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NASA NTRSDate not supplied

The Exploration Crew Health and Performance System of the Future - A Shared Mental Model

The challenges involved in vehicle/habitat design and operation for long-duration deep space missions are complex, and the needs of a human crew must be considered in the context of all the system trades required to enable such missions. An exploration Crew Health and Performance (CHP) system complements all the other vehicle/habitat systems to achieve one specific and critical endpoint – ensuring that the astronauts can perform the job that they were sent into space to do. This includes accounting for all aspects of the physiological, psychological, medical, and environmental realities that the human crew must face in deep space. Along with this system complexity comes the additional need for increased autonomy of the crew due to the immense distance from Earth. The design of future systems must respect the real human capabilities and limitations within the context of a mission to the Moon or Mars. The NASA Moon to Mars Objectives document outlines the need for advanced CHP system designs: • Develop systems that monitor and maintain crew health and performance throughout all mission phases, including during communication delays to Earth, and in an environment that does not allow emergency evacuation or terrestrial medical assistance. • Evaluate and validate progressively Earth-independent crew health & performance systems and operations with mission durations representative of Mars-class missions. • Validate readiness of systems and operations to support crew health and performance for the initial human Mars exploration campaign. This presentation will outline the initial work that Exploration Medical Capability is leading on the development of a prototype design for an exploration Crew Health and Performance (CHP) system for future NASA human deep space long-duration missions. This effort will require commitment from all stakeholders to a shared mental model of what a future exploration CHP system should look like, one that will address the needs, goals, and objectives of the Agency and help to ensure mission success in deep space.

K R Lehnhardt↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
NASA NTRS2024

An On-Board Off-Board Framework for Online Replanning: Applied to UAVs in Urban Environments

Autonomous systems are being used in a multitude of areas at an increasing rate and require a high level of adaptivity and intelligence to operate safely, especially under faulty conditions. This paper introduces a novel genetic algorithm tailored for UAV trajectory replanning, with an improved execution time via search space reduction based on the operating conditions of the UAV and its remaining mission. A unique characteristic of the replanning agent is its fast-start and adaptive properties, pre-seeding candidates with partial solutions and dynamically tuning elitism, crossover, and mutation rates in correspondence to the average fitness and diversity of the population. A population restart mechanism and early stopping mechanism are evaluated as well to assess their effect on solution quality and runtime. Previous work on genetic algorithms for UAV replanning were conducted with short trajectories in a small state space. Our UAV operates in a 56,000 square meter simulated urban environment, with static obstacles and a total of 53 possible waypoints. The agent increases the safety and reliability of UAV autonomy when operating under faulty conditions and when replanning is required.

Machine Learning↗
NASA NTRS2024

Core Body Temperature Predictions Using Metabolic Energy Expenditure and Heart Rate During Simulated Extravehicular Activity

Long duration spaceflight missions will require crew to become more autonomous in conducting extravehicular activities (EVA) without direct communication with Mission Control for biomedical support. To enable such autonomy, we are developing a Crew State and Risk Model (CSRM) as a collection of key physiology domains that drive EVA crew capabilities and workloads. One model component of CSRM is human thermal regulation. In this paper, customized development of a baseline model to predict core body temperature is presented using physiology inputs of heart rate and metabolic rate. The model development dataset included a baseline study where participants (n=6, equal male and female) performed a 5-hour EVA in a two-part session while wearing a hybrid space suit simulator (HS3). The first session included an end-to-end EVA traversing 1500 meters to a geology site and traversing back to a habitat conducting geology, payload relocation, and maintenance operations every 500 meters. The second session included standalone tasks of a 2000-meter traverse followed by geology tasks. Thermal measures of core body temperature, local skin temperature, liquid cooling garment temperature, heart rate, and metabolic rate were collected through the test duration. Multiple regression was used to build a linear equation to predict core body temperature from inputs of heart rate and metabolic rate. Heat storage was calculated via predicted core body temperature plus LCG and skin temperatures. The model was tested against a dataset from a pressurized suited test (n=6, equal male and female) in the NASA Active Response Gravity Offload System (ARGOS) conducting similar end-to-end EVA and standalone tasks. Predicted error of the model against the raw test cases was 0.2±0.15 °C. The baseline prediction of core body temperature using heart rates and metabolic rates allows for simple real-time tracking from data collected in-flight to monitor crew consumables and thermal flight limits during EVA.

Bradley Hoffmann↗
NASA NTRSDate not supplied

An Experimental System for Strategic Flight Path Management in Advanced Air Mobility

In the concept envisioned for Urban Air Mobility (UAM) operations, fleets of electric vertical takeoff and landing (eVTOL) vehicles would operate between vertiports distributed within a densely populated area. These operations would be largely independent from the existing air traffic control system and would place the responsibility for flight planning and aircraft separation on fleet operators. The fourth major level on the UAM Maturity Level scale, UML-4, relies on “collaborative and responsible” automation to enable operations in non-visual conditions with medium traffic density (hundreds of aircraft in one metropolitan region) and medium complexity. This level of service places many requirements on automation systems to assist the operators of these aircraft. NASA has developed the Autonomous Operations Planner (AOP), a reference prototype Flight Path Management automation system, and has modified AOP to support research of anticipated UML-4 operations. AOP creates a four-dimensional flight plan conforming to the constraints of these operations, evaluates and modifies the flight plan during flight as conditions and constraints evolve, and coordinates the flight plan with other airspace users and with service providers. This version of AOP has been integrated into the Sikorsky Autonomy Research Aircraft and used in a flight test activity. In this paper we discuss anticipated characteristics of UAM operations, modifications that were made to AOP to adapt to that environment or to support the flight test, and observations of software and aircraft performance during the flight test. The aircraft achieved four-dimensional conformance with the flight plan and AOP provided adequate planning in almost all cases. We discuss improvements that could be made to AOP to address deficiencies that were observed.

Autonomous Operations Planner↗
NASA NTRSDate not supplied

Interactive Mars: A Direct-Experience Mission

As NASA prepares to explore Mars, we describe an innovative architecture that could be based on public-private partnerships to offset the costs of such a mission, while demonstrating many of the key technologies—propulsion, communications, autonomy, and landing systems—that will be needed for future human exploration. We propose that NASA could use this capability to deliver dozens of small rovers to several sites on Mars to enable the creation of interactive games that would be operated commercially and sponsored privately. NASA would get a reduced cost to demonstrate a portion of the infrastructure needed for human exploration, and the private sector would get access to a new market and sponsorship opportunities for a broadly popular activity. In addition, our proposed architecture could enable a new, intuitive means for scientists to interpret data and conduct ground operations. This proposal is to broadly develop the overall concept, while focusing on the design of the novel communications system, developing requirements for the software architecture needed to control the rovers and implement gaming, and developing gaming concepts.

Carolyn R. Mercer↗
NASA NTRSDate not supplied

Development and Field Test Results of Distributed Ground Sensor Fusion Based Object Tracking

Autonomous operations are a crucial aspect in the context of Advanced Air Mobility and other emerging aviation markets. In order to enable this autonomy, an accurate and detailed understanding of the positions of the various vehicles in the air is necessary. Full localization independent of on-board sensors makes the system suitable for noncooperative vehicles. This paper focuses on the object tracking part that relies on distributed ground-based RF and other sensor fusion, considering specific properties and limitations of different sensor types. Results show satisfactory performance in nominal scenarios with full coverage for some sensor types, but RF signals are challenging because of their nature. This paper includes the results from simulations as well as field tests to support the observations and conclusions.

sensor fusion↗
NASA NTRSDate not supplied

An Experimental System for Strategic Flight Path Management in Advanced Air Mobility

In the concept envisioned for Urban Air Mobility (UAM) operations, fleets of electric vertical takeoff and landing (eVTOL) vehicles would operate between vertiports distributed within a densely populated area. These operations would be largely independent from the existing air traffic control system and would place the responsibility for flight planning and aircraft separation on fleet operators. The fourth major level on the UAM Maturity Level scale, UML-4, relies on “collaborative and responsible” automation to enable operations in non-visual conditions with medium traffic density (hundreds of aircraft in one metropolitan region) and medium complexity. This level of service places many requirements on automation systems to assist the operators of these aircraft. NASA has developed the Autonomous Operations Planner (AOP), a reference prototype Flight Path Management automation system, and has modified AOP to support research of anticipated UML-4 operations. AOP creates a four-dimensional flight plan conforming to the constraints of these operations, evaluates and modifies the flight plan during flight as conditions and constraints evolve, and coordinates the flight plan with other airspace users and with service providers. This version of AOP has been integrated into the Sikorsky Autonomy Research Aircraft and used in a flight test activity. In this paper we discuss anticipated characteristics of UAM operations, modifications that were made to AOP to adapt to that environment or to support the flight test, and observations of software and aircraft performance during the flight test. The aircraft achieved four-dimensional conformance with the flight plan and AOP provided adequate planning in almost all cases. We discuss improvements that could be made to AOP to address deficiencies that were observed.

Autonomous Operations Planner↗
NASA NTRSDate not supplied

Future Homes in Space: Development of Concepts for Exploration Space Habitats

NASA’s Artemis campaign seeks to return humans to the moon and establish a sustained presence on the lunar surface. This session will emphasize how habitation capabilities on the moon and in cislunar space can potentially contribute to the sustainability objectives of Artemis. Habitable elements represent opportunities to enable longer duration stays, increase the number of crew members present, enhance science and utilization activities, drive technology development for future Mars exploration, perform analog missions, and fuel economic opportunities for US industry. Panelists include Paul Kessler (NASA Marshall Space Flight Center, deputy lead for lunar surface habitation); Andrew Choate (NASA Marshall Space Flight Center, Mars habitation lead); Krystofer Dudzinski (NASA Marshall Space Flight Center, a space architect within the MSFC Advanced Concepts Office); and Larry Toups (retired from NASA Johnson Space Center, currently an adjunct professor at University of Houston in space architecture). The panel is moderated by Tracie Prater (NASA Marshall Space Flight Center, Habitation Systems Development Office). The panel will begin with an overview of the history of habitation concepts and an academic perspective on general considerations in space habitat design (Larry Toups). Paul Kessler and Andrew Choate will introduce NASA’s principle of “architecting from the right” to help define objectives for Artemis missions, needs/characteristics, use cases, and functions (as published in the agency’s Architecture Definition Document) and provide perspective on how this principle informs habitation concept development work. NASA panelists will discuss key engineering challenges identified for developing, deploying, and operating habitable assets on the lunar surface and/or in deep space. These may include dust mitigation, outfitting of inflatable softgoods (for concepts which may use softgoods as a primary structural material), survival in lunar darkness, human health and performance considerations, maintenance/repair/sparing, and autonomy. These identified challenges represent risks for habitation systems development and relate closely to capability gaps identified by the agency. While the work of NASA Marshall Space Flight Center’s habitation development office is primarily focused on habitats which are launched from earth pre-integrated (referred to as Class I in the framework previously developed by NASA space architects Kennedy/Cohen) or launched from earth and deployed at the point of use (Class II), there is also extensive work in NASA, academia, and companies on constructed habitats, which would be built on a planetary surface using indigenous resources (Class III habitats). Panelist Krystopher Dudzinski will discuss potential evolutionary pathways from Class I and Class II habitats to Class III habitats, unique and common architectural challenges within each habitat class, and key gaps in implementing Class III habitats from an architectural perspective. NASA panelists and the moderator will also provide an overview of partnership opportunities and avenues for further engagement to advance habitation systems for the SpaceCom audience. NASA is currently developing notional concepts for a lunar surface habitat and Mars transit habitat, which will be discussed during this panel session and used as examples. These concepts represent options for habitation system design and are a point of departure. They do not represent a final plan or formal recommendation on the part of the agency. Based on the most recent analysis cycle, NASA’s lunar surface habitat (SH) concept nominally supports two crew members for 30 days, with the capacity to support four crew during a surge period where crew will swap between the SH and another surface asset, such as a pressurized rover. This example design has a metallic airlock for ingress/egress and the upper portion is an inflatable material system which serves as the habitation module. The notional interior of the habitat is a three-deck layout/configuration which supports all crew mission functions, including exercise, stowage, extravehicular activity (EVA), sleep, hygiene waste collection, maintenance and repair, and meal preparation. Under analysis assumptions for habitation, the Mars Transit Habitat (TH) concept would support four crew on an up to 1,200 day Mars mission. One option for the concept is to initially dock Transit Habitat at Gateway, where it can be used to increase the duration of crew stays in cislunar space and perform shakedown and analog missions prior to a Mars departure. One challenge in longer duration missions which involve both surface exploration and transit is understanding crew adaptation when transitioning between partial gravity and microgravity environments. TH at Gateway offers an opportunity to study this transition and in doing so reduce risks associated with future Mars exploration. Like lunar SH, the most recent analysis cycle concept of a Mars TH is a hybrid structure design, with a metallic section supporting EVAs, axial/radial docking, and Safe Haven capabilities, and an inflatable softgoods structure for the primary habitation function. Interior layouts to optimize crew usability and livability are currently under trade. The panel will include presentation material, but also seeks to engage the audience in a highly interactive conversation regarding the potential role for habitation in future exploration initiatives. Potential topics for discussion include the influence of the crew experience on habitation systems design and livability/usability considerations, the benefits of space habitation development in terrestrial applications, and challenges and opportunities in “feeding forward” lunar surface habitation systems development to Mars exploration.

space habitats↗
NASA NTRSDate not supplied

Precision in Assembled Discrete Lattice Space Structures for Next-Generation ISAM Applications

Robust autonomous robotic assembly of large-scale space structures has been a long-term and challenging goal to enable higher quality in-space communication and science instrumentation. For optical observatory support structures and antenna structures, the challenge is the strict dimensional precision requirements (generally RMS surface error) driven by the operational radiation wavelength. Early theoretical work in the area linked RMS surface error of a reflector support truss plate to the natural passive dynamic mode frequencies of the structure, as well as to the error distribution in dimensions of constituent structural elements. Prior robotic truss assembly demonstrations focused on designing ultra-high precision structural elements, joints, and robotic actuators. Recently, an alternative space structure assembly strategy based on a programmable matter approach (NASA ARMADAS) was demonstrated. This approach uses lattice building blocks (voxels) that are reversibly, mechanically joined into a bulk lattice structure by robots that locomote in and on the structure itself. Such a system can achieve high-level autonomy with low computation, robustly assemble utilizing inexpensive and imprecise robots, and efficiently build structures several orders of magnitude larger than the assembly robots. However, for instrumentation support structure applications, the resulting precision of these building block-based lattice structures is not well studied. Since they are demonstrated with many more assembly units than prior art trusses, it is unclear whether the same precision design approaches apply. In this work, we study in simulation the effects of voxel geometry, error in distribution, assembly resolution (module size), and assembly geometry on the error of both beam and plate lattice structures. While average RMS error of a plate increases with plate size, increasing plate thickness quickly collapses RMS error towards a limit that is on the order of the error of the constituent parts. We validate our models against previously published precision measurements of built systems. Results from this study will guide manufacturing precision requirements, as well as designs, for future robotically assembled structural applications and establish feasibility for different applications.

Christine E Gregg↗
NASA NTRSDate not supplied

Autonomous Navigation, Guidance, and Control Software in a Low SWaP Box

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 autoNGC flight software is built on the plug-and-play architecture of the core Flight System (cFS) consisting of the standard cFS apps and newly developed autoNGC interface apps and libraries. The various apps cooperate through communication over the message-based software bus. With the plug-and-play architecture of autoNGC, cFS apps can easily be added and replaced to meet the needs of different missions, even after launch. The first flight software release of autoNGC is targeted for Summer 2024 to provide autonomous navigation at the Moon and beyond. It can perform sensor fusion of multiple measurement types including pseudo-range from a Global Navigation Satellite System (GNSS) receiver (including weak signal), 1-way and 2-way range and Doppler from ground stations (i.e., direct to Earth (DTE)), bearing and range from optical camera images, and accelerometer data. Accurate onboard navigation and timing is obtained through the Goddard Enhanced Onboard Navigation System (GEONS) software library which fuses different measurement types through an extended Kalman filter (EKF) framework. Optical measurements that are ingested in GEONS are first extracted from optical images by the cFS Goddard Image Analysis and Navigation Tool (cGIANT) app. If the imaged body is far enough away that it appears as a pixel or cluster of pixels, then bearing angles to the body centroid can be provided. If the body is close enough and the shape is known coarsely, then bearing angles and range to the body centroid can be derived from the limb. Bearing angles to individual surface features can also be extracted (i.e., terrain relative navigation (TRN)). Onboard guidance and control capabilities are being developed for a future release to perform autonomous station-keeping and trajectory correction maneuvers in multiple orbital regimes. Capabilities to enable distributed systems missions and constellations, such as crosslink measurements, and onboard time management are being developed as well. The first hardware implementation of autoNGC is a minimal size, weight, and power (SWaP) design allowing for inclusion into CubeSats and SmallSat-size buses. Advancements in miniaturized space processors, such as the SpaceCube 3.0 Mini and the SpaceCube Mini-Z are utilized for low SWaP while maintaining a high level of performance. The current enclosure design is 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. In order to accommodate a wide range of missions, the hardware interfaces are designed for flexibility with a variety of sensor inputs. Through comprehensive testing in the software-in-the-loop, processor-in-the-loop, and hardware-in-the-loop test beds that are concurrently being developed, autoNGC is expected to achieve TRL 6 by late 2024.

Sun Hur-Diaz↗
NASA NTRSDate not supplied

A Multi-Faceted Approach to Demonstrating Multi-Functional Integrated Medical Devices to Advance Earth-Independent Medical Operations

INTRODUCTION TO MIM DEVICES Multi-functional Integrated Medical (MIM) devices conveniently incorporate multiple medical system capabilities within one device. The NASA Exploration Medical Integrated Product Team (XM-IPT) sponsored a market survey and trade study which identified the Tempus ProTM and the LifeBot 10® as the MIM devices that best met the evaluation criteria of the trade study. The Tempus ProTM (Remote Diagnostic Technologies, Ltd., Philips Corp., Farnborough, UK) and LifeBot 10® (LifeBot Health, Chicago, IL) both provide vital sign measurements such as blood pressure, electrocardiograms, heart rate, end tidal CO2, respiration rate, pulse oximetry and temperature along with ultrasound imaging. A video laryngoscopy capability is unique to the Tempus ProTM, while the LifeBot 10® supports connectivity with a digital stethoscope, otoscope, eye exam camera, and dermatoscope. Both devices include procedural guidance capabilities and have various data transmission and report generation features. TECHNOLOGY DEMONSTRATIONS NASA’s exploration-class missions will have severe resource constraints, long return trip durations, significant communication delays, and limited resupply opportunities. The medical systems of these missions will need to fit within an Earth-Independent Medical Operations (EIMO) construct. Key features of an EIMO medical system include: 1) technologies that support the prevention, diagnosis, and treatment of spaceflight medical events; 2) components that meet mass, volume, power and crew time/training constraints; 3) consideration of the medical skill level of the astronaut caregiver; 4) collection, storage and analysis of medical data within a central data architecture; and 5) incorporation of appropriate guidance and support tools that allow crew autonomy. Evaluations are underway to determine if it will be beneficial to include MIM devices within exploration medical systems by conducting a series of planned technical demonstrations. Exploration Atmosphere Chamber studies are being performed to determine MIM functionality in a high oxygen concentration atmosphere. A side-by-side comparison of the Tempus ProTM and LifeBot 10® will be performed during ground-based demonstrations. Use of the MIM devices within an EIMO medical scenario simulation will be practiced during ground-based demonstrations in preparation for International Space Station (ISS) demonstrations of the MIM device. These various demonstrations are designed to gather evidence for or against the inclusion of MIM devices within an EIMO medical system. EXPECTED DEMONSTRATION OUTCOMES Information will be gained about the feasibility, benefits, and challenges of using a multifunctional, all-in-one, medical device for medical diagnosis. Information will also be collected about performance differences as available ground support decreases. Gaining this understanding will allow for further development of exploration medical system capabilities, which take the EIMO construct into consideration.

B. E. Lewandowski↗
NASA NTRS2024

Integration of Automated Systems Test Campaign NC-IAS

NASA’s 2022 strategic plan included the goal to catalyze economic growth and drive innovation in the aviation industry, and to address challenges in air transportation and airspace management within the National Airspace System (NAS). The Aeronautics Research Mission Directorate (ARMD) specifically was tasked with leading aviation innovation to enable safe and sustainable air transportation through revolutionary vehicle advances and efficient flight operations. As a part of meeting these objectives and advancing air mobility concepts, NASA initiated the National Campaign (NC) in the Advanced Air Mobility (AAM) Project within the Airspace Operations and Safety Program (AOSP). The NC was designed to support operational demonstrations with industry as well as the research and development needed to support NASA-led research flight demonstrations. Within NC, the Integration of Automated Systems (IAS), an NC activity, tested and evaluated flight deck automation and airspace operations management functions needed to enable Urban Air Mobility (UAM) operations. This was accomplished through a partnership with Sikorsky Aircraft (specifically Sikorsky Innovations), a Lockheed Martin company, and DARPA (Defense Advanced Research Projects Agency), by leveraging two automation-enabled helicopters equipped with unique capabilities that enabled NASA to develop and test two-ship conflict encounters to demonstrate flight path management and hazard avoidance technologies. The enabler in this testing was NASA-developed “Middleware” (MW) software (also known as Expandable Variable Autonomy Architecture, or EVAA), which among other things allowed multiple algorithms to be incorporated into one software build that was hosted on the dissimilar-type Sikorsky helicopters. The IAS test campaign period of performance was from March 2022 through October 2023 and was structured as a phased, or spiral, approach that ultimately led to the first-ever demonstration of two-ship UAM/AAM operations designed to safely choreograph specific conflict encounters and mission scenarios to test the research algorithms for strategic and tactical aircraft deconfliction. Lessons learned are included in the body of the report. Data collected will be used to inform FAA and industry standards groups on the increasingly automated systems needed for future AAM operations. The test encounters developed for these flight tests were proven to be highly predictable, repeatable, and safely exercised flight path planning and Detect and Avoid (DAA) algorithms. These same test encounters should be leveraged by future flight test campaigns to verify that operational safety is not compromised as the AAM architecture matures. Next steps include repeating similar encounters using unmanned aircraft carrying DAA sensors in the National Airspace.

AAM↗
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 NTRS2024

Looking to the Future: A Call to Action for Advanced GNC Algorithm Verification and Validation

Future space systems will rely on autonomous Guidance, Navigation, and Control (GNC) functions to efficiently manage safe and precise self-directed operations in uncertain complex environments. Fundamentally, the GNC system plays a key role in mission performance and safety because it computes the ideal trajectory (Guidance), determines the actual trajectory (Navigation), and executes the ideal trajectory (Control) of a vehicle’s position and attitude. Our current GNC systems are highly automated and already have a high degree of complexity. As missions become more ambitious, GNC systems for launch vehicles and space platforms (e.g., spacecraft, probes, and landers) will require higher levels of performance and autonomous operation than previously encountered, for example, this includes GNC for optimizing aerodynamic and/or propulsion performance during planetary entry. This GNC Verification and Validation (V&V) paper highlights concerns with what undoubtedly will be a trend towards increased complexity as fully autonomous GNC systems are developed for future space missions. Clearly, complex GNC systems pose challenges in the prelaunch V&V phase, which is a relatively expensive part of a mission’s life cycle. Essentially the V&V phase is focused on checking that the system effectively meets all the design and operational requirements for the mission. The authors of this paper (i.e., the Inter-Agency Working Group of GNC subject matter experts) focused on this fundamental question over the past few years: Will the GNC engineering community of practice be sufficiently prepared to perform the necessary V&V on evolving GNC architectures that are driven by very demanding requirements for autonomy, resiliency, reconfigurability, adaptability, and mission cost-benefit balance? It is the viewpoint of our Inter-Agency team that the GNC V&V approaches and processes needed to address the next generation of complex GNC systems, which likely will employ various forms of modern GNC technology, are not currently established to the level the community will need in the future. While researchers and practitioners have made some progress in developing new GNC V&V methods for modern GNC systems, a good deal of work remains to be done to codify such methods in a comprehensive and systematic manner. Thus, the Inter-Agency team’s partner organizations [the National Aeronautics and Space Administration (NASA), the European Space Agency (ESA), the National Centre for Space Studies (CNES), the German Aerospace Center (DLR), the French Aerospace Lab (ONERA), and ISAE-SUPAERO] have conducted preliminary investigations into advancing GNC V&V techniques, which resulted in the identification of the need for education, new V&V tools, and benchmark problems for the GNC community. The necessary proactive steps to be taken to meet the challenges and fill the gaps in GNC V&V are summarized in this paper. The first steps include identifying advanced analysis tools, developing a GNC V&V roadmap, and expanding education and training programs for GNC practitioners. This paper is a call to action and proposes a comprehensive set of recommended actions for all our stakeholders: space agencies, researchers, and industry.

Samir Bennani↗
NASA NTRSDate not supplied

Stochastic Verification by Analysis for Autonomous Systems Management Architecture (ASMA)

The Gateway Vehicle Systems Manager (VSM) is the top-level of a distributed, hierarchical software control system. VSM is data-driven and will make decisions related to mission, fault, resource management and vehicle control. These attributes combined with a high degree of autonomy make it susceptible to emergent behavior. In order to achieve the high level of confidence needed in this critical system, the VSM team has developed a multifaceted verification strategy employing traditional verification techniques, simulation, model checking, and runtime verification. Individual algorithms are verified using conventional testing and model checking using assume-guarantee contracts. A discrete event-based simulation approach is being developed to verify timelines. This presentation describes an enhancement to the verification approach using analysis to enhance system robustness by detecting and resolving the potential for emergent behavior. The verification by analysis employs a Software in the Loop (SITL) environment with real flight software executing on emulated processors, simulations of vehicle subsystems, flight dynamics, and human inputs. Since the possible input space and configuration data set are too large for exhaustive testing, a Monte Carlo approach is used to cover feasible scenarios, augmented with corner cases and known higher-risk scenarios. A key problem in using Monte Carlo-based system verification is evaluating test results to ensure that system behavior is correct. The presentation describes the approach the VSM team uses to monitor behavior for compliance with predetermined boundaries and to identify anomalous behavior for further analysis. This presentation describes the multi-level systems approach to verification, and the simulation-based layer that covers the feasible state space: 1. Overview of the Gateway VSM 2. Special challenges due to heterogeneous, hierarchical architecture 3. Modeling and simulation environment using flight software and system simulations 4. Developing input sets to ensure state-space coverage 5. Developing model and data configuration sets to ensure model coverage 6. Interpreting results without predetermined outcomes 7. Lessons learned and future work

Verification and Validation↗
NASA NTRSDate not supplied

Overview of the Subscale RAVEN Flight Controls and Modeling Testbed

The Research Aircraft for eVTOL Enabling TechNologies (RAVEN) Subscale Wind-Tunnel and Flight Test (SWFT) model is a subscale aircraft built for flight dynamics and controls research demonstrated in wind-tunnel and flight-test experiments. The intent of this paper is to provide a summary of past, current, and future efforts being pursued by the RAVEN-SWFT project. Initially, vehicle development guidelines were crafted by a multidisciplinary team to ensure that the RAVEN-SWFT vehicle was well suited for research in multiple areas, including aero-propulsive modeling, flight controls, and autonomy, among others. The vehicle has been used to obtain extensive wind-tunnel data, enabling aero-propulsive model development across the transition flight envelope and validation of computational tools. The vehicle will be used to conduct flight testing in order to evaluate modeling strategies and flight control logic. The RAVEN-SWFT model also serves as a risk reduction activity for a conceptual, full-scale vehicle in the 1000-lb class. The next steps in the project are to successfully demonstrate free flight in hover, transition, forward flight, and the reverse thereof, utilizing custom control laws integrated onto the RAVEN-SWFT avionics hardware. The project intends to publicize all of the geometry, data, and methods in future reports.

eVTOL↗
NASA NTRS2024

Discrete Event Simulation-Based Timeline Validation Using R2U2

The Gateway Vehicle Systems Manager (VSM), the top-level software control system in a distributed, hierarchical Autonomous System Management Architecture is, like most modern spacecraft software control systems, heavily data-driven. For example, schedules (timelines) will be developed on the ground and, due to the high degree of autonomy, contain complex procedures involving conditional branching, variable timing, and resource contention resolution. In order to verify that an uploaded timeline will function correctly, it is necessary to explore the feasible set of possible executions. While it is possible to test a timeline using a mission simulation, the complexity of the system and duration of a timeline limits the number of trials and therefore the test coverage. To address this problem, the VSM team is using a discrete event system model that can rapidly generate from a timeline sets of event sequences using Monte Carlo techniques. To achieve rapid and trustworthy checking of the event sequences, we use an offline version of the runtime model checking tool R2U2. This presentation describes the approach the VSM team is using to implement the discrete event simulation and evaluate event sequences using R2U2. The presentation will discuss: 1. Description of the timelines by VSM in the context of VSM operations 2. Expansion of a timeline into a sequence of atomic events 3. Adjustment, in the Monte Carlo environment, of an event sequence to account for uncertainty, external events, and failures 4. Definition of R2U2 input and mission-time linear temporal logic files 5. Generation and use of R2U2 verdict sequences 6. Lessons learned and future work

Verification↗