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NASA NTRS2016

JPL's Foundry Furnace: Web-Based Concurrent Engineering for Formulation

The Jet Propulsion Laboratory’s Innovation Foundry is an enterprise tasked with shepherding space mission concepts through the formulation lifecycle. It oversees a number of “virtual teams” for the various stages of formulation. Among these is Team X, which has had considerable success over its more than 20-year history. In a Team X study, domain experts (including engineers devoted to the various spacecraft subsystems) work concurrently and collaboratively over several days to arrive at a feasible point design with a reasonable cost estimate. They use a set of linked Excel workbooks, each developed and approved by a responsible “line organization” within JPL. This toolset has served Team X well over the years, and has evolved since its inception. At the same time, the Innovation Foundry’s portfolio of formulation teams has expanded, and so has the scope of the design challenges they face. The A-Team runs workshop-like architecture studies to focus science investigations, generate mission concepts, assess feasibility, and explore trade spaces. Team Xc performs rapid point design in the style of Team X, but for CubeSats and small spacecraft, using a different toolset. Proposal teams further mature concepts to the point where they can be proposed. Recognizing the importance of trade space modeling combined with new IT services for providing and integrating data, JPL is developing the Foundry Furnace web-based software infrastructure. It will support A-Team, Team Xc, and Team X, providing study management, a catalog of hardware components, a library of re-usable analyses, and a design environment. It is a modernization of JPL’s concurrent engineering infrastructure, embracing the core concepts of Model-Based Systems Engineering, and built with modern software design philosophies.

Murphy, Jonathan↗

NASA NTRS2010

Use of Dynamic Models and Operational Architecture to Solve Complex Navy Challenges

The United States Navy established 8 Maritime Operations Centers (MOC) to enhance the command and control of forces at the operational level of warfare. Each MOC is a headquarters manned by qualified joint operational-level staffs, and enabled by globally interoperable C41 systems. To assess and refine MOC staffing, equipment, and schedules, a dynamic software model was developed. The model leverages pre-existing operational process architecture, joint military task lists that define activities and their precedence relations, as well as Navy documents that specify manning and roles per activity. The software model serves as a "computational wind-tunnel" in which to test a MOC on a mission, and to refine its structure, staffing, processes, and schedules. More generally, the model supports resource allocation decisions concerning Doctrine, Organization, Training, Material, Leadership, Personnel and Facilities (DOTMLPF) at MOCs around the world. A rapid prototype effort efficiently produced this software in less than five months, using an integrated process team consisting of MOC military and civilian staff, modeling experts, and software developers. The work reported here was conducted for Commander, United States Fleet Forces Command in Norfolk, Virginia, code N5-0LW (Operational Level of War) that facilitates the identification, consolidation, and prioritization of MOC capabilities requirements, and implementation and delivery of MOC solutions.

Grande, Darby↗

NASA NTRS2022

Flight Software Dictionary Development for the Mars2020 Rover

The Mars2020 project, developed and operated by the Jet Propulsion Laboratory (JPL), successfully landed the Perseverance rover and its flying companion Ingenuity on the surface of Mars on February 18th 2021. Perseverance combines heritage and cutting-edge flight software and hardware to accomplish crucial mission requirements related to Martian surface sampling. The design, development, and operation of NASA’s large strategic science missions require the ability to communicate spacecraft capabilities to hundreds of engineers across multiple disciplines. The interaction between flight and ground software development, Verification and Validation (V&V), Assembly, Test, and Launch Operations (ATLO), and management each demand quick understanding of unique slices of information for each discipline. This information includes the current capabilities of the flight system as well as future capabilities and their status as they are developed and tested. Despite the fundamental and critical nature of this information, the flight software dictionaries used to track it are a stumbling block for many projects. These dictionaries provide the cornerstone for the interpretation of data sent from the spacecraft, allowing for quick comprehension by engineers on the ground. During both spacecraft development and operations, flight software dictionary management includes significant challenges due to the large number of interfacing systems and the subtle yet distinct needs of each.The engineering of flight software dictionaries for Mars2020 had numerous challenges, most-notably: parallel dictionary development to support simultaneous separate flight software build campaigns for each mission phase (cruise and surface), managing requests for operations-enabling information without perturbing the heritage interface with the rover, and the introduction of new tools by the dictionary stakeholders that forced the dictionary team to innovate and redesign the heritage tool chain. These challenges generated guiding principles for the dictionary development effort: emphasize coding best practices and unit testing in the dictionary code development tool chain, use institutionally provided COTS (commercial-off-the-shelf) tools whenever possible, and maintain the heritage flight-ground interface all while advancing operations-enabling information via a loosely coupled interface.Throughout development and operations, the Mars2020 dictionary toolchain included IBM DOORS Next Generation, GitHub, Microsoft Excel, Docker, Jenkins, and a significant custom-built Python codebase. Significant interfaces included JPL’s command and control software, heritage flight software team tools and processes, and the many cloud-based ground tools developed for the mission.This paper will discuss the requirements for the Mars2020 dictionary development, the development team’s response to those requirements, lessons learned throughout the process, steps taken towards automated deliveries and continuous integration of stakeholder inputs, potential toolchain improvements for Mars2020, and key takeaways that could be applied to future missions.

Pyrzak, Guy↗

NASA NTRS2010

Architecture-Based Unit Testing of the Flight Software Product Line

This paper presents an analysis of the unit testing approach developed and used by the Core Flight Software (CFS) product line team at the NASA GSFC. The goal of the analysis is to understand, review, and reconunend strategies for improving the existing unit testing infrastructure as well as to capture lessons learned and best practices that can be used by other product line teams for their unit testing. The CFS unit testing framework is designed and implemented as a set of variation points, and thus testing support is built into the product line architecture. The analysis found that the CFS unit testing approach has many practical and good solutions that are worth considering when deciding how to design the testing architecture for a product line, which are documented in this paper along with some suggested innprovennents.

Ganesan, Dharmalingam↗

NASA NTRS2020

Introduction to ISS Crew Displays

The International Space Station (ISS) began payload operations in earnest in 2000 with the arrival of the Expedition 1. To date, ISS has offered Principal Investigators (PIs) a reliable platform for microgravity research, having hosted thousands of onboard science experiments. Most of this research is supported by experiment hardware and software and many include a crew‐operated Graphical User Interface (GUI). The purpose of this article is to share information with PIs and Payload Developer (PD) teams about the processes, standards, and guidelines applicable to crew GUI design that must be complied with when planning payload software. The goal is not to enumerate all of the ISS display standards, but rather to highlight design guidelines and the ISS Program milestones for verification and approval of onboard crew displays.

Graphical User Interface↗

NASA NTRS2021

NASA Work Breakdown Structure (WBS) Handbook

The purpose of this document is to provide program/project teams necessary instruction and guidance in the best practices for WBS and WBS dictionary development and use for project implementation and management control. This handbook can be used for all types of NASA projects and work activities including research, development, construction, test and evaluation, and operations. The products of these work efforts may be hardware, software, data, or service elements (alone or in combination). The aim of this document is to assist project teams in the development of effective work breakdown structures that provide a framework of common reference for all project elements. The WBS and WBS dictionary are effective management processes for planning, organizing, and administering NASA programs and projects. The guidance contained in this document is applicable to both in-house, NASA-led effort and contracted effort. It assists management teams from both entities in fulfilling necessary responsibilities for successful accomplishment of project cost, schedule, and technical goals. Benefits resulting from the use of an effective WBS include, but are not limited to: providing a basis for assigned project responsibilities, providing a basis for project schedule and budget development, simplifying a project by dividing the total work scope into manageable units, and providing a common reference for all project communication.

Christopher Lewis Sadler↗

NASA NTRS2012

Sampling Technique for Robust Odorant Detection Based on MIT RealNose Data

This technique enhances the detection capability of the autonomous Real-Nose system from MIT to detect odorants and their concentrations in noisy and transient environments. The lowcost, portable system with low power consumption will operate at high speed and is suited for unmanned and remotely operated long-life applications. A deterministic mathematical model was developed to detect odorants and calculate their concentration in noisy environments. Real data from MIT's NanoNose was examined, from which a signal conditioning technique was proposed to enable robust odorant detection for the RealNose system. Its sensitivity can reach to sub-part-per-billion (sub-ppb). A Space Invariant Independent Component Analysis (SPICA) algorithm was developed to deal with non-linear mixing that is an over-complete case, and it is used as a preprocessing step to recover the original odorant sources for detection. This approach, combined with the Cascade Error Projection (CEP) Neural Network algorithm, was used to perform odorant identification. Signal conditioning is used to identify potential processing windows to enable robust detection for autonomous systems. So far, the software has been developed and evaluated with current data sets provided by the MIT team. However, continuous data streams are made available where even the occurrence of a new odorant is unannounced and needs to be noticed by the system autonomously before its unambiguous detection. The challenge for the software is to be able to separate the potential valid signal from the odorant and from the noisy transition region when the odorant is just introduced.

Duong, Tuan A.↗

NASA NTRS2021

Carolina Coastal Plain Ecological Forecasting: Utilizing NASA Earth Observations to Map Suitable Venus Flytrap Habitat in an Effort to Inform Conservation, Seed Banking, and Reintroduction in the Carolina Coastal Plain and Sandhills Regions

Although the carnivorous plant Venus flytrap (Dionaea muscipula) is recognized globally, its native range is restricted to a small portion of the North and South Carolina Coastal Plain and Sandhills. Within this limited range, Venus flytrap populations are threatened by habitat loss, fire suppression, and poaching. NASA DEVELOP partnered with the North Carolina Botanical Garden (NCBG), the University of North Carolina Herbarium (NCU), and the North Carolina Natural Heritage Program (NCNHP) to support Venus flytrap conservation. The team developed models based on species presence data and environmental variables using the Software for Assisted Habitat Modeling (SAHM) to create a 2021 habitat suitability map for Venus flytrap. These models incorporated Earth observations collected by Landsat 8 Thermal Infrared Sensor (TIRS), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), and Sentinel-2 Multispectral Instrument (MSI). To predict areas at high risk of development, the team produced a 2050 land-use change map using TerrSet Land Change Modeler. The team found potential areas of conflict between predicted habitat and forecasted future development. Many areas of suitable habitat were concentrated along the coast where development was likely to occur, placing populations there at risk of extirpation. Overlaying suitable habitat with forecasted land change also identified suitable habitats with minimal risk of development, which may serve as lasting Venus flytrap habitat. These results can inform the NCBG and NCNHP’s conservation decision-making, including targeted seed banking, reintroduction, and prioritization of enduring habitats for protection and management.

Monika Rock↗

NASA NTRS2022

Carolina Coastal Plain Ecological Forecasting

Monika Rock↗

NASA NTRS2013

Agile

This is based on a previous talk on agile development. Methods for delivering software on a short cycle are described, including interactions with the customer, the affect on the team, and how to be more effective, streamlined and efficient.

Software development tools↗

NASA NTRS2010

Work Breakdown Structure (WBS) Handbook

The purpose of this document is to provide program/project teams necessary instruction and guidance in the best practices for Work Breakdown Structure (WBS) and WBS dictionary development and use for project implementation and management control. This handbook can be used for all types of NASA projects and work activities including research, development, construction, test and evaluation, and operations. The products of these work efforts may be hardware, software, data, or service elements (alone or in combination). The aim of this document is to assist project teams in the development of effective work breakdown structures that provide a framework of common reference for all project elements. The WBS and WBS dictionary are effective management processes for planning, organizing, and administering NASA programs and projects. The guidance contained in this document is applicable to both in-house, NASA-led effort and contracted effort. It assists management teams from both entities in fulfilling necessary responsibilities for successful accomplishment of project cost, schedule, and technical goals. Benefits resulting from the use of an effective WBS include, but are not limited to: providing a basis for assigned project responsibilities, providing a basis for project schedule development, simplifying a project by dividing the total work scope into manageable units, and providing a common reference for all project communication.

Source record↗

NASA NTRS2016

NASA Work Breakdown Structure (WBS) Handbook

The purpose of this document is to provide program/project teams necessary instruction and guidance in the best practices for Work Breakdown Structure (WBS) and WBS dictionary development and use for project implementation and management control. This handbook can be used for all types of NASA projects and work activities including research, development, construction, test and evaluation, and operations. The products of these work efforts may be hardware, software, data, or service elements (alone or in combination). The aim of this document is to assist project teams in the development of effective work breakdown structures that provide a framework of common reference for all project elements. The WBS and WBS dictionary are effective management processes for planning, organizing, and administering NASA programs and projects. The guidance contained in this document is applicable to both in-house, NASA-led effort and contracted effort. It assists management teams from both entities in fulfilling necessary responsibilities for successful accomplishment of project cost, schedule, and technical goals. Benefits resulting from the use of an effective WBS include, but are not limited to: providing a basis for assigned project responsibilities, providing a basis for project schedule and budget development, simplifying a project by dividing the total work scope into manageable units, and providing a common reference for all project communication.

Planning↗

NASA NTRS2018

NASA Work Breakdown Structure (WBS) Handbook

Schedule↗

NASA NTRS2019

NASA Work Breakdown Structure (WBS) Handbook

Formulation↗

NASA NTRS2016

Modeling in the State Flow Environment to Support Launch Vehicle Verification Testing for Mission and Fault Management Algorithms in the NASA Space Launch System

Analysis methods and testing processes are essential activities in the engineering development and verification of the National Aeronautics and Space Administration's (NASA) new Space Launch System (SLS). Central to mission success is reliable verification of the Mission and Fault Management (M&FM) algorithms for the SLS launch vehicle (LV) flight software. This is particularly difficult because M&FM algorithms integrate and operate LV subsystems, which consist of diverse forms of hardware and software themselves, with equally diverse integration from the engineering disciplines of LV subsystems. M&FM operation of SLS requires a changing mix of LV automation. During pre-launch the LV is primarily operated by the Kennedy Space Center (KSC) Ground Systems Development and Operations (GSDO) organization with some LV automation of time-critical functions, and much more autonomous LV operations during ascent that have crucial interactions with the Orion crew capsule, its astronauts, and with mission controllers at the Johnson Space Center. M&FM algorithms must perform all nominal mission commanding via the flight computer to control LV states from pre-launch through disposal and also address failure conditions by initiating autonomous or commanded aborts (crew capsule escape from the failing LV), redundancy management of failing subsystems and components, and safing actions to reduce or prevent threats to ground systems and crew. To address the criticality of the verification testing of these algorithms, the NASA M&FM team has utilized the State Flow environment6 (SFE) with its existing Vehicle Management End-to-End Testbed (VMET) platform which also hosts vendor-supplied physics-based LV subsystem models. The human-derived M&FM algorithms are designed and vetted in Integrated Development Teams composed of design and development disciplines such as Systems Engineering, Flight Software (FSW), Safety and Mission Assurance (S&MA) and major subsystems and vehicle elements such as Main Propulsion Systems (MPS), boosters, avionics, Guidance, Navigation, and Control (GN&C), Thrust Vector Control (TVC), liquid engines, and the astronaut crew office. Since the algorithms are realized using model-based engineering (MBE) methods from a hybrid of the Unified Modeling Language (UML) and Systems Modeling Language (SysML), SFE methods are a natural fit to provide an in depth analysis of the interactive behavior of these algorithms with the SLS LV subsystem models. For this, the M&FM algorithms and the SLS LV subsystem models are modeled using constructs provided by Matlab which also enables modeling of the accompanying interfaces providing greater flexibility for integrated testing and analysis, which helps forecast expected behavior in forward VMET integrated testing activities. In VMET, the M&FM algorithms are prototyped and implemented using the same C++ programming language and similar state machine architectural concepts used by the FSW group. Due to the interactive complexity of the algorithms, VMET testing thus far has verified all the individual M&FM subsystem algorithms with select subsystem vendor models but is steadily progressing to assessing the interactive behavior of these algorithms with LV subsystems, as represented by subsystem models. The novel SFE applications has proven to be useful for quick look analysis into early integrated system behavior and assessment of the M&FM algorithms with the modeled LV subsystems. This early MBE analysis generates vital insight into the integrated system behaviors, algorithm sensitivities, design issues, and has aided in the debugging of the M&FM algorithms well before full testing can begin in more expensive, higher fidelity but more arduous environments such as VMET, FSW testing, and the Systems Integration Lab7 (SIL). SFE has exhibited both expected and unexpected behaviors in nominal and off nominal test cases prior to full VMET testing. In many findings, these behavioral characteristics were used to correct the M&FM algorithms, enable better test coverage, and develop more effective test cases for each of the LV subsystems. This has improved the fidelity of testing and planning for the next generation of M&FM algorithms as the SLS program evolves from non-crewed to crewed flight, impacting subsystem configurations and the M&FM algorithms that control them. SFE analysis has improved robustness and reliability of the M&FM algorithms by revealing implementation errors and documentation inconsistencies. It is also improving planning efficiency for future VMET testing of the M&FM algorithms hosted in the LV flight computers, further reducing risk for the SLS launch infrastructure, the SLS LV, and most importantly the crew.

Trevino, Luis↗

NASA NTRSDate not supplied

Astrobee: Five years of Completed, Current, and Future Research on the International Space Station using Free Flying Robots.

After five years on the International Space Station (ISS), the Astrobee Research Facility, has completed over 160 Test Sessions logging over 1200 hours of operations. Managed by the NASA ISS Program OZ office and supported by NASA Ames Research Center (ARC) in California, the Astrobee Team currently maintains two identical free-flying Astrobee robots and a Docking Station for research on the ISS. As a technology demonstration platform, the Astrobee Robots are available for Guest Scientists to use for a spectrum of research capabilities. Using ambient air on the ISS, propelled by battery-operated fans, Astrobee is designed to autonomously operate throughout most of the USOS (US Orbital Segment), with the objective of minimizing the need for astronaut support. Astrobee carries a suite of six cameras, a two degree-of-freedom (DOF) arm with a gripper that can grasp ISS handrails and other objects, and three payload bays that provide power and data for guest science hardware. Astrobee can autonomously execute hours-long flight plans or be tele-operated from the ground. While the Astrobee Team continues to improve mapping and autonomous flight capabilities, one of the main goals of Astrobee Robots is to provide research opportunities for Guest Scientists. The Astrobee Robot Software (ARS) makes extensive use of the open-source Robot Operating System (ROS). The ARS can be used interchangeably with an Astrobee Simulator or as Astrobee’s onboard software. ARS features include autonomous docking and perching, real-time teleoperations from the ground, plan based autonomous tasks, multi Astrobee communication, among other capabilities. Through simulation software and ground testing laboratories, the Astrobee Team is available to support Guest Scientists during development and testing and lead real-time ISS operations. The Astrobee Team and Guest Scientists have completed research including Astrobatics maneuvers, RFID and sound sensing capabilities, Gecko materials studies, student Robotics Programming Challenges, and Free Flyer formation flight investigations. Current science with the Astrobee Robots is investigating new docking capabilities through software only research as well as testing new docking hardware installed on the Astrobees. The Astrobee Team and other researchers at NASA Ames continue to explore robotics applications for future NASA missions such as Gateway and potential experiments involving human-robot interactions. Continued advanced mapping resolution capabilities, and high-resolution panoramic imagery also remains areas of research. Exciting in development research involves docking for rendezvous proximity operation (CLINGERS), multi resolution 3D scanning (MRS), space debris removal in microgravity (REACCH). This presentation will mainly focus on completed research over the past year and current science being performed on the Astrobees. This presentation will also focus on how a Guest Scientist/Researcher progresses from conception to running their science on the Astrobees on the ISS, as well as discuss the Astrobee Facility resources available for supporting ground testing and real-time ISS operations.

Astrobee↗

NASA NTRS2012

Using Image Pro Plus Software to Develop Particle Mapping on Genesis Solar Wind Collector Surfaces

The continued success of the Genesis mission science team in analyzing solar wind collector array samples is partially based on close collaboration of the JSC curation team with science team members who develop cleaning techniques and those who assess elemental cleanliness at the levels of detection. The goal of this collaboration is to develop a reservoir of solar wind collectors of known cleanliness to be available to investigators. The heart and driving force behind this effort is Genesis mission PI Don Burnett. While JSC contributes characterization, safe clean storage, and benign collector cleaning with ultrapure water (UPW) and UV ozone, Burnett has coordinated more exotic and rigorous cleaning which is contributed by science team members. He also coordinates cleanliness assessment requiring expertise and instruments not available in curation, such as XPS, TRXRF [1,2] and synchrotron TRXRF. JSC participates by optically documenting the particle distributions as cleaning steps progress. Thus, optical document supplements SEM imaging and analysis, and elemental assessment by TRXRF.

Rodriquez, Melissa C.↗

NASA NTRS2019

Agile Approach to Assuring the Safety-Critical Embedded Software for NASA's Orion Spacecraft

Human-rated missions like those in NASA's Orion Program continue to grow in complexity. The role of software in achieving ambitious mission objectives has expanded dramatically in the last few decades. Assuring the safety and performance of the embedded flight software is quickly growing beyond the reach of traditional methods and resource levels. The methods used to build these software-dominant systems evolve in an on-going attempt to keep pace with the scope of our ambitions. Agile software development is now commonplace. The long timelines and large batches of work associated with traditional methods are being replaced by rapid delivery of small increments _ as system capabilities are realized in waves. Assurance of these critical software capabilities must therefore conquer an ever-expanding frontier of challenges, and do so with an approach matched to the evolving development methods. This paper recounts the journey of the Orion Independent Verification and Validation (IV&V) team as we addressed this dynamic environment. Widening our aperture to encompass a dramatically larger mission scope, while adjusting our cadence to synchronize with the rapid pace of agile software development, a new approach to IV&V is emerging. This approach is characterized by a sharper focus on mission capabilities, matched with a method to dynamically _follow the risk' as the IV&V team delivers more compelling assurance data in waves. Traditional methods prevalent in IV&V tend to scope the work using artifacts of the development process as they evolve from preliminary to final versions, and the pace of delivery was synchronized with the development timelines prevalent in the waterfall lifecycle. That more static approach is out of phase with the demands of the new environment. Scoping work according to the critical capabilities of the system (rather than artifacts of development) and synchronizing with the rapid pace of agile development, we are moving toward more effective parity with the demands of the environment. We explain the concrete steps we took, the principles that motivated our choices, and the results we have achieved to date.

Capability based assurance↗