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Cascade Distillation System Design for Safety and Mission Assurance

Per the NASA Human Health, Life Support and Habitation System Technology Area 06 report "crewed missions venturing beyond Low-Earth Orbit (LEO) will require technologies with improved reliability, reduced mass, self-sufficiency, and minimal logistical needs as an emergency or quick-return option will not be feasible." To meet this need, the development team of the second generation Cascade Distillation System (CDS 2.0) opted a development approach that explicitely incorporate consideration of safety, mission assurance, and autonomy. The CDS 2.0 prelimnary design focused on establishing a functional baseline that meets the CDS core capabilities and performance. The critical design phase is now focused on incorporating features through a deliberative process of establishing the systems failure modes and effects, identifying mitigative strategies, and evaluating the merit of the proposed actions through analysis and test. This paper details results of this effort on the CDS 2.0 design.

Sargusingh, Miriam J.↗

Cascade Distillation System Design for Safety and Mission Assurance

Per the NASA Human Health, Life Support and Habitation System Technology Area 06 report "crewed missions venturing beyond Low-Earth Orbit (LEO) will require technologies with improved reliability, reduced mass, self-sufficiency, and minimal logistical needs as an emergency or quick-return option will not be feasible".1 To meet this need, the development team of the second generation Cascade Distillation System (CDS 2.0) chose a development approach that explicitly incorporate consideration of safety, mission assurance, and autonomy. The CDS 2.0 preliminary design focused on establishing a functional baseline that meets the CDS core capabilities and performance. The critical design phase is now focused on incorporating features through a deliberative process of establishing the systems failure modes and effects, identifying mitigation strategies, and evaluating the merit of the proposed actions through analysis and test. This paper details results of this effort on the CDS 2.0 design.

Sarguisingh, Miriam↗

Resilient Autonomy in the Face of Adversity

The NASA Resilient Autonomy Project developed a software framework that implemented a Run Time Assurance (RTA) architecture that leveraged ASTM International’s F3269 Industry Standard for safely bounding complex behavior in aircraft. This framework was called the Expandable Variable Autonomy Architecture, or EVAA. EVAA was developed during the height of the Covid-19 lockdown that caused the Resilient Autonomy team to pivot from flight test to distributed simulator testing. EVAA was developed to be platform and mission agnostic where platform specifics were behind a hardware abstraction layer that EVAA called a Coupler. EVAA was able to host multiple safety monitors that could resolve individual safety hazards. EVAA was able to resolve priority conflicts when multiple safety hazards needed to be resolved simultaneously and was able to resolve highly complex situations in a safe manner that could exceed human capabilities.

Ethan Williams↗

Sensor Data Qualification for Autonomous Operation of Space Systems

NASA's new Exploration initiative for both robotic and manned missions will require higher levels of reliability, autonomy and reconfiguration capability to make the missions safe, successful and affordable. Future systems will require diagnostic reasoning to assess the health of the system in order to maintain the system s functionality. The diagnostic reasoning and assessment will involve data qualification, fault detection, fault isolation and remediation control. A team of researchers at the NASA Glenn Research Center is currently working on a Sensor Data Qualification (SDQ) system that will support these critical evaluation processes, for both automated and human-in-the-loop applications. Data qualification is required as a first step so that critical safety and operational decisions are based on good data. The SDQ system would monitor a network of related sensors to determine the health of individual sensors within that network. Various diagnostic systems such as the Caution and Warning System would then use the sensor health information with confidence. The proposed SDQ technology will be demonstrated on a variety of subsystems that are relevant to NASA s Exploration systems, which currently include an electrical power system and a cryogenic fluid management system. The focus of this paper is the development and demonstration of a SDQ application for a prototype power distribution unit that is representative of a Crew Exploration Vehicle electrical power system; this provides a unique and relevant environment in which to demonstrate the feasibility of the SDQ technology.

Maul, William A.↗

Adjustable Autonomy and Human-Agent Teamwork in Practice: An Interim Report on Space Applications

We give a preliminary perspective on the basic principles and pitfalls of adjustable autonomy and human-centered teamwork. We then summarize the interim results of our study on the problem of work practice modeling and human-agent collaboration in space applications, the development of a broad model of human-agent teamwork grounded in practice, and the integration of the Brahms, KAoS, and NOMADS agent frameworks. We hope our work will benefit those who plan and participate in work activities in a wide variety of space applications, as well as those who are interested in design and execution tools for teams of robots that can function as effective assistants to humans.

Bradshaw, Jeffrey M.↗

Coordinated NASA Development of Medical Capabilities and Technologies for Deep Space Exploration

Building upon the success of the last two decades of human spaceflight medical operations onboard the International Space Station (ISS), NASA now must start to evolve to a deep space medical operations paradigm. This new paradigm will include myriad challenges including communication delays and blackouts, infrequent or unavailable resupply, potentially significant vehicle resource constraints, and delayed or non-existent evacuation options. To overcome these challenges, two separate but highly coordinated NASA groups are working together to develop and test solutions to these problems: the Exploration Medical Capability (ExMC) Element of the NASA Human Research Program and the Exploration Medical Integrated Product Team (XMIPT) of the Environmental Control and Life Support System – Crew Health and Performance Systems Capability Leadership Team. Although the work of these two groups is very complementary, ExMC generally focuses more on research and early technology development work while the XMIPT works on maturing and integrating new medical capabilities for exploration. During this panel, these two groups together will present details on how medical capability gaps are defined and addressed, a new work plan in development to increase medical autonomy in spaceflight, a technology maturation effort to generate intravenous fluids from potable water onboard a spacecraft, multiple new technology demonstrations of in-situ laboratory analysis capabilities, and new onboard tools to advance medical care for deep space missions.

Kris Lehnhardt↗

Earth-Independent Medical Operations (EIMO) Concept of Operations

In contrast to the current crew health paradigm for low-Earth orbit and Lunar missions, which depends on real-time communication with Mission Control, deep-space exploration missions will require a significant shift in medical operations. This shift is driven by the constraints of operating at a considerable distance from Earth, such as resource limitations—lack of resupply, restricted mass, power, volume, and data—as well as communication delays and the inability to evacuate back to Earth during emergencies. To move toward a more self-reliant medical model, a strategy is needed to gradually increase space-based crew autonomy and reduce risks to mission success in the challenging environment of deep space. This transformative change, known as "Earth-Independent Medical Operations" (EIMO), explores the gradual transfer of medical care and decision-making from Earth-based support to space-based systems. The goal of this transition is to enhance astronaut health and performance while minimizing mission risks. EIMO requires the development of a medical system that integrates seamlessly with mission planning, vehicle and spacesuit design, and data architecture. This integration is crucial for building a robust medical infrastructure that not only safeguards astronaut well-being but also ensures overall mission success. The Human Research Program (HRP) Exploration Medical Capability (ExMC) Element has revised the EIMO model-based Concept of Operations (ConOps) which outlines an initial vision for EIMO. The ConOps, which is built on the stakeholders’ need, system goals, and objectives (NGOs), presents an array of in-mission scenarios that span a wide range of medical conditions demonstrating the system’s capabilities from basic to complex events. Developed by a multidisciplinary team of systems engineers, scientists, and clinicians within ExMC, the ConOps revision includes two new scenarios(Barotrauma and Self-Medical Management and Behavioral Health and Chronic Medical Care), and implementation of findings from EIMO technical interchange meetings that focused on data and training. The envisioned EIMO Medical System (MS) operates as a system of systems, gathering data from various sources such as reference databases, real-time wearable sensors, point-of-care diagnostics, and environmental controls. The MS also incorporates advanced training tools to support autonomous medical care, assisting the Crew Medical Officer (CMO) during medical events where Ground Support is either unavailable or communication-delayed beyond practicality. Furthermore, MS functions and capabilities were decomposed from the scenarios to establish foundational requirements for EIMO and traced to the NASA Spaceflight Human-System Standard(NASA-STD-3001, Volumes 1 and 2). These traces were performed to gain insights on the alignment of EIMO requirements with the NASA standard. This work serves as an initial recommendation to increase crew autonomy gradually and safely for Mars missions and future deep-space exploration.

medical system↗

Enabling Spacecraft Formation Flying in Any Earth Orbit Through Spaceborne GPS and Enhanced Autonomy Technologies

Formation flying is quickly revolutionizing the way the space community conducts autonomous science missions around the Earth and in space. This technological revolution will provide new, innovative ways for this community to gather scientific information, share this information between space vehicles and the ground, and expedite the human exploration of space. Once fully matured, this technology will result in swarms of space vehicles flying as a virtual platform and gathering significantly more and better science data than is possible today. Formation flying will be enabled through the development and deployment of spaceborne differential Global Positioning System (GPS) technology and through innovative spacecraft autonomy techniques, This paper provides an overview of the current status of NASA/DoD/Industry/University partnership to bring formation flying technology to the forefront as quickly as possible, the hurdles that need to be overcome to achieve the formation flying vision, and the team's approach to transfer this technology to space. It will also describe some of the formation flying testbeds, such as Orion, that are being developed to demonstrate and validate these innovative GPS sensing and formation control technologies.

Bauer, F. H.↗

Promoting Crew Autonomy: Current Advances and Novel Techniques

Since the dawn of the era of human space flight, mission control centers around the world have played an integral role in guiding space travelers toward mission success. In the International Space Station (ISS) program, astronauts and cosmonauts have the benefit of near constant access to the expertise and resources within mission control, as well as lifeboat capability to quickly return to Earth if something were to go wrong. As we move into an era of longer duration missions to more remote locations, rapid and ready access to mission control on earth will no longer be feasible. To prepare for such missions, long duration crews must be prepared to operate more autonomously, and the mission control paradigm that has been successfully employed for decades must be re-examined. The team at NASA's Payload Operations and Integration Center (POIC) in Huntsville, Alabama is playing an integral role in the development of concepts for a more autonomous long duration crew of the future via research on the ISS.

Harris, Samantha↗

Crew Health and Performance Integrated Data Architecture (CHP-IDA) TechPort May 2024

Future exploration missions to Mars will have increased need for crew autonomy. Crew Health & Performance (CHP) related data on the ISS is currently, manually downlinked and in disparate locations, which limits crew autonomy for future missions. The CHP-IDA project is developing a backend data system platform that grants the ability to seamlessly collect, store, process, and display CHP-related data for exploration missions. This platform allows for integration of data and advanced analytics that offer crew and ground teams better insight into the crew’s health and performance. It also enables applications that can improve the crew’s ability to provide more autonomous medical care during exploration missions. Data will be collected automatically to reduce crew and ground team time and effort and will synchronize across all in-mission vehicles, habitats, and ground as communication delay permits. The Human Research Program’s (HRP) Medical Data Architecture (MDA) project focused on this backend data architecture but for medical data only. The CHP-IDA project, a joint effort between HRP’s Exploration Medical Capability (ExMC) element and the Exploration Medical Integrated Product Team (XMIPT), expands this capability to all relevant CHP-related data. The additional inputs from nutrition, environment, exercise, radiation, and any other relevant sources will give more insight into crew’s health and performance. Currently, the Human Systems Engineering and Integration Division at Johnson Space Center (JSC) is designing the system. The team completed a system requirements review (SRR) in FY22 and now the focus is on core software development, testbed buildup, and use case scenario demonstration. An end-to-end demonstration with multiple data sources across CHP domains is schedule for the end of FY24 where all three focus areas will be displayed. Following this ground demo, the software will be completed, tested, and validated for flight.

Courtney M Schkurko↗

What Has ExMC Systems Engineering Been Up To Since Last IWS?

Long duration Lunar and Martian missions will change the way NASA currently practices medicine. The missions will require more autonomous capability compared to current low Earth orbit operations. For the medical system, lack of consumable resupply, evacuation opportunities, and real-time ground support are key drivers toward greater autonomy. Recognition of the limited mission and vehicle resources available to carry out exploration missions motivates the Exploration Medical Capability (ExMC) Element’s approach to enabling the necessary autonomy. This element promotes human health and performance in space by advancing medical systems design and risk-informed decision-making for long-duration deep-space exploration missions (LDEMs). ExMC is using system engineering processes and Model-Based System Engineering (MBSE) tools to identify the user needs and requirements of LDEM medical systems. The MBSE approach to medical system design offers a paradigm shift toward greater integration between the vehicle and the medical system, and directly supports the transition of Earth-reliant International Space Station operations to the Earth-independent operations envisioned for LDEMs. This talk will provide a high-level overview of what the ExMC SE team has accomplished since the last IWS, an introduction to upcoming SE talks, and the ongoing systems engineering work.

K. McGuire↗

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

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

Verification and Validation↗

A Distributed Simulation-to-Flight Framework to Support Investigating Trust/Trustworthiness in Multi-Agent Systems

As autonomous systems continue to grow both in use and complexity, the necessity for robust and extensible simulation-to-flight frameworks is paramount for establishing an effective architecture for autonomous systems. Hardware test flights are time-consuming and cost prohibitive during early system design and development. Simulation environments can be useful tools to accelerate algorithm development and testing. However, transitions from simulation to flight (sim-to-flight) can be challenging, unless systems are designed with this transition in mind and with the necessary capabilities built into the architecture and framework. One of the objectives of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) was to design and develop a distributed mixed-reality simulation environment to begin establishing a basis for certification of autonomous systems via research into trust and trustworthiness. ATTRACTOR’s objective was to construct computational concepts of trustworthiness and justifiable trust in multi-agent autonomous teams, to inform future certification of safety-critical and time-critical autonomous systems in aviation. In this paper, we present an autonomous systems architecture and development framework paired with a persistent distributed modeling and simulation (ModSim) environment for test and evaluation of autonomous systems. They were designed under ATTRACTOR in order to measure and establish trustworthiness and trust in single-and multi-agent human-machine systems whether these machines are fixed-wing general aviation, rotary-wing Unmanned Aerial Vehicles (UAVs), ground rovers, or even spacecraft. The Autonomous Entity Operational Network (AEON) framework enables autonomous system development with an easily extensible collection of libraries and plug-n-play nodes facilitated by the Data Distribution Service (DDS) communication protocol standard. The Baseline Environment for Autonomous Modeling (BEAM) simulation environment is a distributed mixed-reality Unity™-based environment built around the same DDS communication paradigm allowing for easy integration with AEON-based autonomous applications, enabling sim-to-flight with minimal configuration changes. Using AEON and BEAM, source code that runs in simulation ports directly to hardware and has successfully flown in the National Airspace System (NAS) at NASA LaRC many times over the lifetime of ATTRACTOR.

Benjamin N Kelley↗

Surface Habitat Systems

The Surface Habitat Systems (SHS) Focused Investment Group (FIG) is part of the National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC) effort to provide a focused direction and funding to the various projects that are working on human surface habitat designs and technologies for the planetary exploration missions. The overall SHS-FIG effort focuses on directing and guiding those projects that: 1) develop and demonstrate new surface habitat system concepts, innovations, and technologies to support human exploration missions, 2) improve environmental systems that interact with human habitats, 3) handle and emplace human surface habitats, and 4) focus on supporting humans living and working in habitats on planetary surfaces. The activity areas of the SHS FIG described herein are focused on the surface habitat project near-term objectives as described in this document. The SHS-FIG effort focuses on mitigating surface habitat risks (as identified by the Lunar Surface Systems Project Office (LSSPO) Surface Habitat Element Team; and concentrates on developing surface habitat technologies as identified in the FY08 gap analysis. The surface habitat gap assessment will be updated annually as the surface architecture and surface habitat definition continues to mature. These technologies are mapped to the SHS-FIG Strategic Development Roadmap. The Roadmap will bring to light the areas where additional innovative efforts are needed to support the development of habitat concepts and designs and the development of new technologies to support of the LSSPO Habitation Element development plan. Three specific areas of development that address Lunar Architecture Team (LAT)-2 and Constellation Architecture Team (CxAT) Lunar habitat design issues or risks will be focused on by the SHS-FIG. The SHS-FIG will establish four areas of development that will help the projects prepare in their planning for surface habitat systems development. Those development areas are the 1) surface habitat concept definition, 2) inflatable surface habitat development, and 3) autonomous habitat operations, and 4) cross-cutting / systems engineering. In subsequent years, the SHS-FIG will solicit a call for innovations and technologies that will support the development of these four development areas. The other development areas will be assessed yearly and identified on the SHS-FIG s Strategic Development Roadmap. Initial investment projects that are funded by the Constellation Program Office (CxPO), LSSPO, or the Exploration Technology Development Projects (ETDP) will also be included on the Roadmap. For example, in one or two years from now, the autonomous habitat operations and testbed would collaborations with the Integrated Systems Health Management (ISHM) and Automation for Operations ETDP projects, which will give the surface habitat projects an integrated habitat autonomy testbed to test software and systems. The SHS-FIG scope is to provide focused direction for multiple innovations, technologies and subsystems that are needed to support humans at a remote planetary surface habitat during the concept development, design definition, and integration phases of that project. Subsystems include: habitability, lightweight structures, power management, communications, autonomy, deployment, outfitting, life support, wireless connectivity, lighting, thermal and more.

Kennedy, Kriss J.↗

Risk of Performance and Behavioral Health Decrements Due to Inadequate Cooperation, Coordination, Communication, and Psychosocial Adaptation within a Team

A team is defined as: "two or more individuals who interact socially and adaptively, have shared or common goals, and hold meaningful task interdependences; it is hierarchically structured and has a limited life span; in it expertise and roles are distributed; and it is embedded within an organization/environmental context that influences and is influenced by ongoing processes and performance outcomes" (Salas, Stagl, Burke, & Goodwin, 2007, p. 189). From the NASA perspective, a team is commonly understood to be a collection of individuals that is assigned to support and achieve a particular mission. Thus, depending on context, this definition can encompass both the spaceflight crew and the individuals and teams in the larger multi-team system who are assigned to support that crew during a mission. The Team Risk outcomes of interest are predominantly performance related, with a secondary emphasis on long-term health; this is somewhat unique in the NASA HRP in that most Risk areas are medically related and primarily focused on long-term health consequences. In many operational environments (e.g., aviation), performance is assessed as the avoidance of errors. However, the research on performance errors is ambiguous. It implies that actions may be dichotomized into "correct" or "incorrect" responses, where incorrect responses or errors are always undesirable. Researchers have argued that this dichotomy is a harmful oversimplification, and it would be more productive to focus on the variability of human performance and how organizations can manage that variability (Hollnagel, Woods, & Leveson, 2006) (Category III1). Two problems occur when focusing on performance errors: 1) the errors are infrequent and, therefore, difficult to observe and record; and 2) the errors do not directly correspond to failure. Research reveals that humans are fairly adept at correcting or compensating for performance errors before such errors result in recognizable or recordable failures. Astronauts are notably adept high performers. Most failures are recorded only when multiple, small errors occur and humans are unable to recognize and correct or compensate for these errors in time to prevent a failure (Dismukes, Berman, Loukopoulos, 2007) (Category III). More commonly, observers record variability in levels of performance. Some teams commit no observable errors but fail to achieve performance objectives or perform only adequately, while other teams commit some errors but perform spectacularly. Successful performance, therefore, cannot be viewed as simply the absence of errors or the avoidance of failure Johnson Space Center (JSC) Joint Leadership Team, 2008). While failure is commonly attributed to making a major error, focusing solely on the elimination of error(s) does not significantly reduce the risk of failure. Failure may also occur when performance is simply insufficient or an effort is incapable of adjusting sufficiently to a contextual change (e.g., changing levels of autonomy).

Landon, Lauren Blackwell↗

Serious Gaming for Building a Basis of Certification via Trust and Trustworthiness of Autonomous Systems

Autonomous systems governed by a variety of adaptive and nondeterministic algorithms are being planned for inclusion into safety-critical environments, such as unmanned aircraft and space systems in both civilian and military applications. However, until autonomous systems are proven and perceived to be capable and resilient in the face of unanticipated conditions, humans will be reluctant or unable to delegate authority, remaining in control aided by machine-based information and decision support. Proving capability, or trustworthiness, is a necessary component of certification. Perceived capability is a component of trust. Trustworthiness is an attribute of a cyber-physical system that requires context-driven metrics to prove and certify. Trust is an attribute of the agents participating in the system and is gained over time and multiple interactions through trustworthy behavior and transparency. Historically, artificial intelligence and machine learning systems provide answers without explanation - without a rationale or insight into the machine “thinking”. In order to function as trusted teammates, machines must be able to explain their decisions and actions. This transparency is a product of both content and communication. NASA’s Autonomy Teaming & TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project seeks to build a basis for certification of autonomous systems via establishing metrics for trustworthiness and trust in multi-agent team interactions, using AI (Artificial Intelligence) explainability and persistent modeling and simulation, in the context of mission planning and execution, with analyzable trajectories. Inspired by Massively Multiplayer Online Role Playing Games (MMORPG) and Serious Gaming, the proposed ATTRACTOR modeling and simulation environment is similar to online gaming environments in which player (aka agent) participants interact with each other, affect their environment, and expect the simulation to persist and change regardless of any individual agent’s active participation. This persistent simulation environment will accommodate individual agents, groups of self-organizing agents, and large-scale infrastructure behavior. The effects of the emerging adaptation and coevolution can be observed and measured to building a basis of measurable trustworthiness and trust, toward certification of safety-critical autonomous systems.

Allen, B. Danette↗

Exploration Medical Capability System Engineering Overview

Deep Space Gateway and Transport missions will change the way NASA currently practices medicine. The missions will require more autonomous capability compared to current low Earth orbit operations. For the medical system, lack of consumable resupply, evacuation opportunities, and real-time ground support are key drivers toward greater autonomy. Recognition of the limited mission and vehicle resources available to carry out exploration missions motivates the Exploration Medical Capability (ExMC) Element's approach to enabling the necessary autonomy. The ExMC Systems Engineering team's mission is to "Define, develop, validate, and manage the technical system design needed to implement exploration medical capabilities for Mars and test the design in a progression of proving grounds." The Element's work must integrate with the overall exploration mission and vehicle design efforts to successfully provide exploration medical capabilities. ExMC is using Model-Based System Engineering (MBSE) to accomplish its integrative goals. The MBSE approach to medical system design offers a paradigm shift toward greater integration between vehicle and the medical system, and directly supports the transition of Earth-reliant ISS operations to the Earth-independent operations envisioned for Mars. This talk will discuss how ExMC is using MBSE to define operational needs, decompose requirements and architecture, and identify medical capabilities needed to support human exploration. How MBSE is being used to integrate across disciplines and NASA Centers will also be described. The medical system being discussed in this talk is one system within larger habitat systems. Data generated within the medical system will be inputs to other systems and vice versa. This talk will also describe the next steps in model development that include: modeling the different systems that comprise the larger system and interact with the medical system, understanding how the various systems work together, and developing tools to support trade studies.

Mindock, J.↗

Exploration Medical Cap Ability System Engineering Overview

Deep Space Gateway and Transport missions will change the way NASA currently practices medicine. The missions will require more autonomous capability compared to current low Earth orbit operations. For the medical system, lack of consumable resupply, evacuation opportunities, and real-time ground support are key drivers toward greater autonomy. Recognition of the limited mission and vehicle resources available to carry out exploration missions motivates the Exploration Medical Capability (ExMC) Element's approach to enabling the necessary autonomy. The ExMC Systems Engineering team's mission is to "Define, develop, validate, and manage the technical system design needed to implement exploration medical capabilities for Mars and test the design in a progression of proving grounds." The Element's work must integrate with the overall exploration mission and vehicle design efforts to successfully provide exploration medical capabilities. ExMC is using Model-Based System Engineering (MBSE) to accomplish its integrative goals. The MBSE approach to medical system design offers a paradigm shift toward greater integration between vehicle and the medical system, and directly supports the transition of Earth-reliant ISS operations to the Earth-independent operations envisioned for Mars. This talk will discuss how ExMC is using MBSE to define operational needs, decompose requirements and architecture, and identify medical capabilities needed to support human exploration. How MBSE is being used to integrate across disciplines and NASA Centers will also be described. The medical system being discussed in this talk is one system within larger habitat systems. Data generated within the medical system will be inputs to other systems and vice versa. This talk will also describe the next steps in model development that include: modeling the different systems that comprise the larger system and interact with the medical system, understanding how the various systems work together, and developing tools to support trade studies.

McGuire, K.↗