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NASA NTRS1996

Use-dependent loss of active sympathetic neurogenic vasodilation after nitric oxide synthase inhibition in conscious rats. Evidence for the presence of preformed stores of nitric oxide-containing factors

In this study, we examined whether air-jet stress-induced active sympathetic hindlimb vasodilation in conscious rats involves the release of preformed stores of nitric oxide-containing factors. We determined the effects of repeated episodes of air-jet stress (six episodes given 5 minutes apart) on mean arterial pressure and vascular resistances in the mesenteric bed and intact and sympathetically denervated hindlimb beds of conscious rats treated with saline or the nitric oxide synthesis inhibitor N omega-nitro-L-arginine methyl ester (L-NAME, 25 mumol/kg IV). In saline-treated rats, air-jet stress produced alerting behavior, minor changes in blood pressure, pronounced mesenteric vaso-constriction, and immediate and marked vasodilation in the sympathetically intact hindlimb but a minor vasodilation in the sympathetically denervated hindlimb. Each air-jet stress produced virtually identical responses. In L-NAME-treated rats, the first air-jet stress produced vasodilator responses in the sympathetically intact and sympathetically denervated hindlimbs that were similar to those in the saline-treated rats. However, each subsequent air-jet stress produced progressively smaller vasodilator responses in the sympathetically intact but not the sympathetically denervated hindlimb. There was no loss of air-jet stress-induced alerting behavior or mesenteric vasoconstriction, suggesting that L-NAME did not interfere with the central processing of the air-jet or the resultant changes in autonomic nerve activity. The progressive diminution of air-jet stress-induced vasodilation in the intact hindlimb of L-NAME-treated rats may be due to the use-dependent depletion of preformed stores of nitric oxide-containing factors that cannot be replenished in the absence of nitric oxide synthesis.

NASA Discipline Regulatory Physiology↗

NASA NTRS2004

Field Effect Transistor Behavior in Electrospun Polyaniline/Polyethylene Oxide Demonstrated

Novel transistors and logic devices based on nanotechnology concepts are under intense development. The potential for ultra-low-power circuitry makes nanotechnology attractive for applications such as digital electronics and sensors. For NASA applications, nanotechnology offers tremendous opportunities for increased onboard data processing, and thus autonomous decisionmaking ability, and novel sensors that detect and respond to environmental stimuli with little oversight requirements. Polyaniline/polyethylene oxide (PANi/PEO) nanofibers are of interest because they have electrical conductivities that can be changed from insulating to metallic by varying the doping levels and conformations of the polymer chain. At the NASA Glenn Research Center, we have observed field effect transistor (FET) behavior in electrospun PANi/PEO nanofibers doped with camphorsulfonic acid. The nanofibers were deposited onto Au electrodes, which had been prepatterned onto oxidized silicon substrates. The preceding scanning electron image shows the device used in the transistor measurements. Saturation channel currents are observed at surprisingly low source/drain voltages (see the following graph). The hole mobility in the depletion regime is 1.4x10(exp -4)sq cm/V sec, whereas the one-dimensional charge density (at zero gate bias) is calculated to be approximately 1 hole per 50 two-ring repeat units of polyaniline, consistent with the rather high channel conductivity (approx.10(exp -3) S/cm). Reducing or eliminating the PEO content in the fiber is expected to enhance device parameters. Electrospinning is thus proposed as a simple method of fabricating one-dimensional polymer FET's.

Mueller, Carl H.↗

NASA NTRS2017

Formal Assurance for Cognitive Architecture Based Autonomous Agent

Autonomous systems are designed and deployed in different modeling paradigms. These environments focus on specific concepts in designing the system. We focus our effort in the use of cognitive architectures to design autonomous agents to collaborate with humans to accomplish tasks in a mission. Our research focuses on introducing formal assurance methods to verify the behavior of agents designed in Soar, by translating the agent to the formal verification environment Uppaal.

Bhattacharyya, Siddhartha↗

NASA NTRS2018

Operator Trust Function for Predicted Drone Arrival

To realize the full benefit from autonomy, systems will have to react to unknown events and uncertain dynamic environments. The resulting number of behaviors is essentially infinite; thus, the system is effectively non-deterministic but an operator needs to understand and trust the actions of the autonomous vehicles. This research began to tackle non-deterministic systems and trust by beginning to develop a user trust function based on intent information displayed and the prescribed bounds on allowable behaviors/actions of the non-deterministic system. Linear regression shows promise on being able to predict a person’s confidence of the machine’s prediction. Linear regression techniques indicated that subject characteristics, scenario difficulty, the experience with the system, and confidence earlier in the scenario account for approximately 60% of the variation in confidence ratings. This paper details the specifics of the liner regression model – essentially a trust function – for predicting a person’s confidence.

Trujillo, Anna C.↗

NASA NTRS2022

Evaluating Network Performance of Containerized Test Framework for Distributed Space Systems

Distributed space systems are a mission architecture consisting of multiple spacecraft as a cohesive system which provide multipoint sampling, increased mission coverage, or improved sample resolution, while reducing mission risk through redundancy. To fully realize the potential of these systems, eventually scaling to hundreds or thousands of spacecraft, distributed space systems need to be operated as a single entity, which will enable a variety of novel scientific space missions. The Distributed Spacecraft Autonomy (DSA) project is a software project which aims to mature the technology needed for those systems, namely autonomous decision-making and swarm networking. The DSA project leverages a containerized swarm test framework to simulate spacecraft software, which can identify emergent behavior early in development. Container virtualization allows distributed spacecraft systems to be simulated entirely in software on a single computer, avoiding the overhead associated with conventional approaches like hardware facsimiles and virtual machines. For this approach to be effective, the simulated system behavior must not be artificially influenced by the swarm test framework itself. To address this, we present a series of benchmarks to quantify virtual network bandwidth available on a single-host computer and contextualize this against the network and application behavior of the DSA swarm test framework.

Distributed Spacecraft Autonomy↗

NASA NTRS2004

Field Effect Transistor Behavior in Electrospun Polyaniline/Polyethylene Oxide Nanofibers

Miranda, Felix A.↗

NASA NTRSDate not supplied

Lunar Excavator Mission Operations using Dynamic Movement Primitives

To support sustainable infrastructure on the Moon, NASA must leverage robots to extract lunar resources for in-situ processing and construction. As part of this effort, NASA is launching the in-situ resource utilization (ISRU) Pilot Excavator later this decade to validate a robotic regolith excavator based on the Regolith Advanced Surface Systems Operations Robot (RASSOR). RASSOR is designed to extract and transport regolith to meet the needs of ISRU architectures. During its mission, Pilot Excavator will be tasked with driving in test patterns to demonstrate the operational concept. One of these tests is a circular trajectory around the lander while avoiding miscellaneous surface hazards such as lunar rocks. To this end, we utilize dynamic movement primitives to represent navigation sequences as primitive trajectories. Here, we introduce a novel obstacle avoidance parameter, which is configured to avoid rocks throughout testing exercises. We demonstrate the effectiveness our method in a newly developed simulation tool called the Simulated Excavation Environment for Lunar Operations (SEELO) using models based on the NASA RASSOR 2.0 excavator. After making key changes to the obstacle avoidance formulation, our results show that the robot is able to safety and robustly navigate the lunar surface with densely populated rock obstacles while retaining the desired circle pattern behavior.

Space Robotics and Automation↗

NASA NTRS1994

Global Asymptotic Behavior of Iterative Implicit Schemes

The global asymptotic nonlinear behavior of some standard iterative procedures in solving nonlinear systems of algebraic equations arising from four implicit linear multistep methods (LMMs) in discretizing three models of 2 x 2 systems of first-order autonomous nonlinear ordinary differential equations (ODEs) is analyzed using the theory of dynamical systems. The iterative procedures include simple iteration and full and modified Newton iterations. The results are compared with standard Runge-Kutta explicit methods, a noniterative implicit procedure, and the Newton method of solving the steady part of the ODEs. Studies showed that aside from exhibiting spurious asymptotes, all of the four implicit LMMs can change the type and stability of the steady states of the differential equations (DEs). They also exhibit a drastic distortion but less shrinkage of the basin of attraction of the true solution than standard nonLMM explicit methods. The simple iteration procedure exhibits behavior which is similar to standard nonLMM explicit methods except that spurious steady-state numerical solutions cannot occur. The numerical basins of attraction of the noniterative implicit procedure mimic more closely the basins of attraction of the DEs and are more efficient than the three iterative implicit procedures for the four implicit LMMs. Contrary to popular belief, the initial data using the Newton method of solving the steady part of the DEs may not have to be close to the exact steady state for convergence. These results can be used as an explanation for possible causes and cures of slow convergence and nonconvergence of steady-state numerical solutions when using an implicit LMM time-dependent approach in computational fluid dynamics.

Yee, H. C.↗

NASA NTRSDate not supplied

Statistical learning framework for safety and failure analysis of a DNN-based autonomous aircraft system

Deep Neural Networks (DNNs) and Machine Learning technology is increasingly used for safety-critical applications in the Aerospace domain. To ensure safe operations, the DNN and the system must undergo rigorous verification and validation, including advanced statistical analyses. Performance and safety of the DNN and system behavior must not only be analyzed for the nominal case, but under numerous off-nominal and failure cases. In this paper we will describe how our statistical learning framework SYSAI can efficiently perform such analyses using the tool’s unique combination of advanced learning modeling and statistical analysis techniques. SYSAI can effectively explore the high-dimensional state and failure space of the system under test; geometrical shape detection of safety regions and boundaries support explainability of the results to the designer. In this paper, we report experiments and results obtained with a vision-based DNN control system (ACT) that is capable of autonomously steering an aircraft down a runway.

Yuning He↗

NASA NTRS2002

Bi-Level Integrated System Synthesis (BLISS) for Concurrent and Distributed Processing

The paper introduces a new version of the Bi-Level Integrated System Synthesis (BLISS) methods intended for optimization of engineering systems conducted by distributed specialty groups working concurrently and using a multiprocessor computing environment. The method decomposes the overall optimization task into subtasks associated with disciplines or subsystems where the local design variables are numerous and a single, system-level optimization whose design variables are relatively few. The subtasks are fully autonomous as to their inner operations and decision making. Their purpose is to eliminate the local design variables and generate a wide spectrum of feasible designs whose behavior is represented by Response Surfaces to be accessed by a system-level optimization. It is shown that, if the problem is convex, the solution of the decomposed problem is the same as that obtained without decomposition. A simplified example of an aircraft design shows the method working as intended. The paper includes a discussion of the method merits and demerits and recommendations for further research.

Sobieszczanski-Sobieski, Jaroslaw↗

NASA NTRS2003

Cooperative Multi-Agent Mobile Sensor Platforms for Jet Engine Inspection: Concept and Implementation

Cooperative behavior algorithms utilizing swarm intelligence are being developed for mobile sensor platforms to inspect jet engines on-wing. Experiments are planned in which several relatively simple autonomous platforms will work together in a coordinated fashion to carry out complex maintenance-type tasks within the constrained working environment modeled on the interior of a turbofan engine. The algorithms will emphasize distribution of the tasks among multiple units; they will be scalable and flexible so that units may be added in the future; and will be designed to operate on an individual unit level to produce the desired global effect. This proof of concept demonstration will validate the algorithms and provide justification for further miniaturization and specialization of the hardware toward the true application of on-wing in situ turbine engine maintenance.

Litt, Jonathan S.↗

NASA NTRS2016

Self-Aware Vehicles: Mission and Performance Adaptation to System Health

Advances in sensing (miniaturization, distributed sensor networks) combined with improvements in computational power leading to significant gains in perception, real-time decision making/reasoning and dynamic planning under uncertainty as well as big data predictive analysis have set the stage for realization of autonomous system capability. These advances open the design and operating space for self-aware vehicles that are able to assess their own capabilities and adjust their behavior to either complete the assigned mission or to modify the mission to reflect their current capabilities. This paper discusses the self-aware vehicle concept and associated technologies necessary for full exploitation of the concept. A self-aware aircraft, spacecraft or system is one that is aware of its internal state, has situational awareness of its environment, can assess its capabilities currently and project them into the future, understands its mission objectives, and can make decisions under uncertainty regarding its ability to achieve its mission objectives.

Gregory, Irene M.↗

NASA NTRS1990

A task control architecture for autonomous robots

An architecture is presented for controlling robots that have multiple tasks, operate in dynamic domains, and require a fair degree of autonomy. The architecture is built on several layers of functionality, including a distributed communication layer, a behavior layer for querying sensors, expanding goals, and executing commands, and a task level for managing the temporal aspects of planning and achieving goals, coordinating tasks, allocating resources, monitoring, and recovering from errors. Application to a legged planetary rover and an indoor mobile manipulator is described.

Simmons, Reid↗

NASA NTRS2005

Recreation Embedded State Tuning for Optimal Readiness and Effectiveness (RESTORE)

Physiological self-regulation training is a behavioral medicine intervention that has demonstrated capability to improve psychophysiological coping responses to stressful experiences and to foster optimal behavioral and cognitive performance. Once developed, these psychophysiological skills require regular practice for maintenance. A concomitant benefit of these physiologically monitored practice sessions is the opportunity to track crew psychophysiological responses to the challenges of the practice task in order to detect shifts in adaptability that may foretell performance degradation. Long-duration missions will include crew recreation periods that will afford physiological self-regulation training opportunities. However, to promote adherence to the regimen, the practice experience that occupies their recreation time must be perceived by the crew as engaging and entertaining throughout repeated reinforcement sessions on long-duration missions. NASA biocybernetic technologies and publications have developed a closed-loop concept that involves adjusting or modulating (cybernetic, for governing) a person's task environment based upon a comparison of that person's physiological responses (bio-) with a training or performance criterion. This approach affords the opportunity to deliver physiological self-regulation training in an entertaining and motivating fashion and can also be employed to create a conditioned association between effective performance state and task execution behaviors, while enabling tracking of individuals psychophysiological status over time in the context of an interactive task challenge. This paper describes the aerospace spin-off technologies in this training application area as well as the current spin-back application of the technologies to long-duration missions - the Recreation Embedded State Tuning for Optimal Readiness and Effectiveness (RESTORE) concept. The RESTORE technology is designed to provide a physiological self-regulation training countermeasure for maintaining and reinforcing cognitive readiness, resilience under psychological stress, and effective mood states in long-duration crews. The technology consists of a system for delivering physiological self-regulation training and for tracking crew central and autonomic nervous system function; the system interface is designed to be experienced as engaging and entertaining throughout repeated training sessions on long-duration missions. Consequently, this self-management technology has threefold capability for recreation, behavioral health problem prophylaxis and remediation, and psychophysiological assay. The RESTORE concept aims to reduce the risk of future manned exploration missions by enhancing the capability of individual crewmembers to self-regulate cognitive states through recreation-embedded training protocols to effectively deal with the psychological toll of long-duration space flight.

Pope, Alan T.↗

NASA NTRSDate not supplied

Understanding and Verifying Neural Networks

Deep Neural Networks (DNNs) have gained immense popularity in recent times and have widespread use in applications such as image classification, sentiment analysis, speech recognition and also in safety-critical applications such as autonomous driving. However, they suffer limitations such as lack of explainability and robustness which raise safety and security concerns in their usage. Further, the complex structure and large input spaces of DNNs act as an impediment to thorough verification and testing. The SafeDNN project at the Robust Software Engineering (RSE) group at NASA aims at exploring techniques to ensure that systems that use deep neural networks are safe, robust and interpretable. In this talk, I will be presenting our technique Prophecy that automatically infers formal properties of deep neural network models. The tool extracts patterns based on neuron activations as preconditions that imply certain desirable output properties of the model. I would be highlighting case studies that use Prophecy in obtaining explanations for network decisions, understanding correct and incorrect behavior, providing formal guarantees wrt safety and robustness, and debugging neural network models. We have applied the tool on image classification networks, neural network controllers providing turn advisories in unmanned aircrafts, regression models used for autonomous center-line tracking in aircrafts and neural network object detectors

Deep Neural Networks↗

NASA NTRS2017

EcAMSat and BioSentinel: Autonomous Bio Nanosatellites Addressing Strategic Knowledge Gaps for Manned Spaceflight Beyond LEO

Manned missions beyond low Earth orbit (LEO) require that several strategic knowledge gaps about the effects of space travel on the human body be addressed. NASA Ames Research Center has been the leader in developing autonomous bio nanosatellites, including past successful missions for GeneSat, PharmaSat, and OOREOS, that tackled some of these issues. These nanosatellites provide in situ measurements, which deliver insight into the dynamic changes in cell behavior in microgravity. In this talk, two upcoming bio nanosatellites developed at Ames, the E. coli Antimicrobial Satellite (EcAMSat) and BioSentinel, will be discussed. Both satellites contain microfluidic systems that precisely deliver nutrients to the microorganisms stored within wells of fluidic cards. Each well, in turn, has its own 3-color LED and detector system which is used to monitor changes in metabolic activity with alamarBlue, a redox indicator, and the optical density of the cells. EcAMSat investigates the effects of microgravity on bacterial resistance to antimicrobial drugs, vital knowledge for understanding how to maintain the health of astronauts in long-term and beyond LEO spaceflight. The behavior of wild type and mutant uropathic E. coli will be compared in microgravity and with ground data to help understand the molecular mechanisms behind antibiotic resistance and how these phenotypes might change in space. BioSentinel seeks to directly measure the effects of space radiation on budding yeast S. cerevisiae, particularly double strand breaks (DSB). While hitching a ride on the SLS EM-1 mission (Orions first unmanned mission to the moon) in 2018, BioSentinel will be kicked off and enter into a heliocentric orbit, becoming the first study of the effects of radiation on living organisms outside LEO since the Apollo program. The yeast are stored in eighteen independent 16-well microfluidic cards, which will be individually activated over the 12 month mission duration. In addition to the wild type and radiation-sensitive mutant strains, a BioSentinel strain of yeast has been developed, which requires a DSB to reactivate growth, thereby allowing for a direct measurement of DSBs caused by radiation. These two missions demonstrate the utility of using autonomous nanosatellites to address strategic knowledge gaps in the push to once again extend manned spaceflight beyond LEO.

Padgen, Mike↗

NASA NTRS2017

EcAMSat and BioSentinel: Autonomous Bio Nanosatellites Addressing Strategic Knowledge Gaps for Manned Spaceflight Beyond LEO

Manned missions beyond low Earth orbit (LEO) require that several strategic knowledge gaps about the effects of space travel on the human body be addressed. NASA Ames Research Center has been the leader in developing autonomous bio nanosatellites, including past successful missions for GeneSat, PharmaSat, and O/OREOS, that tackled some of these issues. These nanosatellites provide in situ measurements, which deliver insight into the dynamic changes in cell behavior in microgravity. In this talk, two upcoming bio nanosatellites developed at Ames, the E. coli Antimicrobial Satellite (EcAMSat) and BioSentinel, will be discussed. Both satellites contain microfluidic systems that precisely deliver nutrients to the microorganisms stored within wells of fluidic cards. Each well, in turn, has its own 3-color LED and detector system which is used to monitor changes in metabolic activity with alamarBlue, a redox indicator, and the optical density of the cells. EcAMSat investigates the effects of microgravity on bacterial resistance to antimicrobial drugs, vital knowledge for understanding how to maintain the health of astronauts in long-term and beyond LEO spaceflight. The behavior of wild type and mutant uropathic E. coli will be compared in microgravity and with ground data to help understand the molecular mechanisms behind antibiotic resistance and how these phenotypes might change in space. BioSentinel seeks to directly measure the effects of space radiation on budding yeast S. cerevisiae, particularly double strand breaks (DSB). While hitching a ride on the SLS EM-1 mission (Orion's first unmanned mission to the moon) in 2018, BioSentinel will be kicked off and enter into a heliocentric orbit, becoming the first study of the effects of radiation on living organisms outside LEO since the Apollo program. The yeast are stored in eighteen independent 16-well microfluidic cards, which will be individually activated over the 12 month mission duration. In addition to the wild type and radiation-sensitive mutant strains, a BioSentinel strain of yeast has been developed, which requires a DSB to reactivate growth, thereby allowing for a direct measurement of DSBs caused by radiation. These two missions demonstrate the utility of using autonomous nanosatellites to address strategic knowledge gaps in the push to once again extend manned spaceflight beyond LEO.

Padgen, Michael R.↗

NASA NTRS2023

Compliant Robotic Behaviors for Satellite Servicing

The demands of traditional industrial robotics differ significantly from those of space robotics. While industry requires robots that can perform repetitive tasks with precision and speed, the space environment needs robots to cope with uncertainties, dynamics, and communication delays or interruptions, similar to human astronauts. These demands make a well-suited application for compliant robotics and behavior-based programming. Pose Target Wrench Limiting (PTWL) is a compliant behavior paradigm developed specifically to meet these demands. PTWL controls a robot by moving a virtual attractor to a target pose. The attractor applies virtual forces, based on stiffness and damping presets, to an underlying admittance controller. Guided by virtual forces, the robot will follow the attractor until safety conditions are violated or success criteria are met. We tested PTWL on a variety of quasi-static tasks that may be useful for future space operations. Our results demonstrate that PTWL is an extremely powerful tool. It makes teleoperation easy and safe for a wide range of quasi-static tasks. It also facilitates the creation of semi-autonomous state machines that can reliably complete complex tasks with minimal human intervention.

Compliance↗