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NASA NTRS2007

Dryden Flight Research Center Overview

This viewgraph document presents a overview of the Dryden Flight Research Center's facilities. Dryden's mission is to advancing technology and science through flight. The mission elements are: perform flight research and technology integration to revolutionize aviation and pioneer aerospace technology, validate space exploration concepts, conduct airborne remote sensing and science observations, and support operations of the Space Shuttle and the ISS for NASA and the Nation. It reviews some of the recent research projects that Dryden has been involved in, such as autonomous aerial refueling, the"Quiet Spike" demonstration on supersonic F-15, intelligent flight controls, high angle of attack research on blended wing body configuration, and Orion launch abort tests.

Meyer, Robert R., Jr.↗

NASA NTRS2018

BioSentinel: An Adaptable Platform for Studying the Biological Effects of Deep Space Radiation

NASA's BioSentinel mission is a 6U nanosatellite with autonomous life support that will utilize the budding yeast Saccharomyces cerevisiae to study the DNA damage response to the deep space radiation environment. BioSentinel is planned to launch in 2019 as a secondary payload on the Space Launch System's first Exploration Mission (EM-1), and will undergo a lunar fly-by and enter heliocentric orbit after deployment. As the first biological mission beyond Low Earth Orbit (LEO) in nearly half a century, this mission will help fill critical gaps in knowledge about the effects of uniquely composed, chronic, low-flux deep space radiation on biological systems. Yeast is well-suited for this mission due to its desiccation tolerance and space-flight heritage. As a eukaryotic model organism, it also serves as a robust analog for human cells. Data gathered on this mission will thus inform us of the hazards involved in long-duration human exploration in deep space, and the protections necessary to mitigate them. Due to its low-cost, flexible and advanced technology, the 4U BioSensor payload contained within the nanosatellite is adaptable to other model microorganisms, exploration platforms and environments relevant to human exploration, such as the ISS, the Lunar Orbital Platform - Gateway and future lunar landers. In order to query the DNA damage response to deep space radiation, BioSentinel contains a wild type yeast strain as a positive control, and a radiation sensitive rad51 mutant strain that is defective for DNA repair. Yeast cells are desiccated in microfluidic cards, and rehydrated with growth medium and metabolic indicator dye at the desired time points during the mission. A thermal control system supports these stasis and growth states, and an optical system continuously measures cell growth and metabolism. An onboard radiation spectrometer and dosimeter allows us to correlate the dose, energy and particle-type of deep space radiation to the biological response. Data received from the deep space biosensor will be compared to control payloads on Earth and the ISS. Ongoing science testing for the BioSentinel project includes optimization for cell viability, desiccation tolerance, and long-term biocompatibility, as well as radiation experiments to understand the sensitivity and responsiveness of cells to varying radiation doses and particle types.

BioSentinel↗

NASA NTRS2005

Monitoring Floods with NASA's ST6 Autonomous Sciencecraft Experiment: Implications on Planetary Exploration

NASA's New Millennium Program (NMP) Autonomous Sciencecraft Experiment (ASE) [1-3] has been successfully demonstrated in Earth-orbit. NASA has identified the development of an autonomously operating spacecraft as a necessity for an expanded program of missions exploring the Solar System. The versatile ASE spacecraft command and control, image formation, and science processing software was uploaded to the Earth Observer 1 (EO-1) spacecraft in early 2004 and has been undergoing onboard testing since May 2004 for the near real-time detection of surface modification related to transient geological and hydrological processes such as volcanism [4], ice formation and retreat [5], and flooding [6]. Space autonomy technology developed as part of ASE creates the new capability to autonomously detect, assess, react to, and monitor dynamic events such as flooding. Part of the challenge has been the difficulty to observe flooding in real time at sufficient temporal resolutions; more importantly, it is the large spatial extent of most drainage networks coupled with the size of the data sets necessary to be downlinked from satellites that make it difficult to monitor flooding from space. Below is a description of the algorithms (referred to as ASE Flood water Classifiers) used in tandem with the Hyperion spectrometer instrument on EO-1 to identify flooding and some of the test results.

Ip, Felipe↗

NASA NTRS2004

Autonomous Multi-Sensor Coordination: The Science Goal Monitor

Many dramatic earth phenomena are dynamic and coupled. In order to fully understand them, we need to obtain timely coordinated multi-sensor observations from widely dispersed instruments. Such a dynamic observing system must include the ability to Schedule flexibly and react autonomously to sciencehser driven events; Understand higher-level goals of a sciencehser defined campaign; Coordinate various space-based and ground-based resources/sensors effectively and efficiently to achieve goals. In order to capture transient events, such a 'sensor web' system must have an automated reactive capability built into its scientific operations. To do this, we must overcome a number of challenges inherent in infusing autonomy. The Science Goal Monitor (SGM) is a prototype software tool being developed to explore the nature of automation necessary to enable dynamic observing. The tools being developed in SGM improve our ability to autonomously monitor multiple independent sensors and coordinate reactions to better observe dynamic phenomena. The SGM system enables users to specify what to look for and how to react in descriptive rather than technical terms. The system monitors streams of data to identify occurrences of the key events previously specified by the scientisther. When an event occurs, the system autonomously coordinates the execution of the users' desired reactions between different sensors. The information can be used to rapidly respond to a variety of fast temporal events. Investigators will no longer have to rely on after-the-fact data analysis to determine what happened. Our paper describes a series of prototype demonstrations that we have developed using SGM and NASA's Earth Observing-1 (EO-1) satellite and Earth Observing Systems' Aqua/Terra spacecrafts' MODIS instrument. Our demonstrations show the promise of coordinating data from different sources, analyzing the data for a relevant event, autonomously updating and rapidly obtaining a follow-on relevant image. SGM was used to investigate forest fires, floods and volcanic eruptions. We are now identifying new Earth science scenarios that will have more complex SGM reasoning. By developing and testing a prototype in an operational environment, we are also establishing and gathering metrics to gauge the success of automating science campaigns.

Koratkar, Anuradha↗

NASA NTRS1994

TDRSS Onboard Navigation System (TONS) flight qualification experiment

The National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) is currently developing an operational Tracking and Data Relay Satellite (TDRS) System (TDRSS) Onboard Navigation System (TONS) to provide realtime, autonomous, high-accuracy navigation products to users of TDRSS. A TONS experiment was implemented on the Explorer Platform/Extreme Ultraviolet Explorer (EP/EUVE) spacecraft, launched June 7, 1992, to flight qualify the TONS operational system using TDRSS forward-link communications services. This paper provides a detailed evaluation of the flight hardware, an ultrastable oscillator (USO) and Doppler extractor (DE) card in one of the TDRSS user transponders and the ground-based prototype flight software performance, based on the 1 year of TONS experiment operation. The TONS experiment results are used to project the expected performance of the TONS 1 operational system. TONS 1 processes Doppler data derived from scheduled forward-link S-band services using a sequential estimation algorithm enhanced by a sophisticated process noise model to provide onboard orbit and frequency determination and time maintenance. TONS 1 will be the prime navigation system on the Earth Observing System (EOS)-AM1 spacecraft, currently scheduled for launch in 1998. Inflight evaluation of the USO and DE short-term and long-term stability indicates that the performance is excellent. Analysis of the TONS prototype flight software performance indicates that realtime onboard position accuracies of better than 25 meters root-mean-square are achievable with one tracking contact every one to two orbits for the EP/EUVE 525-kilometer altitude, 28.5 degree inclination orbit. The success of the TONS experiment demonstrates the flight readiness of TONS to support the EOS-AM1 mission.

Gramling, C. J.↗

NASA NTRSDate not supplied

Evaluation of South Dakota Aggregate as Martian Simulants

This research investigates aggregate sources in South Dakota to simulate martian aggregate for use in future martian concrete infrastructure. This effort supports the Moon to Mars Planetary Autonomous Construction Technology (MMPACT) project lead by the National Aeronautics and Space Administration (NASA). Currently, martian simulants are not produced in large quantities or from many geologic sources. Previous work has primarily focused on tholeiitic basalt even though the martian surface has diverse geology especially in crater locations. Various landing sites were selected to explore martian regolith compositions and textural characteristics. From these results, it was determined that locations with basaltic andesite would best match geological sources in South Dakota. These local regolith sources were then collected and characterized. A comparison between the local and martian aggregate is provided. Ultimately, this work will contribute to binder production for Additive Construction using In-Situ Resource Utilization (ISRU) processing for surface construction.

Aggregate↗

DOE OSTI2025

Machine learning approaches for intentional materials engineering

In this article, the development of nanoporous metals and metallic composites through dealloying processes presents significant opportunities in materials engineering. However, designing multicomponent precursor alloys and establishing corresponding processing methods that yield predictable compositions and nanostructures remain a complex challenge. This article explores how machine learning (ML)-augmented computational and experimental methodologies can tackle these challenges by predicting precursor alloy compositions, final nanoporous structures, and mechanical properties, while integrating ML-enabled autonomous experimentation for material design and quantification. We highlight recent advancements in applying ML to nanostructured materials design via dealloying and discuss how techniques from other nanomaterial designs can be adapted for improved control over morphological and compositional outcomes in nanoporous and nanocomposite materials. Furthermore, we explore the role of ML in autonomous synchrotron x-ray experimentation, enabling real-time feedback between modeling and experimental setups. ML-driven approaches to microstructure characterization and mechanical property prediction are also examined, with a focus on modeling and advanced imaging techniques such as three-dimensional nanotomography. Finally, this article outlines future directions for ML-enhanced materials science, emphasizing the exploration of high-dimensional parameter spaces and the incorporation of materials kinetics into processing and property evaluation, ultimately advancing the design of nanoporous structures and materials science.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

Using Artificial Intelligence and Machine Learning to Enhance Mission Design and Operations of the Habitable Worlds Observatory (HWO)

One key aspect in the development of HWO is the early deployment of artificial intelligence (AI) and machine learning (ML) to enhance mission science and operations. Our subtask group is part of the HWO AI/ML working group and focuses on AI and ML for mission operations. Our task group seeks to educate other HWO working groups about AI and ML capabilities for mission operations, investigate how to bridge technology gaps, and enable new capabilities particularly in the areas of observational scheduling, instrument health monitoring, and downlink operations. We focus on mission tasking / scheduling both for mission analysis in development and operations. AI and ML for mission scheduling includes: tools to support proposal calls and review, ensuring fairness in calls for proposals, community peer reviews and ease workloads, as well as in-flight and ground software development (e.g., using natural language processing (NLP) to support process automation from requirements). AI and ML for the mission’s development and operations include 1) anomaly detection and prediction (from onboard and ground based tools) to monitor the spacecraft’s health, 2) ground-based automated scheduling for mission operations including long-term and short-term planning and maintenance, and 3) flight system flexible execution (as flight proven for Spitzer and JWST) to enable robust execution despite execution variations, and 4) data analysis for prioritization (e.g., real-time data evaluation leading to autonomous actions and adjustments, high-priority identification, onboard data compression, etc.). Incorporation of ML and AI will enable HWO to address the major science questions related to exoplanet characterization, general astrophysics, and solar system exploration and also extend the boundaries of space mission technologies.

Mark Moussa↗

NASA NTRS2008

Radioisotope Power: A Key Technology for Deep Space Exploration

A Radioisotope Power System (RPS) generates power by converting the heat released from the nuclear decay of radioactive isotopes, such as Plutonium-238 (Pu-238), into electricity. First used in space by the U.S. in 1961, these devices have enabled some of the most challenging and exciting space missions in history, including the Pioneer and Voyager probes to the outer solar system; the Apollo lunar surface experiments; the Viking landers; the Ulysses polar orbital mission about the Sun; the Galileo mission to Jupiter; the Cassini mission orbiting Saturn; and the recently launched New Horizons mission to Pluto. Radioisotopes have also served as a versatile heat source for moderating equipment thermal environments on these and many other missions, including the Mars exploration rovers, Spirit and Opportunity. The key advantage of RPS is its ability to operate continuously, independent of orientation and distance relative to the Sun. Radioisotope systems are long-lived, rugged, compact, highly reliable, and relatively insensitive to radiation and other environmental effects. As such, they are ideally suited for missions involving long-lived, autonomous operations in the extreme conditions of space and other planetary bodies. This paper reviews the history of RPS for the U.S. space program. It also describes current development of a new Stirling cycle-based generator that will greatly expand the application of nuclear-powered missions in the future.

Schmidt, George↗

NASA NTRS2009

Radioisotope Power: A Key Technology for Deep Space Explorations

Schmidt, George R.↗

NASA NTRS2017

NASA Centennial Challenge: Three Dimensional (3D) Printed Habitat, Phase 2

The NASA Centennial Challenges: 3D-Printed Habitat Challenge seeks to develop the fundamental technologies necessary to manufacture an off-world habitat using mission recycled materials and/or local indigenous materials. The vision is that autonomous habitat manufacturing machines will someday be deployed to the Moon or Mars to construct shelters for human habitation. NASA and Bradley University, are holding a new US$ 2.5 million competition to design and build a 3-D printed habitat for deep space exploration, including the agency's journey to Mars. The multi-phase 3-D Printed Habitat Challenge, part of NASA's Centennial Challenges program, is designed to advance the additive construction technology needed to create sustainable housing solutions for Earth and beyond. The first phase of the competition ran through Sept. 27, 2015. This phase, a design competition, called on participants to develop state-of-the-art architectural concepts that take advantage of the unique capabilities 3-D printing offers. The top 3 prizes with a prize purse of $40,000 were awarded at the 2015 World Maker Faire in New York. The second phase of the competition is called the Structural Member Competition and it is divided into three levels happening in the spring and summer of 2017. The Compression Test Competition (Level 1) focuses on the fabrication technologies needed to manufacture structural components from a combination of indigenous materials and recyclables, or indigenous materials alone. For Level 1, teams will develop 3D printable materials, build a 3D printing machine, and print two specimens: a truncated cone and a cylinder. The Level 2 Beam Member Competition is the second of three sub-competitions within the overall Structural Member Competition. For Level 2, teams will print a beam that will be tested. The Level 3 Head to Head Competition is the third of three sub-competitions within the overall Structural Member Competition. For Level 3, teams will develop 3D printable materials, use a 3D printing machine, and print three compression specimens of the elected material, three flexural specimens of the elected material, and one dome structure. Tests conducted on the specimens and the dome structure will determine Level 3 scores and awards. On Earth these same habitat manufacturing capabilities could be used to produce housing wherever affordable housing is needed and access to conventional building materials and skills is limited. Terrestrially, it is envisioned that local indigenous materials (dirt, clay, sand, etc.) could be combined with readily available recyclable materials and used to construct semi-permanent shelters against environmental elements for human habitation. The goal of the 3D-Printed Habitat Challenge is to foster the development of new technologies necessary to additively manufacture a habitat using local indigenous materials with, or without, recyclable materials. This paper will summarize the Level 2 results of this NASA Centennial Challenge competition and it will discuss related technology advancement.

3D printing↗

NASA NTRS2005

Experimenting with an Evolving Ground/Space-based Software Architecture to Enable Sensor Webs

A series of ongoing experiments are being conducted at the NASA Goddard Space Flight Center to explore integrated ground and space-based software architectures enabling sensor webs. A sensor web, as defined by Steve Talabac at NASA Goddard Space Flight Center(GSFC), is a coherent set of distributed nodes interconnected by a communications fabric, that collectively behave as a single, dynamically adaptive, observing system. The nodes can be comprised of satellites, ground instruments, computing nodes etc. Sensor web capability requires autonomous management of constellation resources. This becomes progressively more important as more and more satellites share resource, such as communication channels and ground station,s while automatically coordinating their activities. There have been five ongoing activities which include an effort to standardize a set of middleware. This paper will describe one set of activities using the Earth Observing 1 satellite, which used a variety of ground and flight software along with other satellites and ground sensors to prototype a sensor web. This activity allowed us to explore where the difficulties that occur in the assembly of sensor webs given today s technology. We will present an overview of the software system architecture, some key experiments and lessons learned to facilitate better sensor webs in the future.

mandl, Daniel↗

NASA NTRS2004

Bioinspired Engineering of Exploration Systems (BEES) - its Impact on Future Missions

This paper describes an overview of our "Bioinspired Engineering of Exploration Systems for Mars" ( "BEES for Mars") project. The BEES approach distills selected biologically inspired strategies utilizing motion cues/optic flow, bioinspired pattern recognition, biological visual and neural control systems, bioinspired sensing and communication techniques, and birds of prey inspired search and track algorithmic systems. Unique capabilities so enabled, provide potential solutions to future autonomous robotic space and planetary mission applications. With the first series of tests performed in September 2003, August 2004 and September 2004, we have demonstrated the BEES technologies at the El Mirage Dry Lakebed site in the Mojave Desert using Delta Wing experimental prototypes. We call these test flyers the "BEES flyer", since we are developing them as dedicated test platform for the newly developed bioinspired sensors, processors and algorithmic strategies. The Delta Wing offers a robust airframe that can sustain high G launches and offers ease of compact stowability and packaging along with scaling to small size and low ReynOld's number performance for a potential Mars deployment. Our approach to developing light weight, low power autonomous flight systems using concepts distilled from biology promises to enable new applications, of dual use to NASA and DoD needs. Small in size (0.5 -5 Kg) BEES Flyers are demonstrating capabilities for autonomous flight and sensor operability in Mars analog conditions. The BEES project team spans JPL, NASA Ames, Australian National University (ANU), Brigham Young University(BYU), DC Berkeiey, Analogic Computers Inc. and other institutions. The highlights from our recent flight demonstrations exhibiting new Mission enabling capabilities are described. Further, this paper describes two classes of potential new missions for Mars exploration: (1) the long range exploration missions, and (2) observation missions, for real time imaging of critical ephemeral phenomena, that can be enabled by use of BEES flyers. For example, such flyers can serve as a powerful black-box for critical descent and landing data and enablers for improved science missions complementing and supplementing the existing assets like landers and rovers by providing valuable exploration and quick extended low-altitude aerial coverage of the sites of interest by imaging them and distributing instruments to them. Imaging done by orbiters allows broad surface coverage at limited spatial resolution. Low altitude air-borne exploration of Mars offers a means for imaging large areas, perhaps up to several hundred kilometers, quickly and efficiently, providing a close-up birds-eye view of the planetary terrain and close-up approach to constrained difficult areas like canyons and craters. A novel approach to low-mass yet highly capable flyers is enabled by small aircraft equipped using sensors and processors and algorithms developed using BEES technology. This project is focused towards showing the direct impact of blending the best of artificial intelligence attributes and bioinspiration to create a leap beyond existing capability for our future Missions.

navigation↗

NASA NTRSDate not supplied

Neo – Mars Adaptive Training Integrative Knowledge System (MATRIKS) to Improve Operational Performance and its Neural Basis for Spaceflight

With prolonged mission durations, spaceflight crews will become increasingly dependent on onboard technologies for knowledge acquisition and maintenance. It is expected that not all skills and knowledge required for these missions can be retained and retrieved based on pre-mission training alone. Limited and delayed communication will significantly constrain support from Mission Control and crews will increasingly rely on autonomous onboard technologies to successfully perform post-landing operations. With the present project we will target NASA’s particular interest in developing and assessing an adaptive, just-in-time countermeasure that will consolidate and improve skills that are most relevant to space flight operations. To achieve this aim, NASA established a Virtual NASA Specialized Center of Research (VNSCOR)referred to as “Mars Adaptive Training Integrative Knowledge System (MATRIKS)”, comprising the following three projects: (1) “Trinity–Multi-Environment Virtual Training for Long Duration Exploration Missions”, PI: A. Anderson (UC Boulder); (2) “Morpheus–A Haptic Sensory Supplement to Optimize In-Flight Adaptive Training for Human Control of Spacecraft Robotic Arms”, PI: S. Robinson, UC Davis); and the present project “Neo–Adaptive Training integrative knowledge System to Improve Operational Performance and its Neural Basis for Spaceflight” (UPenn, PI: A.C. Stahn). Neo leverages a validated workstation called 6DF that simulates a rendezvous and docking maneuver using real spacecraft flight dynamics. It is designed to (1) train and improve sensorimotor skills relevant for inflight and post-landing operational tasks; (2) feature an autonomous and adaptive training approach that does not rely on feedback from flight operations on the ground; (3) maximize the transfer of mission-relevant motor skills; (4) allow the assessment of the neural circuitry underlying the task; and (5) deliver the training in a motivating and meaningful way to astronauts. Neocomprises two overarching aims: First, we will identify the neural circuitry underlying spaceflight relevant tasks by performing a subset of the 6DFtaskduring functional magnetic resonance imaging (MRI)in a total of up to N=30 subjects with varying levels of 6DF training experience. Second, as part of the above-mentioned VNSCOR MATRIKS the proposed 6DF autonomous intelligent tutor system will be integrated in an additive manner with a haptic feedback intervention (Morpheus), and a multi-environment virtual trainer(Trinity).It is expected that Neo, Morpheus and Trinity mutually complement each other to facilitate an effective countermeasure tool to acquire and retain operational skills that are critical for exploration class missions. To assess the efficacy of this combined effort, the VNSCOR MATRIKS will collect data inN=16 crew members in one HERA campaign of 45 days duration with N=16 crew members(four missions with N=4 crew member seach).The primary goal is to identify changes in operational performance as assessed by NASA’s simulator of Canadarm2 operations, i.e., Robotic On-board Trainer (ROBoT-r) in response to MATRIKS. As part of Neo we will also identify if, and to what extent MATRIKS will promote transfer to general cognitive performance (Cognition battery), distinctive visuo-spatial tasks critical for telerobotic tasks (Spatial Cognition battery), and affect brain structural changes and the neural circuitry of key brain networks expected to be relevant for spaceflight-related performance. At the conclusion of the research, we will have defined and demonstrated the use of a neuroscience-based, adaptive training integrative knowledge system to potentially mitigate visuo-spatial and sensorimotor brain changes associated with prolonged isolation and confinement to reduce the likelihood or impact of potential decrements in human performance capabilities during long-duration space missions. The expected significance of this 4-year project relates to its relevance for facilitating effective countermeasure tools to acquire and retain operational skills that are critical for exploration class missions. This will support the development of necessary countermeasures and technologies in support of human space exploration, focusing on mitigating operational performance risks.

A C Stahn↗

NASA NTRS1990

The real-time learning mechanism of the Scientific Research Associates Advanced Robotic System (SRAARS)

Scientific research associates advanced robotic system (SRAARS) is an intelligent robotic system which has autonomous learning capability in geometric reasoning. The system is equipped with one global intelligence center (GIC) and eight local intelligence centers (LICs). It controls mainly sixteen links with fourteen active joints, which constitute two articulated arms, an extensible lower body, a vision system with two CCD cameras and a mobile base. The on-board knowledge-based system supports the learning controller with model representations of both the robot and the working environment. By consecutive verifying and planning procedures, hypothesis-and-test routines and learning-by-analogy paradigm, the system would autonomously build up its own understanding of the relationship between itself (i.e., the robot) and the focused environment for the purposes of collision avoidance, motion analysis and object manipulation. The intelligence of SRAARS presents a valuable technical advantage to implement robotic systems for space exploration and space station operations.

Chen, Alexander Y.↗

NASA NTRS1986

Artificial intelligence planning applications for space exploration and space robotics

Mission sequencing involves the plan for actuation of the experiments to be conducted aboard a spacecraft; automation is under study by NASA as a means to reduce time and manpower costs in mission planning and in robotic implementation. The development of a mission sequence is conditioned by the limited duration of advantageous spacecraft encounters with objects of study, more research requests than can be satisfied, and requested changes in objectives. Autonomous robot development is hampered by the absence of task-level programming languages, the existence of anomalies in real-world interactions, and a lack of required capabilities in current sensor technology.

Rokey, Mark↗

NASA NTRS2019

NASA Station Explorer for X-Ray Timing and Navigation Technology (SEXTANT) Mission Operations Architecture

"The Station Explorer for X-Ray Timing and Navigation (SEXTANT) mission is a technology demonstrationenhancement to the Neutron Star Interior Composition Explorer (NICER) mission, a NASA AstrophysicsExplorer Mission of Opportunity to the International Space Station (ISS) that was launched in June of 2017.The NICER instrument is a precision pointing X-ray telescope that provides measurements of neutron stars,which the SEXTANT mission uses to perform autonomous onboard X-ray Pulsar Navigation (XNAV) by usingmilli-second pulsars (MSPs), a category of neutron stars, as timing sources for navigation. By comparing thedetected time of arrival of X-ray photons to a reference of expected pulsar lightcurve timing models, one caninfer a range and range rate measurement based on light time delay. Since both timing and orientationinformation comes from a celestial source, this technology could provide a GPS-like navigation capabilityavailable throughout our Solar System and beyond. Applications that XNAV can support include outer planetand interstellar missions, manned missions, libration orbit missions, and current infrastructure such as the DeepSpace Network (DSN). The SEXTANT team successfully completed a rst demonstration of in-space andautonomous XNAV in November 2017. NICER and SEXTANT have separate teams, with NICER being theprimary team with its own science objectives. Operational modes for both missions must have concurrent andindependent components as well as an integrated ground system. Within this joint mission pro le, SEXTANToperations requires an infrastructure and cadence that is exible to handle concurrent science operations fromthe NICER team, independent autonomous navigation demonstrations, and events within the extensive ISSoperations environment. This paper rst details the infrastructure implemented and its concept of operations.It then describes the operations for the SEXTANT demonstration and lessons learned."

Yu, Wayne H.↗

NASA NTRSDate not supplied

The Lunar Explorer Instrument for Space Biology Applications (LEIA) Pre-Flight Tests: Mechanical Considerations

The BioSensor payload on the upcoming LEIA platform aboard the CLPS (CP-22) lander will carry yeast to the Moon's south pole to study the effects of lunar radiation and gravity. From Earth, BioSensor will transport desiccated yeast within 16 fluidic cards, each containing 16 fluidic wells of 100 microliters volume. At the start of the experiment, these microbes will be autonomously rehydrated on the Moon, and optical absorbance measurements at three wavelengths will be performed across all 256 wells. Absorbance measurements in fluidic wells are susceptible to noise and artifacts due to bubble formation during well rehydration and microbial growth. BioSensor, as a biofluidics system, faces significant data anomalies caused by payload vibrations, particularly from the operation of the co-located PROSPECT’s ProSEED drill, to assess lunar resources by penetrating the subsurface. To evaluate the impact of drill vibrations, fully assembled and filled fluidic cards were subjected to sine sweep signals in the x, y, z planes, ranging from 5 - 2560 Hz at g-loads of 0.25 and 0.5, with on-board accelerometer for localized vibration measurement. Wells with various fill conditions, including partial fills with different bubble sizes and completely filled wells, were tested. Results indicated that small bubbles adhered to well walls at high frequencies but were dislodged at low frequencies, causing data anomalies. Medium and large bubbles exhibited higher interference with optical data due to lower surface tension and variable bubble surface flexion. Overall, it was found that irrespective of the fill type, low-frequency vibrations were most detrimental to optical readings. Understanding the drill schedule and correlating it with BioSensor timestamps will help identify periods when BioSensor data is unreliable, a crucial step for accurately interpreting the microbial response data and ensuring the validity of the BioSensor's scientific measurements on the lunar surface.

Chinmayee Govinda Raj↗