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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Astrobee's Multi-year Activities at the International Space Station's Japanese Experimental Module

The Astrobee free-flying robots recently completed their third successful year of operations, housed in the Japanese Experimental Module (JEM) on the International Space Station. We summarize the three years of operation, giving special attention to JAXA's 1st and 2nd Kibo Robot Programming Challenge (RPC) and the mapping processes and tools that make Astrobees' autonomous operation possible. The JEM is an ever changing, dynamic environment where light settings, cargo, payloads, and crew members constantly move and interact with one another. The 1st JAXA Kibo RPC event, a collaboration between JAXA and NASA, was held in 2020. Students from several countries in the Asia-Pacific region competed in programming challenges with a simulated Astrobee. The finalists were then invited to run their code on an actual Astrobee in the JEM. For the final round, students programmed Astrobee to visit three different locations to obtain data that would instruct the robot to complete a final task with the participation of ISS crew. The first competition was a tremendous success, leading to an equally successful 2nd JAXA Kibo RPC in 2021 with even larger participation. The 3rd JAXA Kibo RPC will occur in 2022 expanding further to incorporate US participants. These activities led to several firsts in Astrobee’s history: operation of an Astrobee free-flying robot without crew supervision in preparation for on-orbit operations, autonomous image acquisition towards updates of the navigation map, non-NASA code running on the robot (both from JAXA and participating students), two heterogeneous free-flying robots from two different space agencies working together (Int-Ball and Astrobee) during the final event in 2020, the first payload using Astrobee, and having Astrobee controlled from a non-NASA location (Tsukuba Space Center). The preparation towards these activities involved constant evaluation of the different components of Astrobee's systems, specially mapping and localization. The paper describes the evolution of these systems such as the improvements made in localization to reduce localization drift by using graph-based optimization instead of the extended Kalman Filter localizer. Additionally, it reports on the mapping process and analysis tools created to validate map consistency across different activities in the constantly changing JEM environment. These enhancements have enabled the Astrobee facility to successfully execute over 100 ISS activities supporting over a dozen researchers and partners around the world.

Astrobee↗
NASA NTRSDate not supplied

Developing a Genetic Variant Calling Pipeline for Quantifying the Complex Mutagenic Load Accumulated in BioNutrients-1 Production Pack Samples

Microorganisms hold great promise for on demand production of labile nutrients and pharmaceuticals as well recycling and in situ resource utilization. The utilization of microorganisms for such tasks on space missions is hindered by the limited data on how microbes respond to spaceflight. For example, the genetic stability of microorganisms, and the genomic engineered traits added to deliver desired functions, over long-term storage in the spacecraft environment is poorly understood. The BioNutrients-1 (BN-1) mission conducted a 5-year study of desiccated storage in Low Earth Orbit (LEO) to evaluate the suitability of eight synthetic biology chassis organisms for long-duration space missions. We are employing high-depth, whole genome sequencing (WGS) to determine the mutagenic load that accumulated during long-term storage. Mutation analysis pipelines are well established for homogenous culture grown from a single colony, but the mutational landscape of the BN-1 samples present a unique analysis challenge, as every cell in the BN-1 samples had a unique genetic journey of DNA damage and repair. Consequently, sequence variants are expected at low allele frequency within samples. To address this genetic complexity, we apply two distinct computational approaches to identify mutations in pre-existing WGS data collected from populations of Chlamydomonas reinhardtii that were exposed to UV mutagenesis and growth in LEO. For reference genome free mutation detection, we utilized DiscoSNP++, which is a de Bruijn graph approach. For reference genome-based mutation detection we utilize GATK for Microbes, which is a Bayesian probabilistic approach. We will benchmark these approaches against the mutations originally identified using CRISP, a method optimized for pooled samples. Ultimately, quantifying the mutation load imposed by storage or growth on the ISS will help identify chassis organisms with both high levels of genome stability and viability, which are desirable traits for implementation of bioproduction in long-duration missions.

SNP↗
NASA NTRS2012

Mars Relay Lander and Orbiter Overflight Profile Estimation

This software allows science and mission operations to view graphs of geometric overflights of satellites and landers within the Mars (or other planetary) networks. It improves on the MaROS Web interface within any modern Web browser, in that it adds new capabilities to the MaROS suite. The profile for an overflight is an important element for selecting communication/ overflight opportunities between the landers and orbiters within the Mars network. Unfortunately, determining these estimates is very computationally expensive and difficult to compute by hand. This software allows the user to select different overflights (via the existing MaROS Web interface) and specify the smoothness of the estimation. Estimates for the geometric relationship between a lander and an orbiter are determined based upon the orbital conditions of the orbiter at the moment the orbiter rises above the horizon from the perspective of the lander. It utilizes 2-body orbital equations to propagate the trajectory through the duration of the view period, and returns profiles that represent the range between the two vehicles, and the elevation and azimuth angles of the orbiter as measured from the lander s position. The algorithms assume a 2-body relationship with an ideal, spherical planetary body, so therefore can see errors less than 2% at polar landing sites on Mars. These algorithms are being implemented to provide rough estimates rapidly for the geometry of a geometric view period where more complete data is unavailable, such as for planning purposes. While other software for this task exists, each at the time of this reporting has been contained within a much more complicated package. This tool allows science and mission operations to view the estimates with a few clicks of the mouse.

Wallick, Michael N.↗
NASA NTRS2000

A Tool for Automatic Data Distribution for CFD Applications on Structured Grids

Development of HPF versions of NPB and ARC3D has shown that HPF provides an efficient, concise way to express parallelism and to organize data traffic. The use of HPF, as noted in the papers, requires an intimate knowledge of the applications and a detailed analysis of data affinity, data movement, and data granularity. To simplify and accelerate the task of developing HPF versions of existing CFD applications we have designed and implemented ADAPT (Automatic Data Alignment and Placement Tool). ADAPT analyzes a CFD application working on a single structured grid and generates HPF TEMPLATE, (RE)DISTRIBUTION, ALIGNMENT, and INDEPENDENT directives. The directives can be generated on the nest level, subroutine level, application level, or on the application interface level. ADAPT annotates an existing CFD FORTRAN application, performing computations on single or multiple grids. On each grid the application is considered as a sequence of operators, each applied to a set of variables defined in a particular grid domain. ADAPT automatically detects implicit operators (i.e., having data dependences) and explicit operators (without data dependences). For parallelization of an explicit operator ADAPT creates a template for the operator domain, aligns arrays used in the operator with the template, distributes the template, and declares the loops over the distributed dimensions as INDEPENDENT. For parallelization of an implicit operator, the distribution of the operator's domain should be consistent with the operator's dependences. Any dependence between sections distributed on different processors would preclude parallelization if the compiler does not have an ability to pipeline computations. If a data distribution is "orthogonal" to the dependences of an implicit operator, then the loop which implements the operator can be declared as INDEPENDENT. ADAPT starts with an analysis of array index expressions of the loop nests. For each pair of arrays referenced in an assignment statement, it generates an arc in the alignment graph and annotates it with an affinity relation. The template, alignment, and distribution directives for a particular loop nest are then derived from a transitive closure of the affinity relation. A compromise of data distributions in different nests and subroutines is achieved by merging annotated alignment graphs for adjacent nests/stibroutine calls in the nest/call graph of the application in the process called distribution lifting. ADAPT has been implemented as a C++ program running in conjunction with a parallelization tool called CAPTools. ADAPT uses the parse tree, interprocedural analysis and application database generated by CAPTools. It also uses the Directed Graph class, initially implemented in p2d2 (parallel debugger oi distributed programs), and some other classes supporting symbolic computations. ADAPT uses data distribution techniques described. ADAPT was tested with ARC3D and the FT benchmark and has demonstrated a code performance within a factor of 1.5 of handwritten versions.

Frumkin, Michael↗
NASA NTRS1993

Cognitive engineering models in space systems

NASA space systems, including mission operations on the ground and in space, are complex, dynamic, predominantly automated systems in which the human operator is a supervisory controller. Models of cognitive functions in complex systems are needed to describe human performance and form the theoretical basis of operator workstation design, including displays, controls, and decision aids. Currently, there several candidate modeling methodologies. They include the Rasmussen abstraction/aggregation hierarchy and decision ladder, the goal-means network, the problem behavior graph, and the operator function model. The research conducted under the sponsorship of this grant focuses on the extension of the theoretical structure of the operator function model and its application to NASA Johnson mission operations and space station applications. The initial portion of this research consists of two parts. The first is a series of technical exchanges between NASA Johnson and Georgia Tech researchers. The purpose is to identify candidate applications for the current operator function model; prospects include mission operations and the Data Management System Testbed. The second portion will address extensions of the operator function model to tailor it to the specific needs of Johnson applications. At this point, we have accomplished two things. During a series of conversations with JSC researchers, we have defined the technical goal of the research supported by this grant to be the structural definition of the operator function model and its computer implementation, OFMspert. Both the OFM and OFMspert have matured to the point that they require infrastructure to facilitate use by researchers not involved in the evolution of the tools. The second accomplishment this year was the identification of the Payload Deployment and Retrieval System (PDRS) as a candidate system for the case study. In conjunction with government and contractor personnel in the Human-Computer Interaction Lab, the PDRS was identified as the most accessible system for the demonstration. Pursuant to this a PDRS simulation was obtained from the HCIL and an initial knowledge engineering effort was conducted to understand the operator's tasks in the PDRS application. The preliminary results of the knowledge engineering effort and an initial formulation of an operator function model (OFM) are contained in the appendices.

Mitchell, Christine M.↗
NASA NTRSDate not supplied

Enhancing NASA Earth Science Data Discovery from Scientific Publications

Earth observations from space borne instruments have evolved explosively in the past decades. Following closely are reanalysis systems assimilating model and observational data, yielding even longer records and larger number of variables. Thanks to advances in internet technology, it is now easier than ever to visualize and analyze these data using web interfaces. On the other hand, it also becomes an increasingly daunting task to build upon the existing knowledge published in various peer reviewed sources, and navigate toward the most relevant data, analysis, and visualization. We present an analysis of a subset of publications that utilized a popular visualization web interface at the NASA Goddard Earth Science Data and Information Services Center. Known as "Giovanni", it allows researchers from wide backgrounds to work with hundreds of variables from space observations and assimilation systems. Since coming online more than a decade ago, Giovanni has been credited in more than 100 papers per year, and the total count now is estimated to be nearly 1,500. Many of these papers contain valuable information about when, where and how Giovanni has been used, and hence forge an opportunity to learn and share the knowledge of which variables were used for what research projects. The purpose of our work is to retrieve the information from the papers and organize it as a knowledge repository which links together datasets, variables, places, dates and phenomena all of which reflect the essence of the published research. Since the publications are unstructured texts, we use natural language processing along with machine learning methods in the retrieval process. One of the challenges is deciphering the dataset names, because in many cases researchers refer to variables, rather than the datasets containing them. To constrain the number of terms, we deploy Earth Science ontologies as dictionaries for the term extraction. We demonstrate that storing these terms and underlying ontologies, along with datasets, variables and papers in the knowledge graph database, enables various linkages between all these entities facilitating the data discovery. Thus, we are setting a qualitatively new stage in improvements of web data interfaces, where machine learning techniques are used to establish and optimize usage-based discovery of data.

Irina V Gerasimov↗
NASA NTRS2014

The Importance of Data Visualization: Incorporating Storytelling into the Scientific Presentation

From its inception in 2000, one of the primary tasks of the Biomedical Data Reduction Analysis (BDRA) group has been translation of large amounts of data into information that is relevant to the audience receiving it. BDRA helps translate data into an integrated model that supports both operational and research activities. This data integrated model and subsequent visual data presentations have contributed to BDRA's success in delivering the message (i.e., the story) that its customers have needed to communicate. This success has led to additional collaborations among groups that had previously not felt they had much in common until they worked together to develop solutions in an integrated fashion. As more emphasis is placed on working with "big data" and on showing how NASA's efforts contribute to the greater good of the American people and of the world, it becomes imperative to visualize the story of our data to communicate the greater message we need to share. METHODS To create and expand its data integrated model, BDRA has incorporated data from many different collaborating partner labs and other sources. Data are compiled from the repositories of the Lifetime Surveillance of Astronaut Health and the Life Sciences Data Archive, and from the individual laboratories at Johnson Space Center that support collection of data from medical testing, environmental monitoring, and countermeasures, as designated in the Medical Requirements Integration Documents. Ongoing communication with the participating collaborators is maintained to ensure that the message and story of the data are retained as data are translated into information and visual data presentations are delivered in different venues and to different audiences. RESULTS We will describe the importance of storytelling through an integrated model and of subsequent data visualizations in today's scientific presentations and discuss the collaborative methods used. We will illustrate the discussion with examples of graphs from BDRA's past work supporting operations and/or research efforts.

Babiak-Vazquez, A.↗