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NASA NTRSDate not supplied

Lunar Search & Rescue Applications of Lunar GNSS

Accurate lunar navigation and timing knowledge provides for the development of safety-critical services in the cislunar and lunar surface domain. Currently under development, the Goddard Space Flight Center’s (GSFC) Search and Rescue Mission Office is investigating and integrating search and rescue (SAR) capability into planned and future lunar communication and navigation interfaces. Lunar Search and Rescue (LunaSAR) development has a stated end-goal for assured, reliable, and timely indication of distress events for a wide variety of lunar surface users, including government-sponsored, commercial, and international users. LunaSAR performance requirements are modelled after the current terrestrial Cospas-Sarsat distress notification system, leveraging an internationally robust global navigation satellite system (GNSS) ecosystem as a core element of survivor locating capability. This presentation will discuss NASA’s work to develop user-focused distress messaging capabilities including infusion of example sensor data for triggering of automated distress alerts coupled with location-tagging. Additionally, the presentation will examine overall message structures, rotating fields for use in bi-directional distress messaging, and specific use cases based on NASA’s lunar exploration and lunar communication relay architectures. Modelling and simulation of LunaSAR use by individual lunar explorers will be discussed, based on notional industry and government design reference missions and mission considerations. Results from GSFC-funded Internal Research and Development (IRAD) efforts will be detailed, including successful distress message formulation simulating the ingestion of example legacy space suit telemetry fields. Hardware-in-the-loop testing using high-reliability software defined radio (SDR) modules serve as an example of IRAD successes and the framework for technical requirements. Architectural development and technical evolution from 2020 to 2021 included alignment of LunaSAR distress waveforms with ongoing NASA LunaNet interoperability development, as well as engagement with NASA Lunar Spectrum authorities for allocation of UHF-band distress frequencies on the lunar surface. S-Band and UHF-band transmission characteristics will be detailed, along with band-specific applications of each emission type. Additionally, examples of ingestion and formatting of GNSS signals (using historical terrestrial National Marine Electronics Association-formatted GNSS data) will be detailed, underscoring lunar user needs for a common lunar GNSS receiver output message framework. Maturity and ability to support evolving lunar exploration goals has been demonstrated and will be detailed, with maturity gaps such as position, navigation, and timing (PNT) and lunar reference frames identified within the context of distress message generation. Provision of LunaSAR services for lunar surface users represents a new era of ensured safety for lunar explorers and builds off of forty years of the Cospas-Sarsat program, underscoring the importance of lunar GNSS for safety-critical applications and growing interest in safe, reliable lunar surface operations. Enabled by new GNSS systems being developed by government and industry partners, NASA will continue to evolve and integrate lunar GNSS types into distress message generation, with a focus on compact and efficient message transmission over various lunar communication links. When fielded, LunaSAR will be the first dedicated search and rescue notification system employed on another celestial body. Robust lunar navigation and timing services form the core of LunaSAR capabilities, allowing for system syncing with time-dominant sensors, and high-accuracy location of those in distress while engaged in lunar surface activities.

Search and Rescue↗
NASA NTRSDate not supplied

NASA and Blue Origin’s Flight Assessment of Precision Landing Algorithms Computing Performance

NASA’s Safe and Precise Landing - Integrated Capabilities Evolution (SPLICE) project continues NASA’s work in the development and testing of technologies for Precision Landing and Hazard Avoidance (PL&HA). This paper presents results characterizing how SPLICE flight software utilizes the shared computing resources of the Descent Landing Computer (DLC), one of the PL&HA technologies under development. The SPLICE technologies are being tested as an integrated payload on Blue Origin’s New Shepard suborbital vehicle. The results presented in this paper are measured by applications running in and with the flight software both in flight, and in a high-fidelity Hardware-in-the-Loop (HWIL) simulation environment. Linux utilities to measure performance are also executed from the command line in the HWIL configuration. Performance measurements of the SPLICE workloads executing on the DLC provide insight on how efficiently the software is utilizing the DLC resources. Examples of how these measurements have guided improvements in the flight code are presented. In addition, the DLC uses a commercial processor as a surrogate for NASA’s High-Performance Spaceflight Computing (HPSC) processor. This work provides insight on how an HPSC system may perform delivering PL&HA capabilities on a future mission. The measurements also can be used to infer architectural requirements for PL&HA capabilities, informing the HPSC project and other flight computer development efforts. Examples of the measurements collected include processor utilization, I/O bandwidth, cache and branch misses, and application profiles.

Precision Landing↗
NASA NTRS2022

Blue Origin De-orbit Descent and Landing Tipping Point: Program Final Report

The purpose of this document is to provide a final report on the Deorbit, Descent, and Landing (DDL) Tipping Point program, a public-private partnership between Blue Origin and NASA that is partially funded by NASA contract 80LARC19C0005. In this report we summarize the results of the entire contract, including recommendations and conclusions based on the experience and results obtained. Only the portions funded by the government with associated unlimited rights are documented in detail in this report. For the work funded by Blue Origin, summaries with unlimited rights are provided for context and completeness. The Blue Origin funded work exceeded 25% of the originally proposed total program cost and included a mixture of hardware procurement and critical technology maturation. The scope of this document is a discussion of all the major tasks performed under the contract including the sensor flight demonstrations on New Shepard, the hardware in the loop lunar landing navigator demonstration, and the ground testing of the Flash LiDAR hazard sensor. The contributions from the multiple NASA teams – Johnson Space Center, Langley Research Center, Goddard Space Flight Center, and Jet Propulsion Laboratory – are described along with the contributions from Blue Origin. This document is the Final Report for statement of work item 4.1.2.10 as Deliverable 5.8.

Stefan Bieniawski↗
NASA NTRSDate not supplied

Evaluation of Precision Landing Performance Using A Generalized Aerospace Simulation in Simulink Framework

NASA’s science and exploration goals to return to the Moon and beyond will need to perform precision landings to place humans and cargo supplies near places of scientific interest, surface resources, or pre-established basecamps. With the maturation of new navigation technology, such as terrain relative navigation, precision landing is now feasible, enabling new exploration sites, such as the lunar poles. However, verification of precision landing performance becomes crucial since not reaching the designated landing site would have a high risk of loss of mission. Therefore, having a high-fidelity simulation platform to evaluate six degrees of freedom vehicle performance during high-risk phases of flight such landing is a fundamental part of the system verification and risk reduction. The NASA Marshall Space Flight Center has developed the GeneraLized Aerospace Simulation in Simulink® (GLASS) tool which incorporates guidance, navigation, and control algorithms, as well as vehicle and environmental models, such as gravity, vehicle mass properties, navigation sensors, propulsion, and terrain models. GLASS uses the MathWorks® Simulink® environment which provides a model-based design framework that allows the incorporation of vehicle models in a modular architecture. The Simulink® environment provides seamless integration with all the MathWorks® capabilities and toolboxes, such as control design toolboxes and Simscape™ Multibody™ dynamics toolbox. The MathWorks® environment also allows for guidance, navigation, and control algorithms to be auto coded in C language, enabling quick software and hardware in the loop testing. This paper provides an overview of GLASS capabilities for analyzing precision landing performance, including navigation trades applied to a NASA human lander reference design architecture.

Guidance↗
NASA NTRSDate not supplied

Design, Instrumentation, and Data Analysis for the SLS Core Stage Green Run Test Series

The Space Launch System (SLS) Core Stage (CS) Thrust Vector Control (TVC) system is comprised of eight mechanical feedback Shuttle heritage Type III TVC actuators and four RS-25 engines, each attached to a Shuttle heritage gimbal block/bearing. The actuators are powered by a Shuttle-derived hydraulic Core Auxiliary Power Unit (CAPU), and integrated with an all-new Core Stage thrust structure. The actuators are interfaced to the SLS Vehicle Management (VM) software via an all-new TVC Actuator Control (TAC) avionics subsystem. Despite the significant test and flight experience of the Shuttle hardware, the SLS Green Run ambient and hot fire test activities revealed a number of new findings associated with the dynamic response of the TVC integrated system. Test responses suggested that the TVC system did not meet its performance specifications and its step and frequency responses exhibited unexpected departures from prior lab tests and modeled behavior. This paper is the fifth installment in a seven-paper series surveying the design, engineering, test validation, and flight performance of the Core Stage Thrust Vector Control system. In this paper, the design of the TVC analyses conducted during the Core Stage Green Run test series are discussed in detail. Throughout the course of the test activities, the SLS flight control team worked diligently with the Core Stage contractor to revise test command profiles and ensure sufficient instrumentation was available to collect data. Post-test analysis combined the Green Run modal, ambient, and hot fire test data, MSFC 2- axis Core Stage TVC Inertial Load Simulator (ILS) data, Hardware-In-the-Loop (HWIL) Systems Integration Lab (SIL) results, and actuator Acceptance Testing Procedure (ATP) responses. These data were used to characterize the response, validate critical math models of the TVC subsystem, and isolate the probable cause of the unexpected responses. Through comprehensive analysis of the available test data sources, the integrated team identified the dominant contributors to the observed response and developed test-correlated rationale for vehicle flight control system performance, ultimately leading to a confident posture for the Artemis I mission.

John H. Wall↗
NASA NTRS2024

Historical Aerospace Software Errors Categorized to Influence Fault Tolerance

Since the first use of computers in space and aircraft, software errors have occurred. These errors can manifest as loss-of-life or less catastrophically. As the demand for automation increases, software in mission or safety-critical systems should be designed to be tolerant to the most likely software faults. This paper categorizes a set of 55 historic aerospace software error incidents from 1962 to 2023 to determine trends of how and where automation is most likely to fail, behaving unexpectedly. A distinction between software producing unexpected (erroneous) output versus no output (failsilent) is introduced. Of the historical incidents analyzed, 85% were from software producing wrong output rather than simply stopping. Rebooting was found to be ineffective to clear erroneous behavior, and not reliable to recover from silent failures. Error origin was within the code/logic itself in 58% of cases, 16% from configurable data, 15% from unexpected sensor input, and 11% from command/operator input. A substantial forty percent (40%) of unexpected software behavior was indicated by the absence of code, arising from unanticipated situations and missing requirements, and 16% of incidents were subjectively deemed “unknown-unknowns”. No incidents were found to be the result of programming language, compiler, tool, or operating system; and only sixteen percent (16%) of all incidents were considered errors traditional computer science/programming in nature. These findings indicate that for fault tolerance, erroneous automation behavior must be a primary consideration especially at critical moments, and reboot recoverability may not be viable. Special care should be taken to validate configurable data and commands prior to use. “Test-like-you-fly”, including hardware-in-the-loop combined with robust off-nominal testing should be used to uncover missing logic arising from unanticipated situations not covered by requirements alone. This study uniquely focuses on manifestations of unexpected flight software behavior, independent of ultimate root cause. We characterize software error behavior and origin to improve software design, test, and operations for resilience to the most common manifestations, and provide a rich dataset for further study.

Aerospace↗
NASA NTRSDate not supplied

Airspace Performance Observations of Scalable Autonomous Operations in a High Density Vertiplex Simulation

The National Aeronautics and Space Administration’s (NASA’s) High Density Vertiplex (HDV) sub- project aims to develop and demonstrate progressive automation technologies that contribute to the Advanced Air Mobility (AAM) concept. Using Human-and-Hardware-In-The- Loop (HHITL) techniques, HDV demonstrates initial vertiport automation services at vertiports with increased air traffic volume in both simulated and live test environments. In 2023, the Scalable Autonomous Operations (SAO) simulation was conducted in which prototype vertiport, airspace, and ground control station technologies were assessed on technical performance. During the SAO simulation, an observational study captured an initial impression of the HDV airspace performance, potential disruptions to the airspace, and highlighted some capability and procedural gaps. Observations took place in two parts. In the first part, five scenario use cases (Nominal, Missed Approach, Speed Change, Divert, and Multi- Aircraft Divert) were conducted with three human operator roles (Vertiport Manager, Fleet Manager, and Ground Control Station Operator). Researchers collected metrics on throughput, closest point of approach, and airborne delay. In the second part of the study, the Missed Approach scenario was observed under three traffic density levels (20, 40, and 60 operations per hour) to challenge the automation to correctly identify slots in the vertiport arrival schedule. The results showed that the automation successfully found a slot for the Missed Approach vehicle in the 20 operations per hour condition, after some delay it found one in the 40 condition, and it did not find one in the 60 condition. The observations of technical and human performance throughout the five scenario use cases and the Missed Approach case study indicated that for HDV to increase traffic density and maintain or increase throughput, airspace monitoring services should be able to detect and resolve conflicts between aircraft. Furthermore, the roles and responsibilities of human operators need additional definition when it comes to responding to vehicle conflicts.

advanced air mobility↗
NASA NTRS2024

SUbsonic Single Aft eNgine (SUSAN) Power/Propulsion System Control Architecture Updates

The development of hybrid-electric propulsion technology for transport aircraft presents opportunities for new designs that can reduce fuel consumption and greenhouse gas emissions from commercial aviation and improve on safety and reliability when compared to modern aircraft. The SUbsonic Single Aft eNgine (SUSAN) Electrofan is a conceptual design for a transport aircraft with a series/parallel partial hybrid-electric propulsion system that is being developed by NASA as a reference design for a regional transport aircraft with a high degree of electrification. The SUSAN concept incorporates multiple tightly coupled power, propulsion, and flight control systems that introduce new challenges to the control design process, requiring a hierarchical and more coordinated control architecture. This paper summarizes updates to the SUSAN Power/Propulsion System (PPS) model and control architecture made in preparation for planned flight simulator and hardware-in-the-loop testing at NASA Glenn Research Center (GRC). These updates include a new electrical power system (EPS) model based on the NASA-developed Electrical Modeling and Thermal Analysis Toolbox (EMTAT), and a new PPS control architecture that improves the operational flexibility and responsiveness of the propulsion system. The performance of the upgraded control system is also demonstrated and discussed.

SUSAN↗
NASA NTRS2024

SUbsonic Single Aft eNgine (SUSAN) Power/Propulsion System Control Architecture Updates

The development of hybrid-electric propulsion technology for commercial transport aircraft presents opportunities for new designs that can reduce fuel consumption and greenhouse gas emissions and improve safety and reliability compared to modern aircraft. The SUbsonic Single Aft eNgine (SUSAN) Electrofan is a conceptual design for a transport aircraft with a series/parallel partial hybrid-electric propulsion system being developed by NASA as a reference design for a regional transport aircraft with a high degree of electrification. The SUSAN concept incorporates multiple tightly coupled power, propulsion, and flight control systems that introduce new challenges to the control design process, requiring a hierarchical and more coordinated control approach. This report summarizes updates to the SUSAN power/propulsion system (PPS) model and control architecture made in preparation for planned flight simulator and hardware-in-the-loop testing at the NASA Glenn Research Center. These updates include a new electrical power system model developed using the NASA-developed Electrical Modeling and Thermal Analysis Toolbox (EMTAT) and a new PPS control architecture that improves the operational flexibility and responsiveness of the propulsion system. The performance of the updated controller is demonstrated and discussed.

SUSAN↗
NASA NTRS2024

SUbsonic Single Aft eNgine (SUSAN) Power/Propulsion System Control Architecture Updates

The development of hybrid-electric propulsion technology for transport aircraft presents opportunities for new designs that can reduce fuel consumption and greenhouse gas emissions from commercial aviation and improve on safety and reliability when compared to modern aircraft. The SUbsonic Single Aft eNgine (SUSAN) Electrofan is a conceptual design for a transport aircraft with a series/parallel partial hybrid-electric propulsion system that is being developed by NASA as a reference design for a regional transport aircraft with a high degree of electrification. The SUSAN concept incorporates multiple tightly coupled power, propulsion, and flight control systems that introduce new challenges to the control design process, requiring a hierarchical and more coordinated control architecture. This paper summarizes updates to the SUSAN Power/Propulsion System (PPS) model and control architecture made in preparation for planned flight simulator and hardware-in-the-loop testing at NASA Glenn Research Center (GRC). These updates include a new electrical power system (EPS) model based on the NASA-developed Electrical Modeling and Thermal Analysis Toolbox (EMTAT), and a new PPS control architecture that improves the operational flexibility and responsiveness of the propulsion system. The performance of the upgraded control system is also demonstrated and discussed.

SUSAN↗
NASA NTRSDate not supplied

MAV Software Development: Streamlined Collaboration for MSR

This poster presents the coordinated efforts of the Mars Ascent Vehicle (MAV) software development teams, encompassing Flight Software, Ground Software, and Hardware-in-the-Loop Labs. This collaborative approach, which stems from the Artemis program, ensures efficiency in preparing for the Mars Sample Return (MSR) mission, focusing on the critical role of software integration for mission success.

MAV↗
NASA NTRSDate not supplied

Standalone Hazard Evaluation and Refinement From Instrument Findings (S.H.E.R.I.F.)

The Standalone Hazard Evaluation From Instrument Findings or SHERIF is a set of novel algorithms and associated framework designed to support the generation of Digital Elevation Maps (DEMs) from multiple LiDAR scans and perform Hazard Detection (HD) and Safe Site Selection with minimal input from other onboard systems. SHERIF can employ several techniques to perform Point Cloud registration(PCR) on disparate LiDAR scans of a planetary surface to generate a DEM which evolves over the course of a trajectory, with a flow designed to enable robustness. The framework also supports a variety of Hazard Detection and Safe Site Selection algorithms which can be run on the evolving DEM being produced. SHERIF features a robust and modular construction, allowing the user a high degree of flexibility in selecting and implementing whichever PCR and HD/SSL algorithms they may prefer, while maintaining the data products and sensor independence of the core SHERIF framework. SHERIF was recently tested in a hardware in the loop simulation at NASA JSC.

Entry Descent and Landing Guidance Navigation Cont↗
NASA NTRS2024

Autonomous Navigation of a Lunar Relay Using GNSS and Other Measurements

NASA’s Lunar Communications Relay and Navigation Services (LCRNS) project will establish a relay constellation at the Moon to provide the south pole region with communications and position, navigation, and time (PNT) services. These services will require highly accurate knowledge of position, velocity, and time (PVT) for each relay. This paper explores one approach for performing onboard PVT estimation, the LCRNS PNT Instrument (LPI). This paper considers different configurations of the instrument, specifically different measurement types and clocks, and the resulting navigation performance. Simulation results are first shown for an instrument configuration that uses GPS pseudorange and Doppler measurements with a highly sensitive GPS receiver (i.e., an acquisition and tracking threshold of 23 dB-Hz) and a chip-scale atomic clock (CSAC). The importance of Doppler, clock quality, receiver sensitivity, and optical navigation is examined through comparison of these results to other instrument configurations. Clock quality (i.e., stability) is the strongest determinant of achievable performance, and the inclusion of GPS Doppleralso has a significant effect. Finally, simulation results are compared to two laboratory tests: first a case that includes GPS receiver hardware in the loop, then a case that includes optical navigation and filter flight software in the loop. These agreeclosely with the simulation results and provide evidence the simulation is accurately modeling the instrument under development.

Benjamin W Ashman↗
NASA NTRSDate not supplied

Advancement of Deep Learning and Geometric Methods for Active Terrain Relative Navigation

To enhance NASA’s precision landing capabilities, in conjunction with the development of a novel active terrain relative navigation (ATRN) and terrain mapping system, denoted SHERIF, this work performed a comparative analysis between both deep-learning (DL) based and geometric approaches to hazard detection (HD) and safe-site-identification (SSI) through hardware-in-the loop testing on the Six degree-of-freedom Tendon Actuated Robot (STAR). The Standalone Hazard Evaluation and Refinement using Instrument Findings (SHERIF) system is capable of ingesting sensor data at an asynchronous rate, stitching successive terrain scans together to yield a high-resolution digital elevation map (DEM), performing absolute and relative localization using novel 3D feature extraction and matching methods, and HD/SSI activities. The DL-based HD/SSI algorithm provides a modular alternative to classical geometric approaches which have performance times that scale with map resolution. As the adoption of AI solutions become more prevalent for autonomous system decision making, it is prudent to explore the utility of such solutions in applications where they traditionally excel, such as image classification. Along with the development of a DL-based HD system, this work performed the first comparative analysis between DL and geometric approaches to HD/SSI using real sensor data from real-time testing in a relevant environment.

Davis Adams↗
NASA NTRSDate not supplied

A Discussion of Time Management Concepts and Time Constraint Equations for Multi-Rate Federation Executions

The High Level Architecture (HLA) is a simulation interoperability standard developed by the Simulation Interoperability Standards Organization (SISO) and published as the international standard IEEE 1516-2010 by the Institute for Electrical and Electronics Engineers (IEEE). HLA is a widely used standard for the development and execution of collaborative distributed simulations. HLA provides a number of Management Services to simulation developers: Federation, Declaration, Object, Ownership, Data Distribution, and Time. Of those services, Time Management Services is probably one of the least understood and least used. However, Time Management Services are critical to technical simulations like those created for space systems using the Space Reference Federation Object Model (SpaceFOM). Time Management can be used to insure data coherence and execution repeatability in distributed simulations. When combined with real time execution policies, Time Management is being used to support real time execution of mixed software and hardware in the loop integration, verification, and validation simulations for active space systems development. This paper starts by providing an overview of the HLA Time Management Services. This provides the background to discuss the challenges associated with Time Management and its use, starting with simple common rate frame scheduled simulations, then simple multi-rate simulations, and ending with complex mixed rate simulations. The authors then formulate the significant time constraint relationships between identified frame scheduling parameters. The intent of the paper is to provide a concise discussion of how to use Time Management in both simple cases and in more complex mixed frame rate federation executions.

Simulation Interoperability↗
NASA NTRSDate not supplied

Standalone Hazard Evaluation and Refinement From Instrument Findings (S.H.E.R.I.F.)

The Standalone Hazard Evaluation From Instrument Findings (SHERIF) system is a set of novel algorithms and associated framework designed to support the generation of Digital Elevation Maps (DEMs) from multiple LiDAR scans and perform Hazard Detection (HD) and Safe Site Identification (SSI) with no dependencies on other onboard systems. SHERIF can employ several techniques to perform robust 3D keypoint extraction and Point Cloud registration (PCR) on disparate LiDAR scans of a planetary surface to generate a DEM which evolves over the course of a trajectory. The framework also supports a variety of Hazard Detection and Safe Site Identification algorithms which can be applied to the evolving DEM being produced. SHERIF features a robust and modular construction, allowing the user a high degree of flexibility in selecting and implementing whichever keypoint identificaiton, PCR and HD/SSI algorithms they may prefer, while maintaining the data products and sensor independence of the core SHERIF framework. SHERIF was recently evaluated via simulation and hardware-in-the-loop experimental testing at NASA Johnson Space Center.

hazard detection↗
NASA NTRSDate not supplied

Design Considerations of an Ascent Abort Monitor Algorithm for Use During Service Module Aborts

In support of human rating the Artemis missions, NASA's Orion program requires continuous abort coverage from liftoff through mission destination. During a portion of the ascent trajectory, the currently achievable abort mode is determined by an Orion algorithm using the onboard navigated vehicle state. This ascent abort monitor determines achievability for Orion's Mode 2 abort, Untargeted Abort Splashdown (UAS), by propagating the current vehicle state through ascent abort events to determine sufficient timing to perform the abort and to a ballistic touchdown point to approximate landing location relative to prescribed keep out boundaries. The algorithm was updated for Artemis 2 to allow the capability to limit loads for the majority of ascent. Performance of the algorithm has been demonstrated and verified through dispersed trajectory analysis with emulated flight software, unit testing, and hardware in the loop testing.

Esteban Guzman↗
NASA NTRSDate not supplied

Standalone Hazard Evaluation and Refinement From Instrument Findings (S.H.E.R.I.F.)

The Standalone Hazard Evaluation From Instrument Findings (SHERIF) system is a set of novel algorithms and associated framework designed to support the generation of Digital Elevation Maps (DEMs) from multiple LiDAR scans and perform Hazard Detection (HD) and Safe Site Identification (SSI) with no dependencies on other onboard systems. SHERIF can employ several techniques to perform robust 3D keypoint extraction and Point Cloud registration (PCR) on disparate LiDAR scans of a planetary surface to generate a DEM which evolves over the course of a trajectory. The framework also supports a variety of Hazard Detection and Safe Site Identification algorithms which can be applied to the evolving DEM being produced. SHERIF features a robust and modular construction, allowing the user a high degree of flexibility in selecting and implementing whichever keypoint identificaiton, PCR and HD/SSI algorithms they may prefer, while maintaining the data products and sensor independence of the core SHERIF framework. SHERIF was recently evaluated via simulation and hardware-in-the-loop experimental testing at NASA Johnson Space Center.

hazard detection↗