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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Space Launch System Liftoff and Separation Dynamics Analysis Tool Chain

A flexible, hierarchical tool chain that is being applied to NASA’s Space Launch System (SLS) for critical dynamics phenomena is described. This tool chain, called CLVTOPS, is used to investigate lateral liftoff movement of the vehicle as it departs and clears the mobile launch tower and separation of the two solid rocket boosters without collision with the core stage and payload. The toolset’s architecture was configured to take advantage of a modern software-engineering approach for maximum flexibility and utilization of open-source simulations and associated tools. As opposed to a “monolithic” approach, scripting languages were used to “bind” together a tool chain to configure and organize input data, execute and produce analysis results, and post-process these results to facilitate a rapid iterative analysis process to quickly address issues and pursue alternatives with emphasis on analysis automation. Key capabilities in the tool chain include processing and mining of very large data sets, a wide range of graphical depictions, and high-fidelity, physics-based simulations. The paper begins with a problem description and the motivation for liftoff and separation dynamics analysis followed by a historical survey of dynamics analyses for previous NASA human-rated launch vehicles. Details of the tool chain and its components are then introduced divided, first, into description of the scripting language architecture used to “bind” the simulation tools, programs, and scripts together and, second, the physics models and simulations. Representative analyses and data products are shown for liftoff and booster separation dynamics that provide in-depth insight to the tool chain’s capabilities. Supporting activities such as simulation tool chain verification, version archiving and data management, and training are addressed. The paper concludes with case examples on how the tool chain can be tailored to related aerospace dynamics analyses, both large and small. These patterns and techniques for SLS dynamics tool construction can be applied for other aerospace simulations.

6DOF↗
NASA NTRSDate not supplied

Space Launch System Liftoff and Separation Dynamics Analysis Tool Chain

A flexible, hierarchical tool chain that is being applied to NASA’s Space Launch System (SLS) for critical dynamics phenomena is described. This tool chain, called CLVTOPS, is used to investigate lateral liftoff movement of the vehicle as it departs and clears the mobile launch tower and separation of the two solid rocket boosters without collision with the core stage and payload. The toolset’s architecture was configured to take advantage of a modern software engineering approach for maximum flexibility and utilization of open-source simulations and associated tools. As opposed to a “monolithic” approach, scripting languages were used to “bind” together a tool chain to configure and organize input data, execute and produce analysis results, and post-process these results to facilitate a rapid, iterative analysis process to quickly address issues and pursue alternatives with emphasis on analysis automation. Key capabilities in the tool chain include processing and mining of very large data sets, a wide range of graphical depictions, and high-fidelity, physics-based simulations. The paper begins with a problem description and the motivation for liftoff and separation dynamics analysis followed by a historical survey of dynamics analyses for previous NASA human-rated launch vehicles. Details of the tool chain and its components are then introduced and divided, first, into description of the scripting language architecture used to “bind” the simulation tools, programs, and scripts together and, second, the physics models and simulations. Representative analyses and data products for liftoff and booster separation dynamics are shown in order to provide in-depth insight into the tool chain’s capabilities. Supporting activities such as simulation tool chain verification, version archiving and data management, and training are addressed. The paper concludes with case examples on how the tool chain can be tailored to related aerospace dynamics analyses, both large and small. The flexibility and versatility of this tool chain in supporting analyses of such a diverse range of aerospace applications demonstrates the feasibility of applying these patterns and techniques for tool construction to other aerospace simulations.

6DOF↗
NASA NTRSDate not supplied

NEUROSPF: A Tool For the Symbolic Analysis of Neural Networks

This paper presents NEUROSPF, a tool for the symbolic analysis of neural networks. Given a trained neural network model, the tool extracts the architecture and model parameters and translates them into a Java representation that is amenable for analysis using the Symbolic PathFinder symbolic execution tool. Notably, NEUROSPF encodes specialized peer classes for parsing the model’s parameters, thereby enabling efficient analysis. With NEUROSPF the user has the flexibility to specify either the inputs or the network internal parameters as symbolic, promoting the application of program analysis and testing approaches from software engineering to the field of machine learning. For instance, NEUROSPF can be used for coverage-based testing and test generation, finding adversarial examples and also constraint-based repair of neural networks, thus improving the reliability of neural networks and of the applications that use them.

neural networks↗
NASA NTRS2006

Advances in distributed operations and mission activity planning for Mars surface exploration

A centralized mission activity planning system for any long-term mission, such as the Mars Exploration Rover Mission (MER), is completely infeasible due to budget and geographic constraints. A distributed operations system is key to addressing these constraints; therefore, future system and software engineers must focus on the problem of how to provide a secure, reliable, and distributed mission activity planning system. We will explain how Maestro, the next generation mission activity planning system, with its heavy emphasis on portability and distributed operations has been able to meet these design challenges. MER has been an excellent proving ground for Maestro's new approach to distributed operations. The backend that has been developed for Maestro could benefit many future missions by reducing the cost of centralized operations system architecture.

Shams, Khawaja↗
NASA NTRS2018

Towards a Unified View of Modeling and Programming (ISoLA 2018 Track Introduction)

The article provides an introduction to the track: Towards a Unified View of Modeling and Programming, organized by the authors of this paper as part of ISoLA 2018: the 8th International Symposium On Leveraging Applications of Formal Methods, Verification and Validation. A total of 19 researchers were invited to present their views on the two questions: what are the commonalities between modeling and programming languages, and should we strive towards a unified view of modeling and programming? The idea behind the track, which is a continuation of a similar track at ISoLA 2016, emerged as a result of experiences gathered in the three fields: formal methods, model-based software engineering, and programming languages, and from the observation that these technologies share a large common part, to the extent where one may ask, does the following equation hold: modeling = programming?

Steffen, Bernhard↗
NASA NTRS2018

Verifying Interfaces and Generating Interface Control Documents for the Alignment and Phasing Subsystem of the Thirty Meter Telescope from a System Model in SysML

This paper presents a novel method for verifying interfaces and generating interface control documents (ICDs) from a system model in SysMLTM. In systems and software engineering, ICDs are key artifacts that specify the interface(s) to a system or subsystem, and are used to control the documentation of these interfaces. ICDs enable independent teams to develop connecting systems that use the specified interfaces. In the context of the Thirty Meter Telescope (TMT), interface control documents also act as contracts for delivered subsystems. The Alignment and Phasing system (APS) is one such subsystem. APS is required to implement a particular interface, and formulates requirements for the interfaces to be provided by other components of TMT that interface with APS. As the design of APS matures, these interfaces are frequently refined, making it necessary for related ICDs to be updated. In current systems engineering practice, ICDs are maintained manually. This manual maintenance can lead to a loss in integrity and accuracy of the documents over time, resulting in the documents no longer reflecting the actual state of the interfaces of a system. We show how a system model in SysMLTM can be used to generate ICDs automatically. The method is demonstrated through application to interface control documents pertaining to APS. Specifically, we apply the method to the interface of APS to the primary mirror control system (M1CS) and of APS to the Telescope Control System (TCS). We evaluate the newly introduced method through application to two case studies.

Troy, Mitchell↗
NASA NTRSDate not supplied

Space Robot Operating System (Space ROS)

Space Robot Operating System (Space ROS) is an open-source software framework for flight-quality robotic and autonomous space systems. It combines the benefits of open-source software and flight software best-practices into a single framework. Space ROS is predicated on ROS2 and is therefore reusable, modular and adoptable. As an open-source framework, it is accessible to the global space community. Space ROS is intended to be compliant with NASA flight software engineering practices in order to facilitate meeting mission and safety standards. Space ROS has fault management and real-time capabilities at its core.

Will Chambers↗
NASA NTRS2021

Southwest Water Resources: Monitoring Surface Water Extents of Remote Stock Ponds in the Southwestern United States Using Earth Observing Systems for Enhanced Water Resources Management

Due to increasingly frequent and severe drought conditions in the southwestern US, land managers and livestock producers need to monitor stock ponds with increasing regularity. The ability to assess stock pond water levels with Earth observing satellite systems would enhance monitoring efforts of partners at the US Forest Service, Arizona Department of Game and Fish, and the Diablo Trust. This study employed Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), and Sentinel-2 Multispectral Instrument (MSI) to monitor surface water extent for hundreds of critical stock ponds in Arizona. Using methods adapted from previously developed image processing workflows, this project conducted a time-series analysis to capture seasonal and interannual variations in surface water area between 2013 to 2021. In addition, end users can monitor the surface water extent of stock ponds through the developed Google Earth Engine software tool called Surface Water Identification and Forecasting Tool (SWIFT). SWIFT incorporates the Automated Water Extraction Index, Modified Normalized Difference Water Index, and Tasseled Cap-Wetness Index for optical imagery and the incidence angle, VV and VH polarization bands for Sentinel-1 imagery to detect small water bodies in the study area with an overall accuracy range of 88-93%. These tools will empower our partners to monitor the extents of water in their stock ponds remotely, enabling them to develop data-informed and sustainable management solutions for decades to come.

Rainey Aberle↗
NASA NTRSDate not supplied

Understanding and Verifying Neural Networks

Deep Neural Networks (DNNs) have gained immense popularity in recent times and have widespread use in applications such as image classification, sentiment analysis, speech recognition and also in safety-critical applications such as autonomous driving. However, they suffer limitations such as lack of explainability and robustness which raise safety and security concerns in their usage. Further, the complex structure and large input spaces of DNNs act as an impediment to thorough verification and testing. The SafeDNN project at the Robust Software Engineering (RSE) group at NASA aims at exploring techniques to ensure that systems that use deep neural networks are safe, robust and interpretable. In this talk, I will be presenting our technique Prophecy that automatically infers formal properties of deep neural network models. The tool extracts patterns based on neuron activations as preconditions that imply certain desirable output properties of the model. I would be highlighting case studies that use Prophecy in obtaining explanations for network decisions, understanding correct and incorrect behavior, providing formal guarantees wrt safety and robustness, and debugging neural network models. We have applied the tool on image classification networks, neural network controllers providing turn advisories in unmanned aircrafts, regression models used for autonomous center-line tracking in aircrafts and neural network object detectors

Deep Neural Networks↗
NASA NTRS2023

An MBSE Approach for Developing an Autonomous Rover Platform

The proliferation of increasingly autonomous systems calls for new ways to address how safety is assured. As these systems become more advanced and complex, it becomes more important to model and prototype autonomous functions at the systems level and the functions that assure they are operating safely and as expected. To that effect, researchers at the National Aeronautics and Space Administration (NASA) 's Robust Software Engineering (RSE) group are working on prototyping a Research Autonomous Vehicle, commonly referred to as R-RAV. The R-RAV is an autonomous rover platform designed to act as a case study for assured autonomy research. Moreover, an overarching goal is for the R-RAV to serve as a training ground for other mission projects. In this paper, we will detail how we have used a Model-Based Systems Engineering (MBSE) approach to model a prototype of the R-RAV and test and verify its different functionalities.

MBSE↗
NASA NTRS2022

Building a Transdisciplinary, Exascale-Capable Workforce for Geospace Science

Key Points: ●First-principles, self-consistent geospace modeling will require at least exascale-level computing capabilities; however, the technical skills necessary to develop such simulation codes are not taught as part of Heliophysics training/PhD programs. ●Developing exascale Heliophysics codes will require transdisciplinary collaborations between physicists, computer scientists, software engineers, data scientists, and applied mathematics. Such teams must be persistent and formed around specific skills, not specific problems. ●We must have stable, positive long-term career outcomes for Heliophysics scientific developers in order to retain knowledge of and promote future innovation in scientific algorithm development and advanced computing techniques. One such way to do this is by funding long-term scientific programming groups, akin to Heliophysics DRIVE Centers. ●We must have a balance between promoting innovation via funding short-term closed-source development and allowing the open-source community to benefit from and build on the newest scientific modeling techniques.

C Bard↗
NASA NTRSDate not supplied

Extended Duration: The SIRIUS 21 Crew Perspective

The SIRIUS (Scientific International Research In a Unique terrestrial Station) missions represent a collaborative effort between NASA and Russia’s Institute for Biomedical Problems (IBMP) to conduct a series of long duration isolation and confinement spaceflight analog missions. Three missions of 17-day, 4-month, and 8-month duration (SIRIUS 17, 19, and 21) have been completed at IBMP’s Ground-Based Experimental Complex / Nazemnyy eksperimental'nyy kompleks (NEK) in Moscow, Russia. The international SIRIUS 21 crew comprising representatives from the United States, United Arab Emirates and Russia recently completed the 8-month analog lunar mission. The extended duration mission included simulated lunar transit, orbital, and surface operations with corresponding deep space communication delay, during which the crew participated in nearly 70 studies, eight of which were sponsored by NASA’s Human Research Program. The studies examined the effect of isolation and confinement on the behavioral health of research subjects, and investigated medical countermeasures, team performance, crew dynamics, crew autonomy, food system risks, consequences of confinement and associated physiological stressors. SIRIUS 21 crewmembers also participated in operational tasks such as Rover and CubeSat assembly, simulated lunar sample assessment, VR activities, robotic arm training, environmental systems monitoring, exercise, greenhouse maintenance and 3D printing. Communication with Mission Control was limited to 30-minute periods every two hours. Since access to the internet and email was restricted, simulated ground support provided the Crew’s primary source of daily news and mission information. This panel discussion will include presentations from the US SIRIUS 21 crewmembers – William Brown and Ashley Kowalski – about their experience participating in the mission and science. A facilitated question and answer session will follow with attendees encouraged to ask questions and join in discussion with the SIRIUS 21 crewmembers about their experiences. William Brown came to SIRIUS 21 with experience spread across multiple industries, including the military, defense contracting, healthcare consulting, software engineering, and logistics. He has lived in the Middle East, Central Asia, and Russia. A former Boren Scholar, Brown is fluent in Russian. He holds a Master of International Business degree from the University of South Carolina’s Darla Moore School of Business. Prior to that, he earned a bachelor’s degree in Russian language, literature, and culture from the University of South Carolina. There, he also completed additional undergraduate coursework in computer science. Ashley Kowalski is a Project Leader in The Aerospace Corporation’s International Partnerships Department, where she works with, represents, and provides technical support to the the U.S. Space Force Space Systems Command International Affairs (SSC/IA) office. Through her numerous national and international assignments (Russia, China, and Germany), she has worked on topics related to international space systems, national security space systems, civil systems (including human spaceflight and civil launch projects), space policy, satellite industry analysis, and satellite manufacturing start-ups. She is proficient in Russian and German, and fluent in Polish. Kowalski received her Bachelor of Science and Master of Science degrees in mechanical and aerospace engineering from George Washington University in 2011 and 2012, respectively.

S. E. Whiting↗
NASA NTRS2022

Space ROS: Architecture Roadmap

Space ROS is a matured version of ROS 2 using flight software engineering practices to facilitate mission- and safety-critical applications. This roadmap was developed by the team to communicate the planned future evolution of Space ROS

Austin Probe↗
NASA NTRSDate not supplied

Demonstrating the Promise and Problems for Open Science in Heliophysics

The recent push towards open science comes with a new set of problems to address. The additional work required to align a project with even the concepts of open science is at best problematic and typically not reasonably possible. The issue is further complicated by the lack of supporting infrastructure for the requirements of open science. We present the first executable paper in heliophysics as an example of how a collaboration between scientists, software developers, and software engineers can streamline the research process. This novel approach to science enables the science result to more easily adhere to the concepts of open science through the application of technology from inception to publication. We also use this paper to demonstrate gaps in our infrastructure that currently prevent progress in our pursuit of open science. This presentation is meant to initiate discussions of the methods and gaps presented, and we hope the discussion will be productive.

infrastructure↗
NASA NTRS2023

Trades, Architecture, and Design of the Joint Augmented Reality Visual Informatics (Joint AR) Product

Future expeditions will enable exploration and study of the planetary surfaces of the Moon and Mars by performing extravehicular activity (EVA) operations. Present-day International Space Station (ISS) EVA operations require an intricate and tight choreography of crew, space suits, tools, systems, and flight teams to plan, train, and execute with limited advanced informatics. Additionally, EVA operations, aside from the Apollo Lunar surface missions, have predominately focused on maintenance and construction tasks where success criteria are clearly measurable. However, future exploration missions expect to enable crew to carry out scientific objectives in increasingly Earth-independent ways. In this paper, the Joint Augmented Reality Visual Informatics System (Joint AR) characterizes the design space for developing a modular augmented reality (AR) device for a spacesuit form factor that can support crew decision-making for EVA. This paper highlights the project’s experience with a product-focused management style and use-case centered systems engineering approach to iteratively design, build, and test. The Joint AR product features were defined via trade studies and market analysis of previous EVA display efforts, various AR components such as optics, commercial AR systems, light engines, data interfaces, graphics engine software and analog test beds. We outline the defining architectural design decisions, including safety criticality considerations, suit mounting interfaces, computer architectures, and partnership contracting mechanisms. The outcomes of these studies, architecture decisions, and management requirements result in a recommended design which is the Joint AR product. We discuss the evolution, development of these system components, and what work remains. We hope to share a unified understanding of various design decisions and how they impact the future of crew members’ access to data during Lunar and Martian EVAs. This ongoing effort can enable a community-wide discovery process toward realizing necessary AR features and capabilities for future missions.

Augmented Reality↗
NASA NTRSDate not supplied

Certification Concepts for AI/ML Systems

This presentation goes over some of the tools developed at NASA Ames in the Robust Software Engineering group for the assurance and certification of autonomous systems. The research themes presented include improving safety and risk assessment as early as possible in the lifecycle, elicitation and formalization of requirements to facilitate traceability throughout the lifecycle, especially when formal methods are used, algorithms, tools and techniques for the V&V of ML-enabled systems, advanced testing, use of runtime monitoring to ease use of untrusted components, and contribution to draft regulatory standards and assistance in producing and presenting certification evidences.

Autonomy↗
NASA NTRSDate not supplied

The Anatomy of Software Changes and Bugs in Autonomous Operating System

Cyberphysical systems with autonomous functions are complex pieces of software, consisting of many components, some of which implement autonomous functionality and some may use AI or machine learning algorithms. Software bugs in an autonomous system are of particular concern, as they can have catastrophic consequences. However, detailed studies based on empirical data are rare and therefore these bugs are not well understood. This paper aims to contribute towards filling that gap by investigating the software changes and bugs in Autonomy Operating System (AOS) for Unmanned Aircraft Systems (UAS), which consist of 26 components containing about 103,000 lines of code and having a total of 772 bugfixes. Based on the data extracted from the code repository and semi-structured interviews with the developers of AOS, we explore the differences among autonomous software components, components developed using Model-based Software Engineering, and reuse with respect to change proneness, fault proneness, distribution of bugfixes among AOS components and files of these components, and characteristics of bugs of different AOS components. Our results show that the autonomous components were significantly more change prone (measured in number of commits and code churn) and fault prone (measured in bugfixes per KLoC) than non-autonomous components. The distribution of the locations of bugfixes was skewed, both at component and file level (i.e., a small number of components / files contained the majority of bugs). These evidence-based findings provide important insights to researchers and practitioners alike and can be used to efficiently improve the quality and reliability of autonomous systems.

Katerina Goseva-Popstojanova↗
NASA NTRS2023

Trades, Architecture, and Design of the Joint Augmented Reality Visual Informatics System (Joint AR) Product

Future expeditions will enable exploration and study of the planetary surfaces of the Moon and Mars by performing extravehicular activity (EVA) operations. Present-day International Space Station (ISS) EVA operations require an intricate choreography of crew, space suits, tools, systems, and flight teams to plan, train, and execute with limited advanced informatics. In this paper, the Joint Augmented Reality Visual Informatics System (Joint AR) project team at NASA Johnson Space Center (JSC) characterizes the design space for developing a modular augmented reality (AR) device for a spacesuit form factor that can support crew decision-making for EVA. The Joint AR product was defined via trade studies and market analysis of previous EVA display efforts, various AR components such as optics, commercial AR systems, light engines, data interfaces, and graphics engine software. This paper outlines the defining architectural design decisions, including safety criticality considerations, interfaces, and computer architectures. The outcomes of these studies result in a prototype design which is defined here as the Joint AR product. This work aims to enable a community-wide discussion toward realizing necessary suit-compatible AR features and capabilities for future missions.

Paromita Mitra↗