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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Fault Mitigation Schemes for Future Spaceflight Multicore Processors

Future planetary exploration missions demand significant advances in on-board computing capabilities over current avionics architectures based on a single-core processing element. The state-of-the-art multi-core processor provides much promise in meeting such challenges while introducing new fault tolerance problems when applied to space missions. Software-based schemes are being presented in this paper that can achieve system-level fault mitigation beyond that provided by radiation-hard-by-design (RHBD). For mission and time critical applications such as the Terrain Relative Navigation (TRN) for planetary or small body navigation, and landing, a range of fault tolerance methods can be adapted by the application. The software methods being investigated include Error Correction Code (ECC) for data packet routing between cores, virtual network routing, Triple Modular Redundancy (TMR), and Algorithm-Based Fault Tolerance (ABFT). A robust fault tolerance framework that provides fail-operational behavior under hard real-time constraints and graceful degradation will be demonstrated using TRN executing on a commercial Tilera(R) processor with simulated fault injections.

software based↗
NASA NTRS2011

Analysis and Testing of a LIDAR-Based Approach to Terrain Relative Navigation for Precise Lunar Landing

To increase safety and land near pre-deployed resources, future NASA missions to the moon will require precision landing. A LIDAR-based terrain relative navigation (TRN) approach can achieve precision landing under any lighting conditions. This paper presents results from processing flash lidar and laser altimeter field test data that show LIDAR TRN can obtain position estimates less than 90m while automatically detecting and eliminating incorrect measurements using internal metrics on terrain relief and data correlation. Sensitivity studies show that the algorithm has no degradation in matching performance with initial position uncertainties up to 1.6 km

ion propulsion↗
NASA NTRS2010

A New Strategy to Land Precisely on the Northern Plains of Mars

During the Phoenix mission landing site selection process, the Mars Reconnaissance Orbiter (MRO) High Resolution Imaging Science Experiment (HiRISE) images revealed widely spread and dense rock fields in the northern plains. Automatic rock mapping and subsequent statistical analyses showed 30-90% CFA (cumulative fractional area) covered by rocks larger than 1 meter in dense rock fields around craters. Less dense rock fields had 5-30% rock coverage in terrain away from craters. Detectable meter-scale boulders were found nearly everywhere. These rocks present a risk to spacecraft safety during landing. However, they are the most salient topographic features in this region, and can be good landmarks for spacecraft localization during landing. In this paper we present a novel strategy that uses abundance of rocks in northern plains for spacecraft localization. The paper discusses this approach in three sections: a rock-based landmark terrain relative navigation (TRN) algorithm; the TRN algorithm feasibility; and conclusions.

pin point landing↗
NASA NTRS2010

Analysis and Testing of a LIDAR-Based Approach to Terrain Relative Navigation for Precise Lunar Landing

Capability for precise lunar landing is the goal for future NASA missions. A LIDAR-based terrain relative navigation (TRN) approach lets us achieve this goal and also land under any illumination conditions. Results from field test data showed that the LIDAR TRN algorithm obtained position estimates with mean error of about 20 meters and standard deviations of about 10 meters. Moreover, the algorithm was capable of providing 99 percent correct estimates by assessing the local terrain relief in the data. Also, the algorithm was able to handle initial position uncertainty of up to 1.6 kilometers without performance degradation.

laser altimeter↗
NASA NTRS2017

Synopsis of Precision Landing and Hazard Avoidance (PL&HA) Capabilities for Space Exploration

Until recently, robotic exploration missions to the Moon, Mars, and other solar system bodies relied upon controlled blind landings. Because terrestrial techniques for terrain relative navigation (TRN) had not yet been evolved to support space exploration, landing dispersions were driven by the capabilities of inertial navigation systems combined with surface relative altimetry and velocimetry. Lacking tight control over the actual landing location, mission success depended on the statistical vetting of candidate landing areas within the predicted landing dispersion ellipse based on orbital reconnaissance data, combined with the ability of the spacecraft to execute a controlled landing in terms of touchdown attitude, attitude rates, and velocity. In addition, the sensors, algorithms, and processing technologies required to perform autonomous hazard detection and avoidance in real time during the landing sequence were not yet available. Over the past decade, NASA has invested substantial resources on the development, integration, and testing of autonomous precision landing and hazard avoidance (PL&HA) capabilities. In addition to substantially improving landing accuracy and safety, these autonomous PL&HA functions also offer access to targets of interest located within more rugged and hazardous terrain. Optical TRN systems are baselined on upcoming robotic landing missions to the Moon and Mars, and NASA JPL is investigating the development of a comprehensive PL&HA system for a Europa lander. These robotic missions will demonstrate and mature PL&HA technologies that are considered essential for future human exploration missions. PL&HA technologies also have applications to rendezvous and docking/berthing with other spacecraft, as well as proximity navigation, contact, and retrieval missions to smaller bodies with microgravity environments, such as asteroids.

Robertson, Edward A.↗
NASA NTRS2016

COBALT: A GN&C Payload for Testing ALHAT Capabilities in Closed-Loop Terrestrial Rocket Flights

The COBALT (CoOperative Blending of Autonomous Landing Technology) payload is being developed within NASA as a risk reduction activity to mature, integrate and test ALHAT (Autonomous precision Landing and Hazard Avoidance Technology) systems targeted for infusion into near-term robotic and future human space flight missions. The initial COBALT payload instantiation is integrating the third-generation ALHAT Navigation Doppler Lidar (NDL) sensor, for ultra high-precision velocity plus range measurements, with the passive-optical Lander Vision System (LVS) that provides Terrain Relative Navigation (TRN) global-position estimates. The COBALT payload will be integrated onboard a rocket-propulsive terrestrial testbed and will provide precise navigation estimates and guidance planning during two flight test campaigns in 2017 (one open-loop and closed- loop). The NDL is targeting performance capabilities desired for future Mars and Moon Entry, Descent and Landing (EDL). The LVS is already baselined for TRN on the Mars 2020 robotic lander mission. The COBALT platform will provide NASA with a new risk-reduction capability to test integrated EDL Guidance, Navigation and Control (GN&C) components in closed-loop flight demonstrations prior to the actual mission EDL.

Carson, John M., III↗
NASA NTRS2020

EDL Simulation Results for the Mars 2020 Landing Site Safety Assessment

The Mars 2020 rover is NASA’s next flagship mission, set to explore Mars in search of scientific evidence of past microbial life. Importantly, the rover will also, for the first time, have the ability to collect and cache rock and soil samples for retrieval and return to laboratories here on Earth. A key step in the development of the Mars 2020 mission is the selection of a suitable landing site with the largest likelihood of meeting scientific goals. This decision is a complex and critical one that requires close interaction between the scientific and engineering communities. The chosen landing site must be both scientifically interesting — providing the project with the greatest possible chance of gathering credible and defendable scientific evidence — and also safe enough to attempt a landing in the first place. Thus, arguably one of the most important undertakings of the Entry, Descent, and Landing (EDL) team, is to effectively enumerate, quantify, and communicate the landing risks to all of the stakeholders. The culmination of this effort is the Landing Site Safety Assessment, which is a review commissioned by the project, presided over by the EDL Standing Review Board, and attended by management and science stakeholders, in which the EDL team communicates their assessment of the associated landing risks and the statistical probability of a successful landing at each of the final candidate landing sites. This paper summarizes the results of high-fidelity computer simulations of the Mars 2020 EDL sequence used in this assessment. From an EDL performance perspective, all four candidates offer similar level of robustness, which is in-family with Mars Science Laboratory (MSL). However, two new features of the Mars 2020 EDL sequence – range trigger and Terrain-Relative Navigation (TRN) – dramatically enhance the capability of the EDL system to safely land at landing sites with much more rugged terrain than ever before considered. This has allowed the landing site selection for Mars 2020 to proceed in a manner that has been unprecedentedly weighted more heavily toward scientific interest and less heavily on engineering constraints. With TRN, the overall probability of success is predicted to be approximately 99% for all of the candidates.

David Way↗
NASA NTRSDate not supplied

OASSIS: Onboard Adaptive Safe-site Identification System Y3

The OASSIS Year 3 project continues to innovate with three goals: 1) transition to a generic configuration compatible with GNC flight software, 2) implement a new, computationally-efficient TRN algorithm for lunar landing, and 3) integrate with the a HWIL testbed to validate lunar landing GNC systems. This project enables lunar lander GNC flight software to be tested dynamically without the need of a costly flight campaign and without the risk of catastrophic hardware loss. Additionally, the TRN algorithm development and testing enhances the state-of-the-art in pinpoint landing navigation, ultimately improving the overall landing accuracy, safety, and reliability of a crewed lunar landing mission.

James S Mccabe↗
NASA NTRSDate not supplied

Building Maps for Terrain Relative Navigation Using Blender: An Open-Source Approach

A persistent challenge for vision-based navigation systems that compare imagery to a reference map is generating high quality maps with similar lighting conditions. Image rendering software can be used to apply variable lighting to reference maps or to generate synthetic imagery for test trajectories. While many image rendering software packages are available, with several developed specifically for spaceflight applications, there are often limitations due to cost, image fidelity, or flexibility. In this paper, we demonstrate the use of an open-source image rendering software, Blender, for use in Terrain Relative Navigation (TRN) applications. A scene in Blender was generated based on elevation data and satellite imagery of the region of West Texas used by Blue Origin for the operation of their New Shepard suborbital rocket. The Blender scene was validated by reproducing imagery collected during a flight of New Shepard in October 2020 and was further used to generate reference maps for use by a TRN algorithm on a subsequent New Shepard flight in August 2021. The work was performed under the NASA Safe and Precise Landing Integrated Capabilities Evolution (SPLICE) project, which is focused on technology advancement for precision landing and hazard avoidance. This work aims to lower the cost of entry and generally promote the adoption and advancement of vision-based navigation technologies.

Kyle W Smith↗
NASA NTRSDate not supplied

Digital Elevation Map Parametric Error Analysis Using Corresponding NAC Images

Future lunar landing systems, particularly those used to land humans on the lunar surface aspart of the ARTEMIS program, will require precision navigation relative to the lunar surface. The most common way to meet these stringent navigation require-ments is through terrain relative navigation (TRN), which localizes a spacecraft by comparing descent im-agery with a predefined map of the surface. The accu-racy achievable using TRN is limited by the accuracy of the reference Digital Elevation Map (DEM). It is therefore critical for future lunar missions that potential errors in DEMs be quantified. This paper describes one of NASA’s current efforts to develop a process for evaluating lunar DEM quality.

Chris R Gnam↗
NASA NTRSDate not supplied

Digital Elevation Map Parametric Error Analysis Pipeline using Corresponding NAC Images

Future lunar landing systems, particularly those used to land humans on the lunar surface as part of the ARTEMIS program, will require precision navigation relative to the lunar surface. The most common way to meet these stringent navigation requirements is through terrain relative navigation (TRN), which localizes a spacecraft by comparing descent imagery with a predefined map of the surface. The accuracy achievable using TRN is limited by the accuracy of the reference Digital Elevation Map (DEM). It is therefore critical for future lunar missions that potential errors in DEMs be quantified. This paper describes one of NASA’s current efforts to develop a process for evaluating lunar DEM quality.

Chris Gnam↗
NASA NTRS2021

Mars 2020 Perseverance Edl Gnc Safe Target Selection Reconstruction

On February 18th, 2021, NASA landed Perseverance on the Jezero crater (on Mars) using a new Terrain Relative Navigation (TRN) capability. TRN is com-prised of a new sensor, the Lander Vision System (LVS), and a new GNC algo-rithm, the Safe Target Selection (STS). LVS localized the descent vehicle with respect to a map. STS selected the safe landing target within a reachable region from the on-board Safe Targets Map (STM). The landing target was then handed to the Mars Science Laboratory (MSL) heritage powered descent GNC system to execute the landing. This paper describes the design and the as-flown performance of the STS algorithm.

Dutta, Soumyo↗
NASA NTRSDate not supplied

Low SWaP Onboard Satellite Navigation, Guidance, and Control Technology

Onboard autonomy is a necessity for responsive space operations. Autonomous navigation, guidance, and control (NGC) enables space missions to reduce their dependence on high demand ground assets and costly ground personnel. It also allows for in-situ decision making and higher return on mission data. A flight software and hardware system providing this capability, called “autoNGC,” is currently being developed at NASA Goddard Space Flight Center for infusion into multiple future missions. The first build of autoNGC, providing autonomous navigation for lunar orbiting spacecraft, is targeted for completion by Fall 2024. It provides sensor fusion of multiple measurement types including pseudo-range from a weak signal Global Navigation Satellite Service (GNSS) receiver, 1-way and 2-way direct to Earth (DTE) range and Doppler, bearing and range from optical camera sensed images, and an accelerometer. AutoNGC is also being targeted for future missions that involve small body proximity operations, Sun Earth Libration point orbits, and distributed systems missions (DSMs) including those at outer planets. AutoNGC flight software is being built upon the plug-and-play architecture of the core Flight System (cFS) [Ref. 1]. Figure (Slide 7) shows the message-based software bus layout of various software applications (“apps”) consisting of the standard cFS apps and autoNGC interface apps and libraries. Accurate onboard navigation and timing is obtained through the Goddard Enhanced Onboard Navigation System (GEONS) software library [Ref. 2], which fuses different measurement types through an extended Kalman filter (EKF) framework. Optical measurements that are ingested in GEONS are provided by the cFS Goddard Image Analysis and Navigation Tool (cGIANT) app [Ref. 3]. This app processes optical images to extract the bearing angles of the centroid of the imaged body (near or far), the range to the imaged body, and/or of the features on the surface of a body to perform terrain relative navigation (TRN). Measurement of range to the body’s center of mass can also be derived from the detection of the limb. The first build of autoNGC for a lunar orbiting spacecraft is a minimal size, weight, and power (SWaP) hardware design allowing for inclusion into CubeSats and SmallSat-size class buses. Advancements in miniaturized space processors, such as the SpaceCube 3.0 Mini and the SpaceCube Mini-Z [Ref. 4] are utilized for low SWaP while maintaining a high level of performance. Figure (Slide 11) shows the composition of the first autoNGC build. The current enclosure design has dimensions 12 cm x 17 cm x 13.5 cm. The box mass is expected to be less than 2 kg, and the nominal power is 21 W. The hardware interfaces are designed for flexibility with a variety of sensor inputs. The achievable navigation performance depends on the sensors utilized, including the onboard clock for 1-way pseudo-range measurements. Analysis using a configuration that consists of weak signal GPS, TRN, and 1-way DTE has shown position and velocity accuracies of 10 meters and 2 cm/s (3-σ ) RSS, respectively, with onboard time knowledge estimated to better than 13 ns (3-σ ), for a spacecraft in a representative 12-hour eccentric lunar orbit. Other measurement types such as x-rays from known pulsars (called XNAV) and cross-links can also be processed in GEONS. With the plug-and-play architecture of autoNGC, cFS apps can easily be added and replaced, even after launch. Goddard is actively seeking partners to collaborate in the development of additional capabilities for autoNGC, including industry, academia, and others across the US Government. Plans are being formulated to make the autoNGC software platform available for use by any US government organization to leverage the non-recurring engineering associated with the development of onboard autonomous NGC 3 capabilities. As advancements in space qualified sensors, microprocessors, and algorithms are made, the autoNGC platform provides a ready starting point for inclusion of these technologies.

C. J. Gramling↗
NASA NTRSDate not supplied

Machine Learning Based Crater Detection for Terrain Relative Navigation

As Lunar exploration continues to become more commonplace, reliable methods of precise Terrain Relative Navigation (TRN) are needed. While there are many TRN techniques available, one that has received increased interest in the past few years is that of crater based navigation. Crater based navigation has numerous benefits, including being a human recognizable feature (important for crewed missions), as well as the fact that craters are often possible hazards that need to be detected and avoided. The use of crater based navigation has been limited however. This has been due to the difficulty of running such algorithms on board a spacecraft, as well as the difficulty in procuring large amounts of the required training data. This paper presents a new rendering tool for generating large amounts of high quality training data. It then looks at two recently developed machine learning techniques for crater detection and crater identification in real-time on near-future space hardware.

computer vision↗
DOE OSTI2022

Incorporating climate change into risk-informed resilience planning

In response to the development of portfolio-wide Climate Action Plans by federal agencies, federal sites are working to incorporate the impacts of climate change into their resilience assessments. However, it can be challenging to incorporate climate change scenarios into resilience assessments given the uncertainty inherent in climate change modeling. Incorporating these factors into a resilience plan requires an understanding of what the different climate scenarios mean, as well as how to estimate potential impacts of climate change on hazard occurrence on a regional, or even local, scale under different scenarios. We discuss approaches to incorporating this data into risk-informed resilience assessment processes, such as those implemented in the Department of Energy’s (DOE) Federal Energy Management Program’s (FEMP) Technical Resilience Navigator (TRN) and the Sustainability Performance Division’s (SPD) Vulnerability Assessment and Resilience Plan (VARP) Risk Assessment Tool. We also describe the climate scenarios and the availability of hazard data for site resilience planning, based on modeling included in the International Panel on Climate Change (IPCC), the National Climate Assessment (NCA), and state-level reports. We present examples from the TRN risk assessment and the VARP Risk Assessment Tool to illustrate how sensitivity analysis can be used to incorporate climate change projections into the resilience planning process.

Rabinowitz, Hannah S.↗
NASA NTRSDate not supplied

Terrestrial Demonstration of Orbital Mapping and Validation Capabilities Over a Lunar Surface Analog

The Lunar Navigation Maps (LuNaMaps) project has improved existing tools and processes and developed new tools and processes to support the generation and validation of navigation maps of the lunar surface from orbital imagery. To demonstrate the advancements made through the LuNaMaps project, we conducted a terrestrial demonstration obtaining “orbital” imagery of the Lunar Surface Proving Ground (LSPG) at Astrobotic’s test facility in Mojave California. The LSPG is a 100 m by 100 m pad built to mimic the features and appearance of the lunar surface. In this paper we describe the planning and results of the test, including the capture of imagery for building the maps, the map building processes, building of a “truth map” using traditional surveying tools, and the map validation processes. We demonstrate how the built map compares to the “truth map” and how the validation processes provided insight to this comparison. Additionally, we describe an upcoming partner test in which the navigation maps will be used in a terrain relative navigation (TRN) technology demonstration over the same LSPG surface.

mapping↗
DOE OSTI2024

Terrain-Relative Navigation with Neuro-Inspired Elevation Encoding

Terrain-relative navigation (TRN) encompasses a wide variety of algorithms that perform localization with respect to the terrain below a flying vehicle. In traditional approaches, measurements of the terrain are matched to a map carried onboard. This work presents a terrain-relative navigation filter with a position measurement inspired by neural activity associated with positioning in nature. Here, the filter is shown to produce accurate position measurements that outperform popular optimization and template matching methods given poor prior knowledge of the position. The proposed method is also better-suited to distributed implementation than optimization-based methods.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1987

Using the thermal infrared multispectral scanner (TIMS) to estimate surface thermal responses

A series of measurements was conducted over the H.J. Andrews, Oregon, experimental coniferous forest, using airborne thermal infrared multispectral scanner (TIMS). Flight lines overlapped, with a 28-min time difference between flight lines. Concurrent radiosonde measurements of atmospheric profiles of air temperature and moisture were used for atmospheric radiance corrections of the TIMS data. Surface temperature differences over time between flight lines were used to develop thermal response numbers (TRNs) which characterized the thermal response (in KJ/sq m/C, where K is the measured incoming solar radiation) of the different surface types. The surface types included a mature forest (canopy dominated by dense crowns of Pseudosuga menziesii, with a secondary canopy of dense Tsuga heterophylla, and also a tall shrub layer of Acer circinatum) and a two-year-old clear-cut. The temperature distribution, within TIMS thermal images was found to reflect the surface type examined. The clear-cut surface had the lowest TRN, while mature Douglas fir the highest.

Luvall, J. C.↗