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Results for “onboard data analysis”

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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Near earth asteroid scout CubeSat science data retrieval optimization using onboard data analysis

We present a collection of techniques designed for mitigating limited pointing stability for target acquisition, onboard image calibration and decision making, in a low bandwidth environment. These technologies will fly on the Near Earth Asteroid Scout (NEA Scout) interplanetary CubeSat mission in 2019. To support verification and validation, the flight software implementations of these algorithms have been run on New Horizons, Rosetta and terrestrially acquired data sets. These experiments validate the capability to detect the target, through noise and target location uncertainty.

Basset, Christophe↗
NASA NTRS2016

Onboard Data Analysis for the Planetary Instrument for X-Ray Lithography

Imaging spectrometers are invaluable instruments for robotic science exploration, enabling quantitative maps of physical and chemical properties at high spatial resolution. This is particularly valuable in remote missions to other planetary bodies like Mars. The PIXL instrument on the Mars 2020 rover will deploy an arm-mounted X-Ray fluorescence spectrometer to map chemical composition at sub-millimeter scales. Its high resolution places dramatic new demands on instrument placement accuracy and measurement time. We address these challenges using novel on board data analysis strategies inspired by FRC science autonomy research.

Thompson, David R.↗
NASA NTRS2012

Autonomous Onboard Science Data Analysis for Comet Missions

Coming years will bring several comet rendezvous missions. The Rosetta spacecraft arrives at Comet 67P/Churyumov-Gerasimenko in 2014. Subsequent rendezvous might include a mission such as the proposed Comet Hopper with multiple surface landings, as well as Comet Nucleus Sample Return (CNSR) and Coma Rendezvous and Sample Return (CRSR). These encounters will begin to shed light on a population that, despite several previous flybys, remains mysterious and poorly understood. Scientists still have little direct knowledge of interactions between the nucleus and coma, their variation across different comets or their evolution over time. Activity may change on short timescales so it is challenging to characterize with scripted data acquisition. Here we investigate automatic onboard image analysis that could act faster than round-trip light time to capture unexpected outbursts and plume activity. We describe one edge-based method for detect comet nuclei and plumes, and test the approach on an existing catalog of comet images. Finally, we quantify benefits to specific measurement objectives by simulating a basic plume monitoring campaign.

comets↗
NASA NTRS2010

Using Multi-Core Systems for Rover Autonomy

Task Objectives are: (1) Develop and demonstrate key capabilities for rover long-range science operations using multi-core computing, (a) Adapt three rover technologies to execute on SOA multi-core processor (b) Illustrate performance improvements achieved (c) Demonstrate adapted capabilities with rover hardware, (2) Targeting three high-level autonomy technologies (a) Two for onboard data analysis (b) One for onboard command sequencing/planning, (3) Technologies identified as enabling for future missions, (4)Benefits will be measured along several metrics: (a) Execution time / Power requirements (b) Number of data products processed per unit time (c) Solution quality

multi-core↗
NASA NTRS1998

Onboard Science Data Analysis: Opportunities, Benefits, and Effects on Mission Design

Much of the initial focus for spacecraft autonomy has been on developing new software and systems concepts to automate engineering functions of the spacecraft: guidance, navigation and control, fault protection, and resources management. However, the ultimate objectives of NASA missions are science objectives, which implies that we need a new framework for perfoming science data evaluation and observation planning autonomously onboard spacecraft.

spacecraft autonomy observation planning science d↗
NASA NTRSDate not supplied

Using Coordinated, Multi-Agent Platforms for Dynamic Ocean Worlds Science

Planetary science missions have the opportunity to enhance science return through deployment of autonomous capabilities designed to dynamically respond to new information. Future outer solar system missions to ocean worlds in particular would benefit from this technology - intelligent science payloads (ISP) - because it would allow for a coordinated, near real-time response to ephemeral ‘events’ such as plumes, tectonism, surface implantation, volatile releases, thermal and magnetic anomalies, or radiation, as well as increasing the cadence and coverage of data collection. Prioritization and decision-making frameworks from ISP could be deployed at various scales - from analysis onboard a spacecraft with multiple instruments – to coordinated analyses among separate spacecraft in an e.g., distributed systems mission (DSM) composed of multiple SmallSats. Goddard’s Intelligent Science Payload team is developing an agile autonomous architecture for an icy ocean worlds DSM concept. Our goals are to coordinate data collection and onboard data analysis, and to make autonomous decisions for new data collection and analysis based on science priorities between multiple spacecraft with variable instrumentation and orbits. We use a range of data analysis tools to coordinate the DSM response, spanning from observations of data over a specified threshold to more computationally intensive machine learning algorithms (ML). ML algorithms here currently focus on determining the composition of an ocean world using mass spectrometry, and specifically methods for understanding ‘novelties’ and potential biosignatures. These algorithms could be used to quickly process and analyze onboard data that would be significantly delayed in downlink due to long communication delays for outer solar system missions in order to make dynamic science observations. Our ocean worlds case study ISP architecture is intended as an ‘agile’ and modular framework that could be used as a whole or as particular modules based on mission needs.

Distributed Systems↗
NASA NTRS2022

Science Autonomy for Ocean Worlds Astrobiology: A Perspective

Astrobiology missions to ocean worlds in our solar system must overcome both scientific and technological challenges due to extreme temperature and radiation conditions, long communication times, and limited bandwidth. While such tools could not replace ground-based analysis by science and engineering teams, machine learning algorithms could enhance the science return of these missions through development of autonomous science capabilities. Examples of science autonomy include onboard data analysis and subsequent instrument optimization, data prioritization (for transmission), and real-time decision-making based on data analysis. Similar advances could be made to develop streamlined data processing software for rapid ground-based analyses. Here we discuss several ways machine learning and autonomy could be used for astrobiology missions, including landing site selection, prioritization and targeting of samples, classification of “features” (e.g., proposed biosignatures) and novelties (uncharacterized, “new” features, which may be of most interest to agnostic astrobiological investigations), and data transmission.

ocean worlds↗
NASA NTRS1993

Research requirements for a real-time flight measurements and data analysis system for subsonic transport high-lift research

A multiphased research program to obtain detailed flow characteristics on a multielement high-lift flap system is being conducted on the Transport Systems Research Vehicle (B737-100 aircraft) at NASA Langley Research Center. Upcoming flight tests have required the development of a highly capable and flexible flight measurement and data analysis instrumentation system. This instrumentation system will be more comprehensive than any of the systems used on previous high-lift flight experiment at NASA Langley. The system will provide the researcher near-real-time information for decision making needed to modify a flight test in order to further examine unexpected flow conditions. This paper presents the research requirements and instrumentation design concept for an upcoming flight experiment for the subsonic transport high-lift research program. The flight experiment objectives, the measurement requirements, the data acquisition system, and the onboard data analysis and display capabilities are described.

Whitehead, Julia H.↗
NASA NTRS2008

Absorption and Attenuation Coefficients Using the WET Labs ac-s in the Mid-Atlantic Bight: Field Measurements and Data Analysis

Ocean color algorithms are based on the parameterization of apparent optical properties as a function of inherent optical properties. WET Labs underwater absorption and attenuation meters (ac-9 and ac-s) measure both the spectral beam attenuation [c (lambda)] and absorption coefficient [a (lambda)]. The ac-s reports in a continuous range of 390-750 nm with a band pass of 4 nm, totaling approximately 83 distinct wavelengths, while the ac-9 reports at 9 wavelengths. We performed the ac-s field measurements at nine stations in the Mid-Atlantic Bight from water calibrations to data analysis. Onboard the ship, the ac-s was calibrated daily using Milli Q-water. Corrections for the in situ temperature and salinity effects on optical properties of water were applied. Corrections for incomplete recovery of the scattered light in the ac-s absorption tube were performed. The fine scale of spectral and vertical distributions of c (lambda) and a (lambda) were described from the ac-s. The significant relationships between a (674) and that of spectrophotometric analysis and chlorophyll a concentration of discrete water samples were observed.

Ohi, Nobuaki↗
NASA NTRS2008

Supporting Increased Autonomy for a Mars Rover

This paper presents an architecture and a set of technology for performing autonomous science and commanding for a planetary rover. The MER rovers have outperformed all expectations by lasting over 1100 sols (or Martian days), which is an order of magnitude longer than their original mission goal. The longevity of these vehicles will have significant effects on future mission goals, such as objectives for the Mars Science Laboratory rover mission (scheduled to fly in 2009) and the Astrobiology Field Lab rover mission (scheduled to potentially fly in 2016). Common objectives for future rover missions to Mars include the handling of opportunistic science, long-range or multi-sol driving, and onboard fault diagnosis and recovery. To handle these goals, a number of new technologies have been developed and integrated as part of the CLARAty architecture. CLARAty is a unified and reusable robotic architecture that was designed to simplify the integration, testing and maturation of robotic technologies for future missions. This paper focuses on technology comprising the CLARAty Decision Layer, which was designed to support and validate high-level autonomy technologies, such as automated planning and scheduling and onboard data analysis.

autonomous science↗
NASA NTRS2009

Progressive Classification Using Support Vector Machines

An algorithm for progressive classification of data, analogous to progressive rendering of images, makes it possible to compromise between speed and accuracy. This algorithm uses support vector machines (SVMs) to classify data. An SVM is a machine learning algorithm that builds a mathematical model of the desired classification concept by identifying the critical data points, called support vectors. Coarse approximations to the concept require only a few support vectors, while precise, highly accurate models require far more support vectors. Once the model has been constructed, the SVM can be applied to new observations. The cost of classifying a new observation is proportional to the number of support vectors in the model. When computational resources are limited, an SVM of the appropriate complexity can be produced. However, if the constraints are not known when the model is constructed, or if they can change over time, a method for adaptively responding to the current resource constraints is required. This capability is particularly relevant for spacecraft (or any other real-time systems) that perform onboard data analysis. The new algorithm enables the fast, interactive application of an SVM classifier to a new set of data. The classification process achieved by this algorithm is characterized as progressive because a coarse approximation to the true classification is generated rapidly and thereafter iteratively refined. The algorithm uses two SVMs: (1) a fast, approximate one and (2) slow, highly accurate one. New data are initially classified by the fast SVM, producing a baseline approximate classification. For each classified data point, the algorithm calculates a confidence index that indicates the likelihood that it was classified correctly in the first pass. Next, the data points are sorted by their confidence indices and progressively reclassified by the slower, more accurate SVM, starting with the items most likely to be incorrectly classified. The user can halt this reclassification process at any point, thereby obtaining the best possible result for a given amount of computation time. Alternatively, the results can be displayed as they are generated, providing the user with real-time feedback about the current accuracy of classification.

Wagstaff, Kiri↗
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 NTRS2009

Simulating and Detecting Radiation-Induced Errors for Onboard Machine Learning

Spacecraft processors and memory are subjected to high radiation doses and therefore employ radiation-hardened components. However, these components are orders of magnitude more expensive than typical desktop components, and they lag years behind in terms of speed and size. We have integrated algorithm-based fault tolerance (ABFT) methods into onboard data analysis algorithms to detect radiation-induced errors, which ultimately may permit the use of spacecraft memory that need not be fully hardened, reducing cost and increasing capability at the same time. We have also developed a lightweight software radiation simulator, BITFLIPS, that permits evaluation of error detection strategies in a controlled fashion, including the specification of the radiation rate and selective exposure of individual data structures. Using BITFLIPS, we evaluated our error detection methods when using a support vector machine to analyze data collected by the Mars Odyssey spacecraft. We found ABFT error detection for matrix multiplication is very successful, while error detection for Gaussian kernel computation still has room for improvement.

data analysis↗