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NASA NTRS2017

Automation of POST Cases via External Optimizer and "Artificial p2" Calculation

During conceptual design speed and accuracy are often at odds. Specifically in the realm of launch vehicles, optimizing the ascent trajectory requires a larger pool of analytical power and expertise. Experienced analysts working on familiar vehicles can produce optimal trajectories in a short time frame, however whenever either "experienced" or "familiar " is not applicable the optimization process can become quite lengthy. In order to construct a vehicle agnostic method an established global optimization algorithm is needed. In this work the authors develop an "artificial" error term to map arbitrary control vectors to non-zero error by which a global method can operate. Two global methods are compared alongside Design of Experiments and random sampling and are shown to produce comparable results to analysis done by a human expert.

Dees, Patrick D.↗

NASA NTRS2018

Artificial Intelligence - The Future of Space Communications

Presentation focus on the Cognitive Communications Project Overview. The project aims to develop cognitive communications technologies to increase mission science return and improve resource efficiencies. Another key aspect of the project is understanding the fundamental aspects of cognitive technology and developing artificial intelligence to advance the future of space communications. This involves the use of machine learning algorithms in the next generation architecture for space communications in the effort to increase efficiency, autonomy and increased performance of the space communication and navigations next generation architecture.

Briones, Janette C.↗

NASA NTRS2018

The Turbolift: Linear Sled Hybrid Artificial Gravity Concept

Future crewed space exploration missions into deep space will require enhanced countermeasure technologies to ensure astronaut health. One such hazard is extended exposure to reduced gravity levels (i.e., microgravity, lunar gravity, or Martian gravity). Reduced gravity negatively impacts many physiological systems, leading to hydrostatic intolerance, musculoskeletal atrophy, sensorimotor impairment, bone demineralization, cardiovascular deconditioning, and visual alterations. Various countermeasures have been employed for mitigating these effects, such as exercise, pharmaceuticals, diet, and fluid loading. However, these approaches treat individual symptoms, such that each physiological system is addressed with typically one countermeasure. An alternative to this approach is artificial gravity (AG), which promises to be a holistic, comprehensive countermeasure. The traditional approach to creating AG is through centrifugation. However, centrifugation is not a "pure" form of AG and typically includes the drawbacks of Coriolis forces, gravity gradients, and vestibular cross-coupled illusions.As an alternative, we have proposed a Linear Sled Hybrid (LSH) AG system to mitigate astronauts' physiological deconditioning. This system functions by applying pure linear acceleration to produce footward loading. There is a half rotation (180_) to reorient the rider between acceleration and deceleration phases, such that the loading remains footward, as when standing on Earth. The rotation also provides some footward acceleration to the lower body through centripetal acceleration; hence the "hybrid" aspect of the design. At the end of the deceleration, the rider than accelerates back in the opposite direction and the sequence repeats.This proposed system could be integrated with future crewed space vehicles in a variety of manners.

Gruber, Jason↗

NASA NTRS2018

Introduction to NASA Goddard Workshop on Artificial Intelligence

Artificial Intelligence (AI) is a collection of advanced technologies that allows machines to think and act, both humanly and rationally, through sensing, comprehending, acting and learning. AI's foundations lie at the intersection of several traditional fields Philosophy, Mathematics, Economics, Neuroscience, Psychology and Computer Science. Although the inception of AI started in the 1950's, it has recently made a strong comeback in all aspects of society and all over the world; this is mainly due to the timely combination of increased data volumes, advanced and mature algorithms, and improvements in computing power and storage. Current AI applications include big data analytics, robotics, intelligent sensing, assisted decision making, and speech recognition just to name a few.This workshop will be investigating how AI technologies can be adapted or developed to address the following challenges: Discover events of interest and correlations in large amounts of science data; improve the outcomes of science modeling and data assimilation using improved data processing, integration, and analysis. Design advisors for mission planning and operations, including anomaly detection and spacecraft health monitoring. Develop tools for engineering support, including advanced manufacturing, orbit determination, new component design and system engineering. Customize intelligent user interfaces, including visual analytics and natural language processing.

Le Moigne, Jacqueline↗

NASA NTRS2018

Overview of Artificial Intelligence (AI) at NASA Goddard

Le Moigne, Jacqueline↗

NASA NTRS2018

An Artificial Neural Network for Inferring Solar Wind Proxies at Mars

We present a novel method to determine solar wind proxies from sheath measurements at Mars. Specifically, we develop an artificial neural network (ANN) to simultaneously infer seven solar wind proxies: ion density, ion speed, ion temperature, and interplanetary magnetic field magnitude and its vector components, using spacecraft measurements of ion moments, magnetic field magnitude, magnetic field components in the sheath, and the solar extreme ultraviolet flux. The ANN was trained and tested using3 years of data from the Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft. When compared with MAVEN spacecraft's in situ measured values of the solar wind parameters, we find that the ANN proxies for the solar wind ion density, ion speed, ion temperature, and interplanetary magnetic field magnitude havepercentage differences of 50% or less for 84.4%, 99.9%, 86.8%, and 79.8% of the instances, respectively. Forthe cone angle and clock angle proxies, 69.1% and 53.3% of instances, respectively, have angle differences of 30* or less.

Neural Network↗

NASA NTRS2019

Predicting Satellite Close Approaches Using Statistical Parameters in the Context of Artificial Intelligence

In order to ensure a sustainable use of low earth orbit in particular and near Earth space in general, reliable and effective close approach prediction be-tween space objects is key. Only this allows for efficient and timely colli-sion avoidance. Space Situational Awareness (SSA) for commercial and government missions will be facing the rapidly growing amount of small and potentially less agile satellites as well as debris in the near earth realm, such as the increase in CubeSat launches and upcoming large constellations. At the same time, space object detection capabilities are expected to increase significantly, allowing for the reliable detection of smaller objects, e.g. when the Air Force Space Fence radar becomes operational. In combination, the space object catalog is expected to increase tremendously in size. In this paper, we introduce an investigative approach based on the latest capabili-ties in artificial intelligence in fostering the potential for fast and accurate close approach predictions. We consider the study of statistical and infor-mation theory parameters in contrast and complementary to the classical probability of collision computation alone, in order to determine the feasi-bility of reliably predicting close approaches.

Mashiku, A.↗

NASA NTRS2019

Predicting Satellite Close Approaches Using Statistical Parameters in the Context of Artificial Intelligence

Mashiku, Alinda↗

NASA NTRS2019

Artificial Intelligence: Powering Human Exploration of the Moon and Mars

Artificial Intelligence (AI) is a growing field of computa- tional science techniques designed to mimic functions per- formed by people. Advancements in autonomy will depend on a portfolio of AI technologies. Automated planning and scheduling is a venerable field of study in AI, and is needed for a variety of mission planning functions. Plan execution technology is less well studied, but important for auton- omy and robotics. Specialized forms of automated reason- ing and machine learning are key technologies to enable fault management. Over the past decade, the NASA Au- tonomous Systems and Operations (ASO) project has devel- oped and demonstrated numerous autonomy enabling tech- nologies employing AI techniques. Our work has employed AI in three distinct ways to enable autonomous mission op- erations capabilities. Crew Autonomy gives astronauts tools to assist in the performance of each of these mission oper-ations functions. Vehicle System Management uses AI tech- niques to turn the astronaut's spacecraft into a robot, allow- ing it to operate when astronauts are not present, or to reduce astronaut workload. AI technology also enables Autonomous Robots as crew assistants or proxies when the crew are not present. When these capabilities are used to enable astro- nauts to operate autonomously, they must be integrated with user interfaces, introducing numerous human factors con- siderations; when these capabilities are used to enable vehi- cle system management, they must be integrated with flight software, and run on embedded processors under the control of real-time operating systems.We first describe human spaceflight mission operations capabilities. The remainder of the paper will describe the ASO project, and the development and demonstration per- formed by ASO since 2011. We will describe the AI tech- niques behind each of these demonstrations, which include a variety of symbolic automated reasoning and machine learn- ing based approaches. Finally, we conclude with an assess- ment of future development needs for AI to enable NASA's future Exploration missions.

Mission Operations↗

NASA NTRS2019

Artificial Intelligence in Aviation Safety Applications - Exploring Myths and Truths of AI and ML

Artificial Intelligence (AI) and machine learning (ML) are gaining increased attention as ways to leverage the world's data to solve problems. Although AI and ML offer much potential, there are often misconceptions about the application of such techniques.Panel speakers will present machine learning approaches they have developed on a variety of aviation data, including digital flight data, safety reporting data, and voice communications data. They will discuss the purpose of the application, the data used, and the lessons learned in the development and deployment of their solutions. The panel will also discuss common pitfalls in developing an AI solution, the dangers of the current hype around AI, tips for gaining value from a ML solution, how to determine whether a ML approach is appropriate for a problem, and more.

Reeves, Scott (Capt.)↗

NASA NTRSDate not supplied

Supporting Aquaculture in the Chesapeake Bay Using Artificial Intelligence to Detect Poor Water Quality with Remote Sensing

Reliable information on water quality is not currently available at the space and time scales that are required for aquaculture and other resource management needs. For example, shellfish growing areas maybe impacted by harmful algal blooms or runoff from land that increases turbidity, lowers salinity, or introduces contaminants. Shellfish resource managers in the Chesapeake Bay are especially concerned with sources of bacteria from land such as failing onsite waste systems, failing wastewater infrastructure, and concentrated animal feeding operations. There is an urgent need for remote sensing of water quality indicators beyond chlorophyll-a and suspended sediments to augment field sampling programs. Artificial Intelligence trained with simultaneous in situ and satellite observations is explored in preparation for future hyperspectral satellite missions, which offer potential to detect additional water quality indicators not previously possible. This first step identifies and develops a method to harmonize disparate, unlinked aquatic datasets to derive information about where water quality is likely degraded.

Stephanie Schollaert Uz↗

NASA NTRS2020

Development and Comparison of an Artificial Gravity Concept for Human Spaceflight

Artificial Gravity (AG) for a long-duration space journey has been theorized since the dawn of human spaceflight. The purpose of this project is to begin bringing those concepts into real-life. The goal and primary study for this project is to research and understand the physics behind making a rotating centrifuge and the human limitations that may contribute to the design. The research first started with finding the physics equations we can use to calculate the rotational speed required for a certain gravity or the required radius for the centrifuge structure, this equation is a(sub c) = v2/r = w(sub c)2 r, where v is linear velocity, r is the radius of the structure, and is the rotational speed. Next, it is important to understand AG may be worthwhile because of the negative health effects of microgravity. The research then leads to finding human limitations in a rotating structure. The main limitation of a human living in this habitat is the Coriolis effect, which causes discomfort and motion sickness in the subject inside the structure, this causes the three semicircular fluid canals in your head that sense balance and motion. Finally, we use that knowledge to best hypothesize and propose the radius size for the structure that would negate the Coriolis effect, and using that we can calculate the appropriate rotational speed to get the desired gravity level, for example: 1 g or 9.81 m/s2 of acceleration. Overall this research can encourage increase knowledge in understanding AG, the physics and human limitation in a rotating centrifuge on future NASA long-duration Missions and Goals. This research can be a reference to future design proposals for long-duration exploration missions and colonization of planets or moons.

Cody L Beard↗

NASA NTRSDate not supplied

Common Habitat Design for Microgravity, Artificial Gravity and Partial Gravity in a Safe Haven Configuration

As space exploration efforts advance towards the returning to Lunar surface and later to Mars, new challenges emerge, and habitation becomes one of them. Having a custom manufacturing line for deep space transit and surface habitats (the Moon or Mars) implies a longer design and production line. Instead of spending great amounts of money and time on custom designs a common development has been explored where the interior architectural functions can be multifunctional, capable of being adapted into distinct gravity conditions. The concept developed for a common design is capable of being adapted to two (2) different layout configurations for a common habitat in a safe haven design: (#1) a habitat that could be used both as a deep space transport in micro gravity and on Mars surface partial gravity; and (#2) a habitat that could be used both as a deep space transport in artificial gravity and on Mars surface partial gravity. These designs have the potential to diminish manufacturing time and cost, but also reducing crew training time increasing crew adaptability to their habitation and gravity condition.

Paola M Gonzalez Marquez↗

NASA NTRSDate not supplied

Artificial Neural Networks and AI in high Assurance Applications: Gaps and Techniques

In recent years, capabilties of Deep Neural Networks (DNN) and Artificial Intelligence (AI) systems have grown tremendously. They are now applied in many areas ranging from game playing, social media, science, to robotics, automotive, and aerospace applications.Based upon requirements for safety of DNN and AI in high assurance automotive and aerospace applications, I will discuss the necessity to ensure that AI technqiues for the analysis of Earth observation data and reasoning are working correctly and reliably.In this talk I will present modern techniques for the verification and validation (V&V) of DNN and other AI components as well as approaches for interpretable AI. I will discuss how these techniques can help to ensure quality of the AI results, improve confidence in their application, and facilitate human-AI interaction and collaboration.

Johann Schumann↗

NASA NTRS2021

Introduction to the Physics of Ionic Conduction in Narrow Biological and Artificial Channels

The permeation of ions through narrow water-filled channels is essential to life and of rapidly-growing importance in technology. Reaching an understanding of the mechanisms underlying the permeation process requires an interdisciplinary approach, where ideas drawn from physics are of particular importance and have brought encouraging progress in recent years. This Introduction sets into context the several ground-breaking papers presented in the Entropy Special Issue on "The Physics of Ionic Conduction in Narrow Biological and Artificial Channels''.

ion channel↗

NASA NTRS1996

The Orbital Motion of the Martian Satellites: An Application of Artificial Satellite Theory

This paper describes our extended Sinclair-Morley analytical theory for the Martian Satellites. The theory's foundation is the artificial satellite theory of Brouwer. In the paper, we outline the original theory, present our modifications and additional terms, and compare the theory with numerical integration. We also provide a comparison with one of the competing dynamical Martian satellite theories, namely the ESAPHO/ESADE theory of Chapornt-Touzé.

Sinclair-Morley↗

NASA NTRSDate not supplied

Intracranial Effects of Artificial Gravity: A 3T MRI Study

INTRODUCTION Spaceflight associated neuro-ocular syndrome (SANS) is characterized by the development of optic disc edema, posterior globe flattening, choroidal/retinal folds and hyperopic refractive errors1. SANS is hypothesized to be a result of headward fluid shifts that invariably occurs in the microgravity environment. As a countermeasure, artificial gravity (AG) through centrifugation has been proposed to reduce this headward fluid shift, however there is no current proof of benefit. The goal of this study was to determine if the application of AG can prevent or reduce known changes in brain volumetry, internal carotid artery (ICA) stroke volume and cerebral spinal fluid (CSF) flow velocity that occurs during simulated chronic headward fluid shift using head down tilt bed rest (HDTBR) methodology2 as an indicator of countermeasure efficacy. METHODS Healthy volunteers were recruited for an IRB approved HDTBR study performed at the German Aerospace Center in Cologne, Germany. Strict six-degree HDTBR was used as a spaceflight analog to induce a continuous headward fluid shift. HDTBR was carried out for 60 days for all subjects. Short-arm centrifugation was utilized to generate AG equating to ~0.3g of acceleration at the level of the eye. The subjects were divided equally into three groups: NoAG (control; n=8), daily intermittent AG (6 x 5 min iAG; n=8), and daily continuous 30 min (cAG; n=8). All studies were performed on a single dedicated 3T MRI Scanner. Pulse-gated MRI phase-contrast flow imaging was used to quantify ICA stroke volume and peak-to-peak CSF flow velocity in the mid cerebral aqueduct. 3D-SPGR was acquired for volumetric segmentation of the brain and CSF spaces. MRI acquisitions were obtained at baseline (BDC), 14 days into HDTBR (HDTBR14), 52 days into HDTBR (HDTBR52) and 3-5 days after HDTBR (recovery, R+3/5).The data were analyzed by the mixed model, which included intervention and time (BDC, HDTBR 14, HDTBR 52, R+3/5) as the fixed effects and included subject as the random effect.RESULTS24 healthy subject volunteers (16 men, 8 women, mean age = 33 years ± 9 [standard deviation] and mean BMI = 24.3 kg/m2 ± 2.0) successfully completed all phases of the study. Strict six-degree HDTBR was characterized by progressive and statistically significant (p<.01) increases in mean combined brain and CSF volumes and mean aqueductal CSF peak-to-peak flow velocity, as well as statistically significant (p<.01) progressive decrease in mean ICA stroke volume from baseline to 52 days post intervention (Figs. 1-3). Compared to baseline, only combined brain and CSF volumes did not return to baseline values in the recovery period (p=NS). Neither iAG nor cAG exerted any significant effects on the measured MRI brain parameters as compared to HDTBR alone (p=NS). CONCLUSION Our results indicate that HDTBR at 6-degrees was effective in producing alterations in ICA stroke volume, aqueductal CSF flow velocity, and combined brain and CSF volumetric change that is associated with chronic headward fluid shift. Short duration, 30-min daily exposure to either iAG or cAG appears to be insufficient in preventing or reducing the effects of chronic HDTBR and thus may not be a suitable countermeasure as currently deployed. AG protocol modifications, including increased duration and magnitude of exposure, should be considered for future research.

L A Kramer↗

NASA NTRS2021

ORCAS – Orbiting Configurable Artificial Star Mission Architecture

In this paper, we establish the mission operation concept for the Orbiting Configurable Artificial Star mission, a hybrid space-ground observatory, which aims to enable ground observations of near-diffraction limited resolution and exquisite sensitivity. We present the mission requirements, introduce a potential orbit solution that can meet them, detail the concrete operational steps to be taken to enable such observations, and develop a mission planning tool which generates a mission schedule that meets all mission requirements and can be altered in real time in the case of disruptions to the mission. Finally, we show the the mission could enable 300 adaptive optics and 1500 flux calibration observations throughout its lifetime.

Eliad Peretz↗