and hazard avoidance votes conflict severely. The hazard avoidance votes will not allow the rover to drive through the unsafe area, and the waypoint votes will not allow enough deviation from the straight-line path for the rover to get around the hazard. The rover becomes stuck and is unable to reach the goal. The revised version of AutoNav utilizes a global path-planning program, Field D*, to evaluate the cost of traveling from the end of each GESTALT arc to the goal. In the voting process, Field D* arc votes supplant GESTALT goal-seeking arc votes. Hazard avoidance, steering bias, and Field D* votes are merged and the rover is driven a preset distance along the arc with the highest vote. Then new images are acquired and the process as described is repeated until the goal is reached. This new technology allows the rovers to autonomously navigate around much more complex obstacle arrangements than was previously possible. In addition, this improved autonomy enables longer traverses per Sol (a day on Mars), and can make planning drives easier for operators on Earth.

Stentz, Anthony↗
NASA NTRS2011

Analysis of Noise Exposure Measurements Made Onboard the International Space Station

The International Space Station (ISS) is a unique workplace environment for U.S. astronauts and Russian cosmonauts to conduct research and live for a period of six months or more. Noise has been an enduring environmental physical hazard that has been a challenge for the U.S. space program since before the Apollo era. Noise exposure in ISS poses significant risks to the crewmembers, such as; hearing loss (temporary or permanent), possible disruptions of crew sleep, interference with speech intelligibility and communication, possible interference with crew task performance, and possible reduction in alarm audibility. Acoustic measurements are made aboard ISS and compared to requirements in order to assess the acoustic environment to which the crewmembers are exposed. The purpose of this paper is to describe in detail the noise exposure monitoring program as well as an assessment of the acoustic dosimeter data collected to date. The hardware currently being used for monitoring the noise exposure onboard ISS will be discussed. Acoustic data onboard ISS has been collected since the beginning of ISS (Increment 1, November 2000). Noise exposure data analysis will include acoustic dosimetry logged data from crew-worn during work and sleep periods and also fixed-location measurements from Increment 1 to present day. Noise exposure levels (8-, 16- and 24-hr), LEQ, will also be provided and discussed in this paper. Discussions related to hearing protection will also be included. Future directions and recommendations for the noise exposure monitoring program will be highlighted. This acoustic data is used to ensure a safe and healthy working and living environment for the crewmembers aboard the ISS.

Limardo, Jose G.↗
NASA NTRS2004

Innovative Imagery System for Enhanced Habitability Onboard ISS: Desired Features and Possible Hardware Applications

With the advent of the ISS and the experience of Russian, European, and US crewmembers on Mir, the importance of the psychological element in long duration missions is increasingly recognized. An integrated imagery system or Magic Window System could enhance the habitability, performance, and productivity for long term stays in space. Because this is type of system is a new concept for space, functional and technical requirements need to be determined. As part of a three-year project, the functional and technical requirements for an Imagery System onboard the International Space Station (ISS) have been explored. Valuable information was gathered from a survey completed by participants that had been in analog environments (remote/isolated) such as Antarctica, Aquarius, ISS crewmember debriefs, and crew support meetings to identify key functions desired for an integrated Magic Window System. Exercise and medical care activities were identified as areas that could benefit from such a system. It was determined that for exercise, it was worth exploring the concept of displaying a dynamic screen that changes as the crewmember's speed changes while showing physiological measures in a combined display. In terms of enhancing the interfaces for medical care activities, the Magic Window System could show video clips along side procedures for just-in-time training scenarios through a heads-up display. In addition, the portability, usability, and reliability were stressed as important considerations for an integrated system of technologies or Magic Window System. In addition, a review of state-of-the-art screens and other existing technologies such as tablet PCs and Personal Digital Assistants (PDAs) was conducted and contributed to defining technical requirements and feasibility of systems. Some heuristic evaluations of large displays and PDAs were conducted. Finally, feasibility for implementation onboard ISS has been considered. Currently, specific headset units are undergoing usability testing. The outcome of these activities will be valuable to determine the best candidates for an integrated system that could accommodate different needs depending on task.

Whitmore, Mihriban↗
NASA NTRS2010

Onboard Systems Record Unique Videos of Space Missions

Ecliptic Enterprises Corporation, headquartered in Pasadena, California, provided onboard video systems for rocket and space shuttle launches before it was tasked by Ames Research Center to craft the Data Handling Unit that would control sensor instruments onboard the Lunar Crater Observation and Sensing Satellite (LCROSS) spacecraft. The technological capabilities the company acquired on this project, as well as those gained developing a high-speed video system for monitoring the parachute deployments for the Orion Pad Abort Test Program at Dryden Flight Research Center, have enabled the company to offer high-speed and high-definition video for geosynchronous satellites and commercial space missions, providing remarkable footage that both informs engineers and inspires the imagination of the general public.

Source record↗
NASA NTRS2011

SpaceCube 2.0: An Advanced Hybrid Onboard Data Processor

The SpaceCube 2.0 is a compact, high performance, low-power onboard processing system that takes advantage of cutting-edge hybrid (CPU/FPGA/DSP) processing elements. The SpaceCube 2.0 design concept includes two commercial Virtex-5 field-programmable gate array (FPGA) parts protected by gradiation hardened by software" technology, and possesses exceptional size, weight, and power characteristics [5x5x7 in., 3.5 lb (approximately equal to 12.7 x 12.7 x 17.8 cm, 1.6 kg) 5-25 W, depending on the application fs required clock rate]. The two Virtex-5 FPGA parts are implemented in a unique back-toback configuration to maximize data transfer and computing performance. Draft computing power specifications for the SpaceCube 2.0 unit include four PowerPC 440s (1100 DMIPS each), 500+ DSP48Es (2x580 GMACS), 100+ LVDS high-speed serial I/Os (1.25 Gbps each), and 2x190 GFLOPS single-precision (65 GFLOPS double-precision) floating point performance. The SpaceCube 2.0 includes PROM memory for CPU boot, health and safety, and basic command and telemetry functionality; RAM memory for program execution; and FLASH/EEPROM memory to store algorithms and application code for the CPU, FPGA, and DSP processing elements. Program execution can be reconfigured in real time and algorithms can be updated, modified, and/or replaced at any point during the mission. Gigabit Ethernet, Spacewire, SATA and highspeed LVDS serial/parallel I/O channels are available for instrument/sensor data ingest, and mission-unique instrument interfaces can be accommodated using a compact PCI (cPCI) expansion card interface. The SpaceCube 2.0 can be utilized in NASA Earth Science, Helio/Astrophysics and Exploration missions, and Department of Defense satellites for onboard data processing. It can also be used in commercial communication and mapping satellites.

Lin, Michael↗
NASA NTRS2012

Hyperspectral Feature Detection Onboard the Earth Observing One Spacecraft using Superpixel Segmentation and Endmember Extraction

We present a demonstration of onboard hyperspectral image processing with the potential to reduce mission downlink requirements. The system detects spectral endmembers and then uses them to map units of surface material. This summarizes the content of the scene, reveals spectral anomalies warranting fast response, and reduces data volume by two orders of magnitude. We have integrated this system into the Autonomous Science craft Experiment for operational use onboard the Earth Observing One (EO-1) Spacecraft. The system does not require prior knowledge about spectra of interest. We report on a series of trial overflights in which identical spacecraft commands are effective for autonomous spectral discovery and mapping for varied target features, scenes and imaging conditions.

Superpixel Endmember Detection↗
NASA NTRS2013

An Onboard ISS Virtual Reality Trainer

Prior to the retirement of the Space Shuttle, many exterior repairs on the International Space Station (ISS) were carried out by shuttle astronauts, trained on the ground and flown to the station to perform these repairs. After the retirement of the shuttle, this is no longer an available option. As such, the need for the ISS crew members to review scenarios while on flight, either for tasks they already trained or for contingency operations has become a very critical subject. In many situations, the time between the last session of Neutral Buoyancy Laboratory (NBL) training and an Extravehicular Activity (EVA) task might be 6 to 8 months. In order to help with training for contingency repairs and to maintain EVA proficiency while on flight, the Johnson Space Center Virtual Reality Lab (VRLab) designed an onboard immersive ISS Virtual Reality Trainer (VRT), incorporating a unique optical system and making use of the already successful Dynamic Onboard Ubiquitous Graphical (DOUG) graphics software, to assist crew members with current procedures and contingency EVAs while on flight. The VRT provides an immersive environment similar to the one experienced at the VRLab crew training facility at NASA Johnson Space Center. EVA tasks are critical for a mission since as time passes the crew members may lose proficiency on previously trained tasks. In addition, there is an increased need for unplanned contingency repairs to fix problems arising as the ISS ages. The need to train and re-train crew members for EVAs and contingency scenarios is crucial and extremely demanding. ISS crew members are now asked to perform EVA tasks for which they have not been trained and potentially have never seen before.

Miralles, Evelyn↗
NASA NTRS2012

Two Years Onboard the MER Opportunity Rover

The Autonomous Exploration for Gathering Increased Science (AEGIS) system provides automated data collection for planetary rovers. AEGIS is currently being used onboard the Mars Exploration Rover (MER) mission's Opportunity to provide autonomous targeting of the MER Panoramic camera. Prior to AEGIS, targeted data was collected in a manual fashion where targets were manually identified in images transmitted to Earth and the rover had to remain in the same location for one to several communication cycles. AEGIS enables targeted data to be rapidly acquired with no delays for ground communication. Targets are selected by AEGIS through the use of onboard data analysis techniques that are guided by scientist-specified objectives. This paper provides an overview of the how AEGIS has been used on the Opportunity rover, focusing on usage that occurred during a 21 kilometer historic trek to the Mars Endeavour crater.

Mars Exploration Rover (MER)↗
NASA NTRS2013

Memory-Efficient Onboard Rock Segmentation

Rockster-MER is an autonomous perception capability that was uploaded to the Mars Exploration Rover Opportunity in December 2009. This software provides the vision front end for a larger software system known as AEGIS (Autonomous Exploration for Gathering Increased Science), which was recently named 2011 NASA Software of the Year. As the first step in AEGIS, Rockster-MER analyzes an image captured by the rover, and detects and automatically identifies the boundary contours of rocks and regions of outcrop present in the scene. This initial segmentation step reduces the data volume from millions of pixels into hundreds (or fewer) of rock contours. Subsequent stages of AEGIS then prioritize the best rocks according to scientist- defined preferences and take high-resolution, follow-up observations. Rockster-MER has performed robustly from the outset on the Mars surface under challenging conditions. Rockster-MER is a specially adapted, embedded version of the original Rockster algorithm ("Rock Segmentation Through Edge Regrouping," (NPO- 44417) Software Tech Briefs, September 2008, p. 25). Although the new version performs the same basic task as the original code, the software has been (1) significantly upgraded to overcome the severe onboard re source limitations (CPU, memory, power, time) and (2) "bulletproofed" through code reviews and extensive testing and profiling to avoid the occurrence of faults. Because of the limited computational power of the RAD6000 flight processor on Opportunity (roughly two orders of magnitude slower than a modern workstation), the algorithm was heavily tuned to improve its speed. Several functional elements of the original algorithm were removed as a result of an extensive cost/benefit analysis conducted on a large set of archived rover images. The algorithm was also required to operate below a stringent 4MB high-water memory ceiling; hence, numerous tricks and strategies were introduced to reduce the memory footprint. Local filtering operations were re-coded to operate on horizontal data stripes across the image. Data types were reduced to smaller sizes where possible. Binary- valued intermediate results were squeezed into a more compact, one-bit-per-pixel representation through bit packing and bit manipulation macros. An estimated 16-fold reduction in memory footprint relative to the original Rockster algorithm was achieved. The resulting memory footprint is less than four times the base image size. Also, memory allocation calls were modified to draw from a static pool and consolidated to reduce memory management overhead and fragmentation. Rockster-MER has now been run onboard Opportunity numerous times as part of AEGIS with exceptional performance. Sample results are available on the AEGIS website at http://aegis.jpl.nasa.gov.

Burl, Michael C.↗
NASA NTRS2014

Real-Time Onboard Global Nonlinear Aerodynamic Modeling from Flight Data

Flight test and modeling techniques were developed to accurately identify global nonlinear aerodynamic models onboard an aircraft. The techniques were developed and demonstrated during piloted flight testing of an Aermacchi MB-326M Impala jet aircraft. Advanced piloting techniques and nonlinear modeling techniques based on fuzzy logic and multivariate orthogonal function methods were implemented with efficient onboard calculations and flight operations to achieve real-time maneuver monitoring and analysis, and near-real-time global nonlinear aerodynamic modeling and prediction validation testing in flight. Results demonstrated that global nonlinear aerodynamic models for a large portion of the flight envelope were identified rapidly and accurately using piloted flight test maneuvers during a single flight, with the final identified and validated models available before the aircraft landed.

Brandon, Jay M.↗
NASA NTRS2011

Onboard Radar Processing Development for Rapid Response Applications

We are developing onboard processor (OBP) technology to streamline data acquisition on-demand and explore the potential of the L-band SAR instrument onboard the proposed DESDynI mission and UAVSAR for rapid response applications. The technology would enable the observation and use of surface change data over rapidly evolving natural hazards, both as an aid to scientific understanding and to provide timely data to agencies responsible for the management and mitigation of natural disasters. We are adapting complex science algorithms for surface water extent to detect flooding, snow/water/ice classification to assist in transportation/ shipping forecasts, and repeat-pass change detection to detect disturbances. We are near completion of the development of a custom FPGA board to meet the specific memory and processing needs of L-band SAR processor algorithms and high speed interfaces to reformat and route raw radar data to/from the FPGA processor board. We have also developed a high fidelity Matlab model of the SAR processor that is modularized and parameterized for ease to prototype various SAR processor algorithms targeted for the FPGA. We will be testing the OBP and rapid response algorithms with UAVSAR data to determine the fidelity of the products.

dual polarized data↗
NASA NTRS2013

Using Onboard Telemetry for MAVEN Orbit Determination

Determination of the spacecraft state has been traditional done using radiometric tracking data before and after the atmosphere drag pass. This paper describes our approach and results to include onboard telemetry measurements in addition to radiometric observables to refine the reconstructed trajectory estimate for the Mars Atmosphere and Volatile Evolution Mission (MAVEN). Uncertainties in the Mars atmosphere models, combined with non-continuous tracking degrade navigation accuracy, making MAVEN a key candidate for using onboard telemetry data to help complement its orbit determination process.

orbit determination↗