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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

SEXTANT X-Ray Pulsar Navigation Demonstration: Flight System and Test Results

The Station Explorer for X-ray Timing and Navigation Technology (SEXTANT) is a technology demonstration enhancement to the Neutron-star Interior Composition Explorer (NICER) mission. NICER is a NASA Explorer Mission of Opportunity that will be hosted on the International Space Station (ISS). SEXTANT will, for the first time, demonstrate real-time, on-board X-ray Pulsar Navigation (XNAV), a significant milestone in the quest to establish a GPS-like navigation capability available throughout our Solar System and beyond. This paper gives an overview of the SEXTANT system architecture and describes progress prior to environmental testing of the NICER flight instrument. It provides descriptions and development status of the SEXTANT flight software and ground system, as well as detailed description and results from the flight software functional and performance testing within the high-fidelity Goddard Space Flight Center (GSFC) X-ray Navigation Laboratory Testbed (GXLT) software and hardware simulation environment. Hardware-in-the-loop simulation results are presented, using the engineering model of the NICER timing electronics and the GXLT pulsar simulator-the GXLT precisely controls NASA GSFC's unique Modulated X-ray Source to produce X-rays that make the NICER detector electronics appear as if they were aboard the ISS viewing a sequence of millisecond pulsars

Pulsar↗
NASA NTRS1998

Development and Testing of Automatically Generated ACS Flight Software for the MAP Spacecraft

By integrating the attitude determination and control system (ACS) analysis and design, flight software development, and flight software testing processes, it is possible to improve the overall spacecraft development cycle, as well as allow for more thorough software testing. One of the ways to achieve this integration is to use code-generation tools to automatically generate components of the ACS flight software directly from a high-fidelity (HiFi) simulation. In the development of the Microwave Anisotropy Probe (MAP) spacecraft, currently underway at the NASA Goddard Space Flight Center, approximately 1/3 of the ACS flight software was automatically generated. In this paper, we will examine each phase of the ACS subsystem and flight software design life cycle: analysis, design, and testing. In the analysis phase, we scoped how much software we would automatically generate and created the initial interface. The design phase included parallel development of the HiFi simulation and the hand-coded flight software components. Everything came together in the test phase, in which the flight software was tested, using results from the HiFi simulation as one of the bases of comparison for testing. Because parts of the spacecraft HiFi simulation were converted into flight software, more care needed to be put into its development and configuration control to support both the HiFi simulation and flight software. The components of the HiFi simulation from which code was generated needed to be designed based on the fact that they would become flight software. This process involved such considerations as protecting against mathematical exceptions, using acceptable module and parameter naming conventions, and using an input/output interface compatible with the rest of the flight software. Maintaining good configuration control was an issue for the HiFi simulation and the flight software, and a way to track the two systems was devised. Finally, an integrated test approach was devised to support flight software testing at both the unit- and build-test levels using the HiFi simulation to generate data for performance verification. Another benefit of the simulation and code-generation application used on the MAP project is that it supported bringing flight software and test data into the HiFi simulation environment. It was possible to integrate parts of the hand-coded flight software into the HiFi simulation, and also possible to import flight software test data for comparison and performance verification. This capability was used to incorporate the flight software Kalman filter into the HiFi simulation. This enabled us to greatly increase the amount of testing that could be done on the filter, because we could exert a greater degree of control over the software-only simulation than over the flight software test environment. Also, since the simulation could be used to run the Kalman filter faster than real time, our testing efficiency was greatly increased. We will conclude our discussion with a summary of the lessons learned thus far using automatically- generated code for the MAP project, and the spacecraft status as we work towards our scheduled launch in the year 2000.

ODonnell, James R., Jr.↗
NASA NTRS2011

High-Performance 3D Articulated Robot Display

In the domain of telerobotic operations, the primary challenge facing the operator is to understand the state of the robotic platform. One key aspect of understanding the state is to visualize the physical location and configuration of the platform. As there is a wide variety of mobile robots, the requirements for visualizing their configurations vary diversely across different platforms. There can also be diversity in the mechanical mobility, such as wheeled, tracked, or legged mobility over surfaces. Adaptable 3D articulated robot visualization software can accommodate a wide variety of robotic platforms and environments. The visualization has been used for surface, aerial, space, and water robotic vehicle visualization during field testing. It has been used to enable operations of wheeled and legged surface vehicles, and can be readily adapted to facilitate other mechanical mobility solutions. The 3D visualization can render an articulated 3D model of a robotic platform for any environment. Given the model, the software receives real-time telemetry from the avionics system onboard the vehicle and animates the robot visualization to reflect the telemetered physical state. This is used to track the position and attitude in real time to monitor the progress of the vehicle as it traverses its environment. It is also used to monitor the state of any or all articulated elements of the vehicle, such as arms, legs, or control surfaces. The visualization can also render other sorts of telemetered states visually, such as stress or strains that are measured by the avionics. Such data can be used to color or annotate the virtual vehicle to indicate nominal or off-nominal states during operation. The visualization is also able to render the simulated environment where the vehicle is operating. For surface and aerial vehicles, it can render the terrain under the vehicle as the avionics sends it location information (GPS, odometry, or star tracking), and locate the vehicle over or on the terrain correctly. For long traverses over terrain, the visualization can stream in terrain piecewise in order to maintain the current area of interest for the operator without incurring unreasonable resource constraints on the computing platform. The visualization software is designed to run on laptops that can operate in field-testing environments without Internet access, which is a frequently encountered situation when testing in remote locations that simulate planetary environments such as Mars and other planetary bodies.

Powell, Mark W.↗
NASA NTRS1990

The test facility requirements for the thermal vacuum thermal balance test of the Cosmic Background Explorer Observatory

The Cosmic Background Explorer Observatory (COBE) underwent a thermal vacuum thermal balance test in the Space Environment Simulator (SES). This was the largest and most complex test ever conducted at this facility. The 4 x 4 m (13 x 13 ft) spacecraft weighed approx. 2223 kg (4900 lbs) for the test. The test set up included simulator panels for the inboard solar array panels, simulator panels for the flight cowlings, Sun and Earth Sensor stimuli, Thermal Radio Frequency Shield heater stimuli and a cryopanel for thermal control in the Attitude Control System Shunt Dissipator area. The fixturing also included a unique 4.3 m (14 ft) diameter Gaseous Helium Cryopanel which provided a 20 K environment for the calibration of one of the spacecraft's instruments, the Differential Microwave Radiometer. This cryogenic panel caused extra contamination concerns and a special method was developed and written into the test procedure to prevent the high buildup of condensibles on the panel which could have led to backstreaming of the thermal vacuum chamber. The test was completed with a high quality simulated space environment provided to the spacecraft. The test requirements, test set up, and special fixturing are described.

Milam, Laura J.↗
NASA NTRS1991

Test facility requirements for the thermal vacuum thermal balance test of the Cosmic Background Explorer Observatory

The Cosmic Background Explorer Observatory (COBE) underwant a thermal vacuum thermal balance test in the Space Environment Simulator (SES). This was the largest and most complex test ever conducted at this facility. The 4 x 4 m (13 x 13 ft) spacecraft weighed approx. 2223 kg (4900 lbs) for the test. The test set up included simulator panels for the inboard solar array panels, simulator panels for the flight cowlings, Sun and Earth Sensor stimuli, Thermal Radio Frequency Shield heater stimuli and a cryopanel for thermal control in the Attitude Control System Shunt Dissipator area. The fixturing also included a unique 4.3 m (14 ft) diameter Gaseous Helium Cryopanel which provided a 20 K environment for the calibration of one of the spacecraft's instruments, the Differential Microwave Radiometer. This cryogenic panel caused extra contamination concerns and a special method was developed and written into the test procedure to prevent the high buildup of condensibles on the panel which could have led to backstreaming of the thermal vacuum chamber. The test was completed with a high quality simulated space environment provided to the spacecraft. The test requirements, test set up, and special fixturing are described.

Milam, Laura J.↗
NASA NTRS1998

Object Creation and Human Factors Evaluation for Virtual Environments

The main objective of this project is to provide test objects for simulated environments utilized by the recently established Army/NASA Virtual Innovations Lab (ANVIL) at Marshall Space Flight Center, Huntsville, Al. The objective of the ANVIL lab is to provide virtual reality (VR) models and environments and to provide visualization and manipulation methods for the purpose of training and testing. Visualization equipment used in the ANVIL lab includes head-mounted and boom-mounted immersive virtual reality display devices. Objects in the environment are manipulated using data glove, hand controller, or mouse. These simulated objects are solid or surfaced three dimensional models. They may be viewed or manipulated from any location within the environment and may be viewed on-screen or via immersive VR. The objects are created using various CAD modeling packages and are converted into the virtual environment using dVise. This enables the object or environment to be viewed from any angle or distance for training or testing purposes.

Lindsey, Patricia F.↗
NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Reinforcement Learning

To continue on a sustainable and flexible path, NASA needs to address the challenge of collecting and moving large amounts of regolith at the destination. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from a reduced sensor payload. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments to develop autonomous trenching controllers via reinforcement learning and prototype state estimation architectures. The goal of reinforcement learning is for an agent to learn a policy (task strategy) through interactions with an environment. When the agent performs an action, a change occurs in environment state and a numerical reward is received which informs the agent whether the action performed was good or not. Since reinforcement learning algorithms learn through trial-and-error, a simulation is a desirable first environment for development and learning. We developed two simulations, the first is a 2D excavation simulation developed to facilitate parameter selection, and a 3D simulation developed using a game physics engine, to simulate simplified soil interactions and increase the fidelity of the dynamic models of the robotic agents. The development of this 3D simulation has enabled the training of additional sensing capabilities and research both at the granular mechanics and operations levels. We experimented with various virtual sensor payloads to identify a combination that enabled efficient excavation operation and learning. Our reward function is based on how much material is excavated per step. A penalty is also received for leaving the dig site and to smooth the acceleration of the drum arms. We implemented pseudo time-of-flight sensors to report distance from each drum to ground and the height above ground which was found to be more efficient than existing solutions. Our findings suggest that reinforcement learning for autonomous operations has learned viable trenching strategies within 3000 training episodes in our simplified 2D environment and helped identify desirable sensing capabilities, arrangements, and considerations such as the positioning of time-of-flight sensors. Future work includes expanding our simulation to more complex environments and scenarios, and transfer learning from simulation to RASSOR 2.0 hardware for deployment in the Regolith Test Bin at NASA's Kennedy Space Center.

rassor↗
NASA NTRS2011

Hazard Detection Software for Lunar Landing

The Autonomous Landing and Hazard Avoidance Technology (ALHAT) Project is developing a system for safe and precise manned lunar landing that involves novel sensors, but also specific algorithms. ALHAT has selected imaging LIDAR (light detection and ranging) as the sensing modality for onboard hazard detection because imaging LIDARs can rapidly generate direct measurements of the lunar surface elevation from high altitude. Then, starting with the LIDAR-based Hazard Detection and Avoidance (HDA) algorithm developed for Mars Landing, JPL has developed a mature set of HDA software for the manned lunar landing problem. Landing hazards exist everywhere on the Moon, and many of the more desirable landing sites are near the most hazardous terrain, so HDA is needed to autonomously and safely land payloads over much of the lunar surface. The HDA requirements used in the ALHAT project are to detect hazards that are 0.3 m tall or higher and slopes that are 5 or greater. Steep slopes, rocks, cliffs, and gullies are all hazards for landing and, by computing the local slope and roughness in an elevation map, all of these hazards can be detected. The algorithm in this innovation is used to measure slope and roughness hazards. In addition to detecting these hazards, the HDA capability also is able to find a safe landing site free of these hazards for a lunar lander with diameter .15 m over most of the lunar surface. This software includes an implementation of the HDA algorithm, software for generating simulated lunar terrain maps for testing, hazard detection performance analysis tools, and associated documentation. The HDA software has been deployed to Langley Research Center and integrated into the POST II Monte Carlo simulation environment. The high-fidelity Monte Carlo simulations determine the required ground spacing between LIDAR samples (ground sample distances) and the noise on the LIDAR range measurement. This simulation has also been used to determine the effect of viewing on hazard detection performance. The software has also been deployed to Johnson Space Center and integrated into the ALHAT real-time Hardware-in-the-Loop testbed.

Huertas, Andres↗
NASA NTRS2021

Quantifying the Observational Requirements of a Space-borne LiDAR Snow Mission

This study quantifies the level of observational accuracy required from a spaceborne light detection and ranging (LiDAR) snow depth retrieval mission for enabling beneficial impacts for snow estimation. The study is conducted over a region in Western Colorado using a suite of observing system simulation experiments(OSSEs).The Joint UK Land Environment Simulator, version 5.0 (JULES v5.0) is employed to simulate a suite of idealized LiDAR observations, considering a range of LiDAR snow depth retrieval errors, different hypothetical sensor swath widths, and the impact of cloud cover on observability. These simulated observations are then assimilated into the Noah land surface modelwith multi-parameterization options, version 3.6 (Noah-MP v3.6) model. This data assimilation setup is used to systematically evaluate the potential utility of LiDAR observations for improving modeled snow water equivalent (SWE)estimates and water budget variables such as runoff. Results from the OSSE runsshow that, in general, assimilation of synthetic LiDAR observations provide beneficial impacts when theLiDAR snow depth retrieval error standard deviation (σerror) is below 60 cm.Based on comparisons between the realistic (i.e., swath-limited and cloud-attenuated) case and the idealized (i.e., infinite swath width in the absence of cloud cover) case,this study concludes that observations with a conservative error standard deviation threshold of 40 cm (i.e., upper limit of the snow depth retrieval error that adds value to the SWE estimates viaassimilation) are needed for improving modeled snow estimates. More than a 33% reduction in SWEroot mean square errors and more than a 15% increase in correlation coefficientsare achieved when σ error ≤ 40 cmusing a 170-km sensor swath width in the presence of cloud attenuation effects. Further, the integrated hydrologic response, as represented by total (surface and subsurface) runoff estimates during the snow ablation season, are also enhanced when assimilating synthetic LiDAR snow depth retrievals with errors below this level.

Yonghwan Kwon↗
NASA NTRS2022

Cooperative Separation in Upper Class E Airspace

This document presents baseline functional requirements for a prototype NASA research Upper Class E Traffic Management (ETM) system to enable the cooperative separation concept [1, 2]. The baseline functional requirements are developed by incorporating inputs from NASA and the FAA researchers and engineering staff, and industry partners while accounting for the unique performance characteristics and mission needs of various existing and future ETM vehicle types. The functionalities include information sharing for situational awareness, conformance monitoring, and operating practices for cooperative separation. Several realistic traffic scenarios were built to test, validate, and demonstrate the cooperatively managed operation in the ETM environment and the associated capabilities in a simulation environment.

Upper Class E Air Traffic Management↗
NASA NTRS2006

Concept for coring from a low-mass rover

Future Mars missions, such as the Mars Sample Return (MSR) mission, may benefit from core sample acquisition from a low-mass rover where the rover cannot be assumed to be stationary during a coring operation. Manipulation from Mars rovers is currently done under the assumption that the rover acts as a stationary, stable platform for the arm. An MSR mission scenario with a low-mass rover has been developed and the technology needs have been investigated. Models for alternative types of coring tools and tool-environment interaction have been developed and input along with wheel-soil interaction models into the Stanford Simulation & Active Interfaces (SAI) simulation environment to enable simulation of coring operations from a rover. Coring tests using commercial coring tools indicate that the quality of the core is a critical criterion in the system design. Current results of the models, simulation, and coring tests are provided.

rovers↗
NASA NTRS2018

Autonomous Nested Search for Hydrothermal Venting

Ocean Worlds in the outer solar system represent one of the best chances for the discovery of extra-terrestrial life. Bodies, such as Europa and Enceladus, are thought to harbor liquid oceans, often encased in a thick icy shell. In order to further investigate these oceans, a new mission concept needs to be developed, a submersible craft. This vehicle would be required to traverse the icy shell and travel hundreds or even thousands of kilometers to survey the ocean below. In doing this, the vehicle might be out of contact for weeks or months at a time. The vehicle must be able to autonomously detect,locate, and study features of interest. One potential target is hydrothermal venting, due to their unique ecosystems on Earth. We have developed an autonomous, nested search strategy to locate sources of hydrothermal venting based on currently used methods. To test this search technique a simulation environment was developed using a hydrothermal plume dispersion simulation and a vehicle model. We show the effectiveness of the search method in this environment.

Seewald, Jeffrey S.↗
NASA NTRSDate not supplied

ICE-RASSOR: Intelligent Capabilities Enhanced Regolith Advanced Surface Systems Operations Robot

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU)processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from a reduced sensor payload. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We apply supervised learning using real data to estimate the soil mass collected without the need for mass flow rate monitors or other explicate sensing techniques. We also create a reduced-order simulation environment to develop autonomous trenching controllers via reinforcement learning and prototype state estimation architectures. Our initial results suggest that excavated regolith mass can be inferred within 2.9% RMS error of full scale, and reinforcement learning for autonomous operations has learned viable trenching strategies and helped identify desirable sensing capabilities, arrangements, and considerations. Future work includes regolith mass estimation during dynamic operation, expanding our simulation to more complex environments, and transfer learning from simulation to hardware.

machine learning↗
NASA NTRSDate not supplied

ICE-RASSOR: Intelligent Capabilities Enhanced

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RAS-SOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar sur-face, RASSOR software and sensory systems need to be robust and maximize the information extracted from on-board sensing. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We apply supervised learning using real data to estimate the soil mass collected without the need for mass flow rate monitors or other explicate sensing techniques. We also create a reduced-order simulation environment to develop autonomous trenching controllers via reinforcement learning and proto-type state estimation architectures. Our initial results suggest that excavated regolith mass can be inferred within 2.9% RMS error of full scale, and reinforcement learning for autonomous operations has learned viable trenching strategies and helped identify desirable sensing capabilities, arrangements, and considerations. Future work includes regolith mass estimation during dynamic operation, expanding our simulation to more complex environments, and transfer learning from simulation to hardware.

machine learning↗
NASA NTRSDate not supplied

ICE-RASSOR: Intelligent Capabilities Enhanced Regolith Advanced Surface Systems Operations Robot

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from on-board sensory. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We apply supervised learning using real data to estimate the soil mass collected without the need for mass flow rate monitors or other explicate sensing techniques. We also create a reduced-order simulation environment to develop autonomous trenching controllers via reinforcement learning and proto-type state estimation architectures. Our initial results suggest that excavated regolith mass can be inferred within 2.9% RMS error of full scale, and reinforcement learning for autonomous operations has learned viable trenching strategies and helped identify desirable sensing capabilities, arrangements, and considerations. Future work includes regolith mass estimation during dynamic operation, expanding our simulation to more complex environments, and transfer learning from simulation to hardware.

machine learning↗