Results for “autonomous experiment” — Page 39

NASA NTRS1994

(abstract) Sciencecraft

The technological capabilities are now at hand to design an integrated system that combines science instruments, spacecraft, and propulsion elements into a single system. The authors have called this a sciencecraft since it is intended to provide automatic scientific observations of planetary and astrophysical objects. Integration of function allows lower mass and cost and supports a short development cycle. A specific science mission is described in this paper, a flyby of Neptune, Triton, and an object in the Kuiper belt. The SCIENCECRAFT system is described. It has electric propulsion and is capable of measuring the surface constituents and morphology of objects visited and characterizing their atmospheres both in emission and adsorption (against the Sun). Miniature fields and particles experiments are incorporated that will provide interplanetary information together with details of the magnetic and electric attributes of each object. The Sciencecraft is Delta launched and has a flight time to the Kuiper belt of 7 years. The design is such that the craft functions in a largely autonomous mode to provide low cost mission operations.

planetary astrophysics science instruments spacecr↗

NASA NTRS2007

Development of an Airborne Molecular Direct Detection Doppler Lidar for Tropospheric Wind Profiling

Global measurement of tropospheric winds is a key measurement for understanding atmospheric dynamics and improving numerical weather prediction. Global wind profiles remain a high priority for the operational weather community and also for a variety of research applications including studies of the global hydrologic cycle and transport studies of aerosols and trace species. In addition to space based winds, high altitude airborne Doppler lidar systems flown on research aircraft, UAV's or other advanced sub-orbital platforms would be of great scientific benefit for studying mesoscale dynamics and storm systems such as hurricanes. The Tropospheric Wind Lidar Technology Experiment (TWiLiTE) is a three year program to advance the technology readiness level of the key technologies and subsystems of a molecular direct detection wind lidar system by validating them, at the system level, in an integrated airborne lidar system. The TWiLiTE Doppler lidar system is designed for autonomous operation on the WB57, a high altitude aircraft operated by NASA Johnson. The WE357 is capable of flying well above the midlatitude tropopause so the downward looking lidar will measure complete profiles of the horizontal wind field through the lower stratosphere and the entire troposphere. The completed system will have the capability to profile winds in clear air from the aircraft altitude of 18 km to the surface with 250 m vertical resolution and < 3 mis velocity accuracy. Progress in technology development and status of the instrument design will be presented.

Gentry, Bruce↗

NASA NTRS2017

Machine Learning for Slow but Steady Interplanetary Construction

For prolonged manned missions to destinations such as the moon and Mars, there is a need for significant infrastructure construction ahead of time, such as habitats and landing pads. Unfortunately we have little experience in remote construction and using conventional methods is likely to be expensive, cumbersome and unreliable. Fortunately these challenges may be overcome by taking advantage of the long lead time for such missions and using teams of small and slow construction robots. We propose using teams of simple autonomous robots for this purpose that would perform continuous construction over a period of many years or even decades. While individual robot reliability will be low over such long time frames, system reliability will be maintained by using machine learning over simulations to achieve coordination and reconfigurations in the event of lost robots.

Tensegrity robots↗

NASA NTRS2019

Predicted Performance of an X-Ray Navigation System for Future Deep Space and Lunar Missions

In November 2017, the NASA Goddard Space Flight Center (GSFC) Station Explorer for X-ray Timing and Navigation Technology (SEXTANT) experiment successfully demonstrated the feasibility of X-ray Pulsar Navigation (XNAV) as part of the Neutron Star Interior Composition Explorer (NICER) mission, which is an X-ray Astrophysics Mission of Opportunity currently operating onboard the International Space Station (ISS). XNAV provides a GPS-like absolute autonomous navigation and timing capability available anywhere in the Solar System and beyond. While the most significant benefits of XNAV are expected to come in support of very deep-space missions, the absolute autonomous navigation and timing capability also has utility for inner Solar System missions where increased autonomy or backup navigation and timing services are required, e.g., address loss of communication scenarios.The NASA commitment to develop a Gateway to support exploration of the Moon and eventually Mars, as well as current and future robotic missions such as James Webb Space Telescope (JWST), New Horizons, and much more, certainly will tax the existing ground based infrastructure in terms of availability. There- fore, an extended look at the feasibility and potential performance of XNAV for comparable missions is warranted. In this paper, we briefly review the XNAV concept and present case studies of its utility and performance for a Gateway orbit, Sun-Earth libration orbit, and a deep space transit trajectory.

Getchius, Joel↗

DOE OSTI2026

Leveraging large language models to address data scarcity in machine learning for graphene synthesis

Machine learning in experimental materials science faces significant challenges due to the scarcity of data, which are costly and time-consuming to generate, particularly when relying on in-house experiments. Literature data mining offers a potential solution but introduces issues like mixed data quality, inconsistent formats, and non-uniform reporting of synthesis parameters, resulting in partially missing and heterogeneous features across the dataset. Here, we propose data imputation and feature engineering methods that employ pre-trained large language models (LLMs) to enhance machine learning performance on scarce, heterogeneous datasets, demonstrated on graphene CVD synthesis data and the ML-HydPARK hydrogen storage dataset. GPT models perform data imputation via tailored prompting and semantic normalization of inconsistently reported features through embeddings, for example, to harmonize the complex nomenclature of CVD substrates. Beyond yielding more diverse and richer feature representations than traditional methods such as K-nearest neighbors (KNN) and Multivariate Imputation by Chained Equations (MICE), LLM-based data imputation is evaluated against dataset characteristics and prompting strategies. We vary the level of autonomy granted to the LLM, from generic prompting that leverages pre-trained knowledge for autonomous data generation to data-informed prompting that constrains outputs using target-specific information, and demonstrate which level of autonomy yields superior imputation performance across datasets and feature types. The proposed data engineering methods markedly improve downstream performance; for example, in graphene layer number classification using a support vector machine (SVM), binary accuracy increases from 39% to 65% and ternary accuracy from 52% to 72%. Fine-tuning experiments on both datasets show that combining our proposed LLM-based data imputation and feature encoding methods with numerical machine learning predictors outperforms standalone fine-tuned LLM predictors in data-scarce settings. The proposed strategies emphasize data enhancement techniques rather than refining learning architectures or regularizing loss functions, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.

Chemical vapor deposition↗

DOE OSTI2024

Data for: A Synoptic System for Capturing Ecosystem Control Points Across Terrestrial-Aquatic Interfaces

The investigation of how climate change and water level fluctuations impact variable and interconnected ecosystems, like the interfaces between terrestrial and aquatic environments, requires the collection and integration of many data types. We describe an integrative and autonomous environmental monitoring approach that uses environmental sensors and data loggers to monitor surface water, groundwater, soil, and vegetation changes and generate essential data for predictive models. We established the network at seven sites along the Chesapeake Bay and Lake Erie coastlines, including a large-scale flood manipulation experiment, collectively generating over three million observations per month. Such sensor networks hold great promise for tracking and comprehending environmental changes where land and water intersect. The sensor system and overall approach to sensor management that we have designed is intended to be widely accessible for research teams spanning in size from an individual investigator to large multi-institution projects. This dataset shows example data generated by the sensor network described above. Data output for data loggers connected to groundwater water quality sondes measuring dissolved oxygen, pH, oxidative redox potential (ORP), groundwater elevation, groundwater salinity and temperature, and groundwater elevation; replicate soil moisture and conductivity probes installed at 10 and 30 cm below the ground surface; rainfall and solar radiation; and mean sap flow from 8 replicate probes. Sensors are installed at upland forest (UP), wetlands (W), and transitional locations between these ecosystems (TR). Example datasets are from Goodwin Islands in the Chesapeake Bay and Portage River along the Lake Erie coastline.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2011

ALHAT System Validation

NASA has embarked on a multiyear technology development effort to develop a safe and precise lunar landing capability. The Autonomous Landing and Hazard Avoidance Technology (ALHAT) Project is investigating a range of landing hazard detection methods while developing a hazard avoidance capability to best field test the proper set of relevant autonomous GNC technologies. Ultimately, the advancement of these technologies through the ALHAT Project will provide an ALHAT System capable of enabling next generation lunar lander vehicles to globally land precisely and safely regardless of lighting condition. This paper provides an overview of the ALHAT System and describes recent validation experiments that have advanced the highly capable GNC architecture.

Brady, Tye↗

NASA NTRS2012

Web: A Wireless Experiment Box for the Dextre Pointing Package ELC Payload

The Wireless Experiment Box (WEB) was proposed to work with the International Space Station (ISS) External Wireless Communication (EWC) system to support high-definition video from the Dextre Pointing Package (DPP). DPP/WEB was a NASA GSFC proposed ExPRESS Logistics Carrier (ELC) payload designed to flight test an integrated suite of Autonomous Rendezvous and Docking (AR&D) technologies to enable a wide spectrum of future missions across NASA and other US Government agencies. The ISS EWC uses COTS Wireless Access Points (WAPs) to provide high-rate bi-directional communications to ISS. In this paper, we discuss WEB s packaging, operation, antenna development, and performance testing.

Bleier, Leor Z.↗

NASA NTRS2012

WEB - A Wireless Experiment Box for the Dextre Pointing Package ELC Payload

The Wireless Experiment Box (WEB) was proposed to work with the International Space Station (ISS) External Wireless Communication (EWC) system to support high-definition video from the Dextre Pointing Package (DPP). DPP/WEB was a NASA GSFC proposed ExPRESS Logistics Carrier (ELC) payload designed to flight test an integrated suite of Autonomous Rendezvous and Docking (AR&D) technologies to enable a wide spectrum of future missions across NASA and other US Government agencies. The ISS EWC uses COTS Wireless Access Points (WAPs) to provide high-rate bi-directional communications to ISS. In this paper, we discuss WEB s packaging, operation, antenna development, and performance testing.

Bleier, Leor Z.↗

NASA NTRSDate not supplied

Experimental Capabilities and Achievements of the Space Environment Dynamometer (SED)

The Space Environment Dynamometer (SED) chamber was designed to conduct research in cryobotics; an area of study that focuses on robotic systems and rotating machinery operating in extreme cold environments including Earth, low Earth orbit, Mars, Moon, asteroids, Solar orbit, planetary orbit, or those encountered during travel among these destinations. The test chamber incorporates a modular dynamometer, consisting of a variety of brakes, torque sensors and motors to be easily interchanged between tests. Each test employs a unique test profile that incorporates different setpoints of applied torques and velocities for a given period or number of rev- olutions. The modularity of the dynamometer setup allows for any combination of motor, gearbox to be tested. This chamber is one of a kind and resides at the Swamp Works facility within the Granular Mechanics and Regolith Operations laboratory (GMRO) at NASA Kennedy Space Center. Other cold chambers exist, however they are project specific and do not simultaneously output real time torque, temperature and efficiency data. The modularity of this extreme cold environment test chamber, coupled with the custom software and instrumentation, makes it one of a kind. The chamber is capable of stabilizing cryogenic temperatures and pressures to commensurate moon environments. Tests have successfully been conducted on motors and gearboxes for various cryogenic temperature set points, torques, and angular velocities. Relevant internal temperatures of the test article and chamber were recorded using a variety of temperature sensors. The temperature setpoints, on the motors and gearboxes tested, were stabilized by using PID gain scheduling of the PWM signal for the various heaters. The heat removal for the motor was provided by creating a thermally conductive path from a cryohead directly to the test article using copper straps. A variety of strainwave gears (SWGs), also known as Harmonic drives, planetary gear systems and DC motor actuator configurations have been tested in the chamber. The experiments performed were for various projects including Bulk Metallic Glass Gears (BMGG), Volatiles Investigating Polar Exploration Rover (VIPER), In-Situ Resource Utilization Pilot Excavator (IPEX) . Various upgrades have been made to the extreme cold environment test chamber for the use of cryobotic research. These upgrades greatly increased the autonomous capabilities of the test set up by providing redundancies in the hardware and software. The redundancies were primarily added to protect the integrity of the cryohead. A new strapping and insulation method was performed to create the thermal conductive path from the actuators to the cryohead. The software was upgraded to include temperature setpoint control, further increasing the autonomous capabilities of the test. This paper goes into detail regarding the upgrades made to the extreme cold environment test chamber, as well as highlights the results from a COLDArm acceptance test.

Casey J. Clark↗

NASA NTRSDate not supplied

The Completion of a Geosynchronous Earth Orbit Survey with the Eugene Stansbery-Meter Class Autonomous Telescope

The Eugene Stansbery-Meter Class Autonomous Telescope (ES-MCAT) is the primary optical sensor used by the NASA Orbital Debris Program Office (ODPO) to statistically characterize the geosynchronous Earth orbit (GEO) debris environment and support future Orbital Debris Engineering Model (ORDEM) releases. The ES-MCAT completed its first optical survey of the GEO region from 2020 to 2022. The primary goal of this survey was to autonomously collect and process GEO data with calculated photometric and astrometric uncertainties. A pointing plan was developed to provide uniform sampling within the region of interest (ROI) while accounting for predicted downtime due to insufficient observing conditions. Detections are autonomously correlated to the Space Surveillance Network (SSN) catalog to determine if objects are correlated targets (CTs) or uncorrelated targets (UCTs), the latter of which are of interest for modeling the GEO orbital debris environment. To assess the size detection sensitivity over time and monitor the general performance of the telescope’s optics and software, the optical throughput of the system and limiting magnitudes are evaluated on a routine basis. While the telescope’s ability to operate autonomously and remotely allowed for the GEO survey to continue throughout the COVID-19 pandemic, travel restrictions hampered routine cleaning of the optics during this time, and the primary mirror degraded enough to require recoating. The mirror was removed in 2022, concluding the first GEO survey. The primary mirror received a new coating designed to be more robust against the harsh environment surrounding the ES-MCAT’s location on Ascension Island, accounting for experience gained during operations over the first GEO survey. In early 2023, the recoated primary mirror was reinstalled, and the second GEO survey was initiated. The primary goal of the second GEO survey is to characterize the evolving GEO debris environment with updated optics, software, and pointing strategies while allowing for the inclusion of non-GEO regimes or those that are outside of the ROI. While the pointing method implemented in the first survey allowed for adequate coverage of the ROI over two years, it has been improved to include pointings that avoid the Moon’s position and the galactic plane to reduce software processing time and maximize the detection capabilities of fainter objects. This method also accounts for the changing weather patterns throughout the year and reduces coverage gaps in the ROI. Provided the success of the first two-year GEO survey using autonomous operations, the ODPO is actively collaborating with the United States Space Force (USSF) to make the ES-MCAT a contributing sensor to the SSN. This paper presents results from the first GEO survey including magnitude distributions and orbital parameters for CTs and UCTs. Details are provided for the automated processing pipeline and the optical system throughput for the previous and current primary mirror coatings. In addition, an updated strategy for the second GEO survey to optimize coverage over the ROI is discussed, as are preliminary results from the ongoing second survey.

Corbin Cruz↗

NASA NTRSDate not supplied

The Completion of a Geosynchronous Earth Orbit Survey with the Eugene Stansbery-Meter Class Autonomous Telescope

The Eugene Stansbery-Meter Class Autonomous Telescope (ES-MCAT) is the primary optical sensor used by the NASA Orbital Debris Program Office (ODPO) to statistically characterize the geosynchronous Earth orbit (GEO) debris environment and support future Orbital Debris Engineering Model (ORDEM) releases. The ES-MCAT completed its first optical survey of the GEO region from 2020 to 2022. The primary goal of this survey was to autonomously collect and process GEO data with calculated photometric and astrometric uncertainties. A pointing plan was developed to provide uniform sampling within the region of interest (ROI) while accounting for predicted downtime due to insufficient observing conditions. Detections are autonomously correlated to the Space Surveillance Network (SSN) catalog to determine if objects are correlated targets (CTs) or uncorrelated targets (UCTs), the latter of which are of interest for modeling the GEO orbital debris environment. To assess the size detection sensitivity over time and monitor the general performance of the telescope’s optics and software, the optical throughput of the system and limiting magnitudes are evaluated on a routine basis. While the telescope’s ability to operate autonomously and remotely allowed for the GEO survey to continue throughout the COVID-19 pandemic, travel restrictions hampered routine cleaning of the optics during this time, and the primary mirror degraded enough to require recoating. The mirror was removed in 2022, concluding the first GEO survey. The primary mirror received a new coating designed to be more robust against the harsh environment surrounding the ES-MCAT’s location on Ascension Island, accounting for experience gained during operations over the first GEO survey. In early 2023, the recoated primary mirror was reinstalled, and the second GEO survey was initiated. The primary goal of the second GEO survey is to characterize the evolving GEO debris environment with updated optics, software, and pointing strategies while allowing for the inclusion of non-GEO regimes or those that are outside of the ROI. While the pointing method implemented in the first survey allowed for adequate coverage of the ROI over two years, it has been improved to include pointings that avoid the Moon’s position and the galactic plane to reduce software processing time and maximize the detection capabilities of fainter objects. This method also accounts for the changing weather patterns throughout the year and reduces coverage gaps in the ROI. Provided the success of the first two-year GEO survey using autonomous operations, the ODPO is actively collaborating with the United States Space Force (USSF) to make the ES-MCAT a contributing sensor to the SSN. This paper presents results from the first GEO survey including magnitude distributions and orbital parameters for CTs and UCTs. Details are provided for the automated processing pipeline and the optical system throughput for the previous and current primary mirror coatings. In addition, an updated strategy for the second GEO survey to optimize coverage over the ROI is discussed, as are preliminary results from the ongoing second survey.

Corbin Cruz↗

DOE OSTI2024

The Evolution of Airport Terminal Wayfinding Technologies

This work was presented during the Transportation Research Board (TRB) and Airport Cooperative Research Program (ACRP) webinar "Enhancing the Airport Experience with Wayfinding" on May 23, 2024. The process of navigating within airports has evolved over the years, with various technologies emerging to complement static signage. Historically, wayfinding began with static signage and airport staff members providing personal guidance, before progressing into digital signs, interactive kiosks, and displays. In recent years, wayfinding has continued along its digital path using mobile applications, indoor positioning technology, and even robot guides. With ever- larger airport facilities and a growing number of travelers, the future of wayfinding will likely be heavily linked to further digital developments such as facial recognition, augmented-reality technology, and autonomous vehicle navigation. This presentation delves into the history of interior wayfinding in airports, its present state, and the anticipated future. Key wayfinding technologies are discussed with an emphasis on emerging smartphone applications. Other considerations such as legal issues, language barriers, and human-technology interactions are included. A wayfinding framework is proposed, with static wayfinding technologies serving as the base upon which dynamic and personalized digital technologies are built. In this framework, electronic wayfinding technologies do not replace - but rather augment - traditional methods. These technologies can be integrated into existing wayfinding systems for a seamless traveler experience.

airport↗

DOE OSTI2026

Demonstration and analysis of volumetric additive manufacturing via sub-orbital spaceflight testing

Computed Axial Lithography (CAL) represents a significant advancement in the emerging field of Volumetric Additive Manufacturing (VAM). CAL addresses key limitations of traditional photopolymer additive manufacturing technologies, by eliminating the need for layering and support structures. Unlike conventional methods, CAL prints components by illuminating all points within a desired geometry simultaneously, using tomographic reconstruction to form the object in a single step. This unique approach eliminates the relative motion between the object and the precursor material, enabling faster printing speeds and reducing the waste associated with support structures. However, CAL parts require post-processing steps before they can be utilized. CAL's core attributes make it particularly suited for In-Space Manufacturing (ISM), due to its fast fabrication times, wide breadth of materials it can use, and minimized footprint. CAL has been successfully demonstrated in microgravity during parabolic flight experiments. However to fully validate and understand CAL's behaviour in microgravity, all manufacturing and post-processing steps must be integrated. In June 2024, we conducted SpaceCAL Mission 3, testing this entire workflow on a suborbital flight aboard Virgin Galactic's SpaceShipTwo. During ~140 s of microgravity, the system autonomously manufactured and post-processed four parts using PEGDA700 resin. Post-flight analysis showed that 2/4 parts were recognisable, while others were distorted due to bubble formation from residual water droplets, off-axis optical aberrations, and non-uniform solvent rinsing. Despite these limitations, this study represents the first integrated CAL workflow in space, providing an initial experimental demonstration and analysis for closed-loop in-space manufacturing.

Waddell, Taylor [Department of Mechanical Engineer↗

NASA NTRS2014

A Long Distance Laser Altimeter for Terrain Relative Navigation and Spacecraft Landing

A high precision laser altimeter was developed under the Autonomous Landing and Hazard Avoidance (ALHAT) project at NASA Langley Research Center. The laser altimeter provides slant-path range measurements from operational ranges exceeding 30 km that will be used to support surface-relative state estimation and navigation during planetary descent and precision landing. The altimeter uses an advanced time-of-arrival receiver, which produces multiple signal-return range measurements from tens of kilometers with 5 cm precision. The transmitter is eye-safe, simplifying operations and testing on earth. The prototype is fully autonomous, and able to withstand the thermal and mechanical stresses experienced during test flights conducted aboard helicopters, fixed-wing aircraft, and Morpheus, a terrestrial rocket-powered vehicle developed by NASA Johnson Space Center. This paper provides an overview of the sensor and presents results obtained during recent field experiments including a helicopter flight test conducted in December 2012 and Morpheus flight tests conducted during March of 2014.

Pierrottet, Diego F.↗

NASA NTRS2019

Everybody Needs Somebody Sometimes: Validation of Adaptive Recovery in Robotic Space Operations

This work assesses an adaptive approach to fault recovery in autonomous robotic space operations, which uses indicators of opportunity, such as physiological state measurements and observations of past human assistant performance, to inform future selections. We validated our reinforcement learning approach using data we collected from humans executing simulated mission scenarios. We present a method of structuring human-factors experiments that permits collection of relevant indicator of opportunity and assigned assistance task performance data, as well as evaluation of our adaptive approach, without requiring large numbers of test subjects. Application of our reinforcement learning algorithm to our experimental data shows that our adaptive assistant selection approach can achieve lower cumulative regret compared to existing non-adaptive baseline approaches when using real human data. Our work has applications beyond space robotics to any application where autonomy failures may occur that require external intervention.

Human-centered robotics↗

NASA NTRS1994

An incremental database access method for autonomous interoperable databases

We investigated a number of design and performance issues of interoperable database management systems (DBMS's). The major results of our investigation were obtained in the areas of client-server database architectures for heterogeneous DBMS's, incremental computation models, buffer management techniques, and query optimization. We finished a prototype of an advanced client-server workstation-based DBMS which allows access to multiple heterogeneous commercial DBMS's. Experiments and simulations were then run to compare its performance with the standard client-server architectures. The focus of this research was on adaptive optimization methods of heterogeneous database systems. Adaptive buffer management accounts for the random and object-oriented access methods for which no known characterization of the access patterns exists. Adaptive query optimization means that value distributions and selectives, which play the most significant role in query plan evaluation, are continuously refined to reflect the actual values as opposed to static ones that are computed off-line. Query feedback is a concept that was first introduced to the literature by our group. We employed query feedback for both adaptive buffer management and for computing value distributions and selectivities. For adaptive buffer management, we use the page faults of prior executions to achieve more 'informed' management decisions. For the estimation of the distributions of the selectivities, we use curve-fitting techniques, such as least squares and splines, for regressing on these values.

Roussopoulos, Nicholas↗

DOE OSTI2024

Redesigning large-scale multimodal transit networks with shared autonomous mobility services

Here, this study addresses a large-scale multimodal transit network design problem, with Shared Autonomous Mobility Services (SAMS) as both transit feeders and an origin-to-destination mode. The framework captures spatial demand and modal characteristics, considers intermodal transfers and express services, determines transit infrastructure investment and path flows, and generates transit routes. A system-optimal multimodal transit network is designed with minimum total door-to-door generalized costs of users and operators, satisfying transit origin-destination demand within a pre-set infrastructure budget. Firstly, the geography, demand, and modes in each zone are characterized with continuous approximation. The decisions of network link investment and multimodal path flows in zonal connection optimization are formulated as a minimum-cost multi-commodity network flow (MCNF) problem and solved efficiently with a mixed-integer linear programming (MILP) solver. Subsequently, the route generation problem is solved by expanding the MCNF formulation to minimize intramodal transfers. The model is illustrated through a set of experiments with the Chicago network comprised of 50 zones and seven modes, under three scenarios. The computational results present savings in traveler journey time and operator cost demonstrating the potential benefits of collaboration between multimodal transit systems and SAMS.

Autonomous vehicles↗