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A collection of viewgraphs on NASA's Life Sciences Biomedical Programs is presented. They show the structure of the Life Sciences Division; the tentative space exploration schedule from the present to 2018; the biomedical programs with their objectives, research elements, and methodological approaches; validation models; proposed Antarctic research as an analog for space exploration; and the Science Working Group's schedule of events.
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.
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.
Harnessing our greatest national resource, as represented by the nation's students, will require a thoughtful, well developed and administered program that includes precise, executable strategies and valid evaluation tools. Responding to a national education outreach priority, the National Aeronautics and Space Administration's Life and Biomedical Sciences and Applications Division has initiated a process or organizing and implementing various strategies through a steering committee that includes representatives from Headquarters and three field centers with major Life Sciences programs. The mandate of the Life Sciences Education Outreach Steering Committee is to develop ways of communicating space life science issues to America's students through the nation's teachers by curriculum enhancement and direct participation in the education process with an emphasis in the primary and secondary schools. Metrics are also developed for each individually defined process so that the mechanis can be continuously refined and improved.
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 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. Here we present an overview of AI/ML architecture to support deep space mission goals, developed with leaders in the field. First, we focus on the fundamental biological research that supports our understanding of physiological responses to spaceflight, and we describe current efforts to support AI/ML research including data standardization and data engineering through maximally open and FAIR (findable, accessible, interoperable, reusable) databases and the generation of AI-ready datasets for reuse and analysis. We also discuss remote data management frameworks for research data as well as environmental and health data that are generated during deep space missions. We highlight several research projects that leverage data standardization and management for fundamental biological discovery to uncover the complex effects of space travel on living systems. Next, we provide an overview of cutting-edge AI/ML approaches that can be integrated to support remote monitoring and analysis during deep space missions, including generative models and large language models to learn the underlying biomedical patterns and predict outcomes or answer questions during off world medical scenarios. We also describe current AI/ML methods to support this research and monitoring through automated cloud-based labs which enable limited human intervention and closed-loop experimentation in remote settings. These labs could support mission autonomy by analyzing environmental data streams, and would be facilitated through in situ analytics capabilities to avoid sending large raw data files through low bandwidth communications. Finally, in the context of deep space missions with limited communications or access to medical advice from Earth, we describe a solution for integrated, real-time mission biomonitoring across hierarchical levels from continuous environmental monitoring, to wearables and point-of-care devices, to molecular and physiological monitoring. We introduce a precision space health system that will ensure that the future of space health is predictive, preventative, participatory and personalized.
Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.
The National User Resource for Biological Accelerator Mass Spectrometry (User Resource) will provide isotopic analysis (primarily radiocarbon or 14C) by accelerator mass spectrometry (AMS) for NIH- funded researchers across the United States and will be the only User Resource of its type in the United States. The User Resource will provide measurement capability and expertise to a research community that requires highly sensitive, quantitative isotope analyses. Since commissioning a new accelerator mass spectrometer in June 2014, we have measured over 4000 samples a year for collaborators and service users. The User Resource will enable us to continue to meet these research needs, as well as provide for new users whose research programs would benefit from AMS as a measurement tool. The User Resource’s forte will be ultra-high sensitivity quantitation of radiocarbon and selected other radioisotopes for research studies where isotopes are required. Radioisotope labeling studies have been and will continue to be an important tool for addressing many complex biomedical science problems. AMS is a specialized and unique type of mass spectrometry that provides absolute quantitation of radiocarbon and other relevant radioisotopes with extreme sensitivity, having limits of detection in real samples on the order of a few attomol/mg of sample at measurement precisions of ~3%. It is the only instrumental method capable of quantifying radioisotope-labeled agents routinely in real-world samples with such precision and sensitivity. The sensitivity of AMS allows for the quantification of radiolabeled metabolites in extremely complex matrices of cells and organisms at very low concentrations and in small samples. AMS allows studies to be conducted without perturbing metabolism leading to more relevant quantification of metabolic rates and pathways. In addition, it enables quantification of pharmacokinetic and metabolic properties of toxicants at environmentally relevant concentrations in model systems as well as the ability to quantify pharmacokinetics and other molecular endpoints directly in humans. Such quantitative assessments can 1) improve risk assessment for toxicants, 2) address safety and efficacy considerations for therapeutic entities, 3) deepen understanding of xenobiotic and intermediary metabolism, 4) help understand the interactions between critical molecular pathways, and 5) improve efforts to model and predict various metabolic and biological states. These capabilities have been applied in a number of areas including research in carcinogenesis, toxicology, nutrition, pharmacology/drug development and basic biological science. As a NIGMS National Resource the National User Resource for Biological Accelerator Mass Spectrometry will help NIH funded scientists achieve a deeper understanding of the etiology of human health concerns by (1) enabling the quantification of pharmacokinetics and other molecular endpoints directly in humans; (2) offering the ability to conduct quantitative studies using biologics such as proteins or lipids, and thereby reducing the amount of radioisotope usage in biomedical labs; and (3) enabling more relevant studies of metabolic pathways in health and disease through the use of much lower, more biologically-relevant, concentrations of metabolic substrates in cells and intact organisms. Such studies support NIGMS’s basic biomedical research areas that contribute to the understanding of fundamental cellular and physiological principles and enable research supported by the Biophysics, Biomedical Technology, and Computational Biosciences (BBCB); Genetics and Molecular, Cellular, and Developmental Biology (GMCDB); Pharmacology, Physiology, Biological Chemistry (PPBC) and Training, Workforce Development, and Diversity (TWD) Divisions. Over the next five years, our goals are to: 1. Improve the efficiency of operation for AMS measurements through installation of new interfaces to our AMS systems, technical modifications to improve gas accepting ion source efficiency and upgrading our data analysis software for improved ease of use and data reporting. 2. Increase the accessibility and visibility of ultra-sensitive 14C measurements for the biomedical research community by training of new investigators and expanding our national user base. 3. Provide high throughput, ultra-sensitive 14C analysis for the NIGMS and NIH user community.
Applications of aerospace technology to biomedical science are described. Recent research and development of specific techniques, services, and equipment adopted by physicians to help combat disease and disability are reviewed.
Three general classes of manned space flight missions of the future are described. These include: earth-orbital, lunar, and planetary. Biomedical science and technology is analyzed emphasizing areas of research needed to support future manned space flights and the information to be obtained from them.
Developed by NASA Glenn Research Center, the LMM aboard the International Space Station (ISS) is enabling multiple biomedical science experiments. Techshot, Inc., has developed a series of colloid specialty cell systems (C-SPECS) for use in the colloid science experiment module on the LMM. These low-volume mixing devices will enable uniform particle density and remotely controlled repetition of LMM colloid experiments. By automating the experiment process, C-SPECS allow colloid samples to be processed more quickly. In addition, C-SPECS will minimize the time the crew will need to spend on colloid experiments as well as eliminate the need for multiple and costly colloid samples, which are expended after a single examination. This high-throughput capability will lead to more efficient and productive use of the LMM. As commercial launch vehicles begin routine visits to the ISS, C-SPECS could become a significant means to process larger quantities of high-value materials for commercial customers.
The GeneLab project is a science initiative to maximize the scientific return of omics data collected from spaceflight and from ground simulations of microgravity and radiation experiments, supported by a data system for a public bioinformatics repository and collaborative analysis tools for these data. The mission of GeneLab is to maximize the utilization of the valuable biological research resources aboard the ISS by collecting genomic, transcriptomic, proteomic and metabolomic (so-called omics) data to enable the exploration of the molecular network responses of terrestrial biology to space environments using a systems biology approach. All GeneLab data are made available to a worldwide network of researchers through its open-access data system. GeneLab is currently being developed by NASA to support Open Science biomedical research in order to enable the human exploration of space and improve life on earth. Open access to Phase 1 of the GeneLab Data Systems (GLDS) was implemented in April 2015. Download volumes have grown steadily, mirroring the growth in curated space biology research data sets (61 as of June 2016), now exceeding 10 TB/month, with over 10,000 file downloads since the start of Phase 1. For the period April 2015 to May 2016, most frequently downloaded were data from studies of Mus musculus (39) followed closely by Arabidopsis thaliana (30), with the remaining downloads roughly equally split across 12 other organisms (each 10 of total downloads). GLDS Phase 2 is focusing on interoperability, supporting data federation, including integrated search capabilities, of GLDS-housed data sets with external data sources, such as gene expression data from NIHNCBIs Gene Expression Omnibus (GEO), proteomic data from EBIs PRIDE system, and metagenomic data from Argonne National Laboratory's MG-RAST. GEO and MG-RAST employ specifications for investigation metadata that are different from those used by the GLDS and PRIDE (e.g., ISA-Tab). The GLDS Phase 2 system will implement a Google-like, full-text search engine using a Service-Oriented Architecture by utilizing publicly available RESTful web services Application Programming Interfaces (e.g., GEO Entrez Programming Utilities) and a Common Metadata Model (CMM) in order to accommodate the different metadata formats between the heterogeneous bioinformatics databases. GLDS Phase 2 completion with fully implemented capabilities will be made available to the general public in September 2017.
GeneLab is currently being developed by NASA to accelerate 'open science' biomedical research in support of the human exploration of space and the improvement of life on earth. Phase I of the four-phase GeneLab Data Systems (GLDS) project emphasized capabilities for submission, curation, search, and retrieval of genomics, transcriptomics and proteomics ('omics') data from biomedical research of space environments. The focus of development of the GLDS for Phase II has been federated data search for and retrieval of these kinds of data across other open-access systems, so that users are able to conduct biological meta-investigations using data from a variety of sources. Such meta-investigations are key to corroborating findings from many kinds of assays and translating them into systems biology knowledge and, eventually, therapeutics.
GeneLab is currently being developed by NASA to accelerate open science biomedical research in support of the human exploration of space and the improvement of life on earth. Phase I of the four-phase GeneLab Data Systems (GLDS) project emphasized capabilities for submission, curation, search, and retrieval of genomics, transcriptomics and proteomics (omics) data from biomedical research of space environments. The focus of development of the GLDS for Phase II has been federated data search for and retrieval of these kinds of data across other open-access systems, so that users are able to conduct biological meta-investigations using data from a variety of sources. Such meta-investigations are key to corroborating findings from many kinds of assays and translating them into systems biology knowledge and, eventually, therapeutics.
GeneLab (http://genelab.nasa.gov) is a NASA initiative designed to accelerate “open science” biomedical research in support of the human exploration of space and the improvement of life on earth. The GeneLab Data Systems (GLDS) were developed to help investigators corroborate findings from “omics” (genomics, transcriptomics, proteomics, and metabolomics) assays and translate them into systems biology knowledge and, eventually, therapeutics, including countermeasures to support life in space. Phase I of the project (completed) emphasized developing key capabilities for submission, curation, storage, search, and retrieval of omics data from biomedical research in and of space environments. The development focus for Phase II (completed) was federated data search and retrieval of these kinds of data from other open-access repositories. The last phase of the project (in work) entails developing an omics analysis tool set, and a portal to visualize processed omics data, emphasizing integration with the data repository and search functions developed during the prior phases. The final product will be an open-access system where users can individually or collaboratively publish, search, integrate, analyze, and visualize omics data.
GeneLab (http://genelab.nasa.gov) is a NASA initiative designed to accelerate "open science" biomedical research in support of the human exploration of space and the improvement of life on earth. The GeneLab Data Systems (GLDS) were developed to help investigators corroborate findings from "omics" (genomics, transcriptomics, proteomics, and metabolomics) assays and translate them into systems biology knowledge and, eventually, therapeutics, including countermeasures to support life in space. Phase I of the project (completed) emphasized developing key capabilities for submission, curation, storage, search, and retrieval of omics data from biomedical research in and of space environments. The development focus for Phase II (completed) was federated data search and retrieval of these kinds of data from other open-access repositories. The last phase of the project (in work) entails developing an omics analysis tool set, and a portal to visualize processed omics data, emphasizing integration with the data repository and search functions developed during the prior phases. The final product will be an open-access system where users can individually or collaboratively publish, search, integrate, analyze, and visualize omics data.
GeneLab (http://genelab.nasa.gov) is a NASA initiative designed to accelerate "open science" biomedical research in support of the human exploration of space and the improvement of life on earth. The GeneLab Data Systems (GLDS) were developed to help investigators corroborate findings from "omics" (genomics, transcriptomics, proteomics, and metabolomics) assays and translate them into systems biology knowledge and, eventually, therapeutics, including countermeasures to support life in space. Phase I of the project (completed) emphasized developing key capabilities for submission, curation, storage, search, and retrieval of omics data from biomedical research in and of space environments. The development focus for Phase II (completed) was federated data search and retrieval of these kinds of data from other open-access repositories. The last phase of the project (in work) entails developing an omics analysis tool set, and a portal to visualize processed omics data, emphasizing integration with the data repository and search functions developed during the prior phases. The final product will be an open-access system where users can individually or collaboratively publish, search, integrate, analyze, and visualize omics data.
Sustainable missions, beyond low Earth orbit, will require autonomous capabilities in order to achieve NASA’s Artemis program objectives. Correspondingly, the crew must have a means to efficiently interact with these autonomous systems; this can be facilitated via voice and speech communications. Voice-based controls enable the user to access autonomous systems hands-free/eyes-free, allowing the user to better focus on critical tasks. The goal of this project was to explore the knowledge and technology needed to successfully design effective voice interfaces for autonomous systems. The main objective was to understand how a crew member, through voice interaction, could most efficiently and intuitively communicate with a notional autonomous vehicle system manager. This project leveraged prior research conducted by the University of Michigan’s Bioastronautics and Life Support System (BLiSS) team as part of a NASA Moon to Mars eXploration Systems and Habitation (M2M X-Hab) 2020 Academic Innovation Challenge. The X-Hab 2020 work from the BliSS Team resulted in an intuitive graphical user interface/user experience that was built on an Internet of Things (IOT) platform. The Voice User Interface (VUI) design for the M2M X-Hab 2021 project leveraged this technology and incorporated a voice-based assistant and NASA’s Platform for Autonomous Systems (NPAS) software. This required technologies to convert voice to text, conduct semantic interpretations, and convert responses from the autonomous system to text and to speech; additionally, the background noise environment of spacecraft was assessed, and a relatable personality for the autonomous system to facilitate human-like conversations was created. This work’s success was largely due to the diverse team that included expertise in Space Systems Engineering, Human Computer Interaction, Aerospace Engineering, Computer Science, Biomedical Engineering, and Applied Physics. The differing perspectives fostered elaborate discussions, resulting in the conception of three main interactions: (1) User-System, (2) NPAS-System, and (3) Environment-System. The system developed, i.e. the VUI, had to be unique, efficient, and intuitive; thus, the team crafted a personality for the system to enable human-like conversation. User surveys sent to students and young professionals were used to help determine these personality traits by capturing perspectives and expectations of the “Artemis Generation Astronauts”. To further simulate human-like conversations, the system had to be able to quickly interpret user speech and be able to integrate with NASA’s NPAS system for quick and reliable information transfer. Results of this research include (1) a working prototype user interface, that is compatible with NASA’s NPAS system; (2) software that demonstrates the ability to interpret user requests and respond appropriately; (3) the capability to implement fully expanded conversations between user and system using intuitive communication in four request categories; and (4) software and hardware recommendations that optimize the system’s ability to operate, i.e. be heard, in a noisy environment. The technologies chosen for this project’s demonstrations included the following: Raspberry Pi, RASA, Mozilla Deep Speech, Coqui, RTX Voice and Adobe XD. This work has laid the foundation for the development of VUI’s used for autonomy, and is intended to provide guidance for future VUI development.
The sustainable missions beyond Low Earth Orbit (LEO) envisioned for NASA’s Artemis program will require autonomous capabilities. Moreover, Artemis mission crews will need a means to efficiently interact with a spacecraft’s autonomous systems. This interaction can be facilitated by voice and speech communications because voice-based controls enable users to interact hands- and eyes-free, allowing the user to better focus on critical tasks. The goal of our project was to explore the knowledge and technology needed to successfully design effective Voice User Interfaces (VUIs) for autonomous systems utilizing Human Centered Design (HCD) principles. The focus of the human factors’ aspect of engineering, pays close attention to psychological and physiological principles in the development of autonomous crew operation systems. A main objective was to understand how a crew member, through voice interaction, could efficiently and intuitively communicate with a notional autonomous vehicle system manager. This project was a part of the NASA Moon to Mars eXploration Systems and Habitation (M2M X-Hab) 2020 Academic Innovation Challenge. The work from the BLiSS Team, at the University of Michigan, resulted in the design of a system persona, Diego, to which an astronaut may quickly build trust with autonomous systems, to alleviate known stressors on mental health expected during long duration space missions. Optimal software to facilitate integration of the system persona into a reference Lunar orbiting Gateway station was defined. Additionally, a Speech to Text (STT) system and a Graphical User Interface (GUI) that could be implemented in future missions was developed on an Internet of Things (IOT) platform. The Voice User Interface (VUI) design for the M2M X-Hab 2020 project leveraged previous technology developed by the BLiSS team to incorporate a voice-based interface into NASA’s Platform for Autonomous Systems (NPAS) software. This required technologies to convert voice to text, conduct semantic interpretations, and convert responses from the autonomous system to text and to speech; additionally, the spacecraft background noise environment was assessed, a noise mitigation technique was developed, and a relatable personality for the autonomous system was developed in order to facilitate human-like conversations. The success of our effort was largely due to the diversity of the team that included expertise in Space Systems Engineering, Human Computer Interaction, Aerospace Engineering, Computer Science, Biomedical Engineering, and Applied Physics. The diverse perspectives fostered elaborate discussions, resulting in the conception of three main subsystems: (1) User-System, (2) NPAS-System, and (3) Environment-System. The VUI was unique and had to be efficient and intuitive. For this project, 5 subteams were formed, each with a separate objective, Voice Design team, Background Noise Mitigation team, Software Integration team and Graphical User Interface team. The BLiSS team crafted a personality for the VUI to enable human-like conversation and drive user adoption and trust. User surveys were completed and used to help determine the required VUI system personality traits by capturing perspectives and expectations of prospective “Artemis Generation Astronauts”. To further simulate human-like conversations, the system had to be able to quickly interpret user speech and be able to integrate with NASA’s NPAS platform for quick and reliable information transfer. The outcomes of our research were: (1) a working prototype user interface, that is compatible with NASA’s NPAS platform; (2) software that demonstrates the ability of the VUI system to interpret user requests and respond appropriately; (3) the capability to implement fully expanded conversations between user and system using intuitive communication in four request categories; and (4) software and hardware recommendations that optimize the system’s ability to operate in a noisy environment. Our research has laid the foundation for the development of VUI’s for autonomy, and provides a baseline for future VUI developments.