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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Automated Scheduling of Personnel to Staff Operations for the Mars Science Laboratory

Leveraging previous work on scheduling personnel for space mission operations, we have adapted ASPEN (Activity Scheduling and Planning Environment) [1] to the domain of scheduling personnel for operations of the Mars Science Laboratory. Automated scheduling of personnel is not new. We compare our representations to a sampling of employee scheduling systems available with respect to desired features. We described the constraints required by MSL personnel schedulers and how each is handled by the scheduling algorithm.

MSL↗
NASA NTRS2016

Synergistic Allocation of Flight Expertise on the Flight Deck (SAFEdeck): A Design Concept to Combat Mode Confusion, Complacency, and Skill Loss in the Flight Deck

This paper presents a new design and function allocation philosophy between pilots and automation that seeks to support the human in mitigating innate weaknesses (e.g., memory, vigilance) while enhancing their strengths (e.g., adaptability, resourcefulness). In this new allocation strategy, called Synergistic Allocation of Flight Expertise in the Flight Deck (SAFEdeck), the automation and the human provide complementary support and backup for each other. Automation is designed to be compliant with the practices of Crew Resource Management. The human takes a more active role in the normal operation of the aircraft without adversely increasing workload over the current automation paradigm. This designed involvement encourages the pilot to be engaged and ready to respond to unexpected situations. As such, the human may be less prone to error than the current automation paradigm.

Schutte, Paul↗
NASA NTRS2012

Automation of Hubble Space Telescope Mission Operations

On June 13, 2011, after more than 21 years, 115 thousand orbits, and nearly 1 million exposures taken, the operation of the Hubble Space Telescope successfully transitioned from 24x7x365 staffing to 815 staffing. This required the automation of routine mission operations including telemetry and forward link acquisition, data dumping and solid-state recorder management, stored command loading, and health and safety monitoring of both the observatory and the HST Ground System. These changes were driven by budget reductions, and required ground system and onboard spacecraft enhancements across the entire operations spectrum, from planning and scheduling systems to payload flight software. Changes in personnel and staffing were required in order to adapt to the new roles and responsibilities required in the new automated operations era. This paper will provide a high level overview of the obstacles to automating nominal HST mission operations, both technical and cultural, and how those obstacles were overcome.

Burley, Richard↗
NASA NTRS2016

Moving NASA Beyond Low Earth Orbit: Future Human-Automation-Robotic Integration Challenges

This presentation will provide an overview of current human spaceflight operations. It will also describe how future exploration missions will have to adapt and evolve in order to deal with more complex missions and communication latencies. Additionally, there are many implications regarding advanced automation and robotics, and this presentation will outline future human-automation-robotic integration challenges.

human-robotic↗
NASA NTRS2009

Computations of Aerodynamic Performance Databases Using Output-Based Refinement

Objectives: Handle complex geometry problems; Control discretization errors via solution-adaptive mesh refinement; Focus on aerodynamic databases of parametric and optimization studies: 1. Accuracy: satisfy prescribed error bounds 2. Robustness and speed: may require over 105 mesh generations 3. Automation: avoid user supervision Obtain "expert meshes" independent of user skill; and Run every case adaptively in production settings.

Nemec, Marian↗
NASA NTRS1978

Programmable formatting of program text: Experiences drawn from the TAMPR system

The TAMPR System originated as an approach to the problem of automating the routine modifications of FORTRAN source programs required to adapt them to a variety of uses or environments. Three steps are involved: (1) A FORTRAN source program is processed by the TAMPR Recognizer, yielding essentially a parse tree called the abstract form, (2) the Transformation Interpreter applies IGT's (Intragrammatical Transformations) to the abstract form as tree operations, and (3) the abstract form is then reconverted to source program form by the Formatter. By ensuring that the transformations are applied only to the correct syntactic entities and only in the intended contexts, the use of the abstract form greatly simplifies establishing the reliability of the overall process.

Dritz, K. W.↗
NASA NTRS1991

Joint U.S./Japan Conference on Adaptive Structures, 1st, Maui, HI, Nov. 13-15, 1990, Proceedings

The present volume of adaptive structures discusses the development of control laws for an orbiting tethered antenna/reflector system test scale model, the sizing of active piezoelectric struts for vibration suppression on a space-based interferometer, the control design of a space station mobile transporter with multiple constraints, and optimum configuration control of an intelligent truss structure. Attention is given to the formulation of full state feedback for infinite order structural systems, robustness issues in the design of smart structures, passive piezoelectric vibration damping, shape control experiments with a functional model for large optical reflectors, and a mathematical basis for the design optimization of adaptive trusses in precision control. Topics addressed include approaches to the optimal adaptive geometries of intelligent truss structures, the design of an automated manufacturing system for tubular smart structures, the Sandia structural control experiments, and the zero-gravity dynamics of space structures in parabolic aircraft flight.

Wada, Ben K.↗
NASA NTRS2014

Automated Scheduling of Science Activities for Titan Encounters by Cassini

In an effort to demonstrate the efficacy of automated planning and scheduling techniques for large missions, we have adapted ASPEN (Activity Scheduling and Planning Environment) [1] and CLASP (Compressed Large-scale Activity Scheduling and Planning) [2] to the domain of scheduling high-level science goals into conflict-free operations plans for Titan encounters by the Cassini spacecraft.

Ray, Trina L.↗
NASA NTRS2014

Automated Scheduling of Science Activities for Titan Encounters by Cassini

In an effort to demonstrate the efficacy of automated planning and scheduling techniques for large missions, we have adapted ASPEN (Activity Scheduling and Planning Environment) [1] and CLASP (Compressed Large-scale Activity Scheduling and Planning) [2] to the domain of scheduling high-level science goals into conflict-free operations plans for Titan encounters by the Cassini spacecraft.

Ray, Trina L.↗
NASA NTRS1994

A Software Architecture for Automating Operations Processes

The Operations Engineering Lab (OEL) at JPL has developed a software architecture based on an integrated toolkit approach for simplifying and automating mission operations tasks. The toolkit approach is based on building adaptable, reusable graphical tools that are integrated through a combination of libraries, scripts, and system-level user interface shells. The graphical interface shells are designed to integrate and visually guide a user through the complex steps in an operations process. They provide a user with an integrated system-level picture of an overall process, defining the required inputs and possible outputs through interactive on-screen graphics.

mission↗
NASA NTRS2003

Automating the Processing of Earth Observation Data

NASA s vision for Earth science is to build a "sensor web": an adaptive array of heterogeneous satellites and other sensors that will track important events, such as storms, and provide real-time information about the state of the Earth to a wide variety of customers. Achieving this vision will require automation not only in the scheduling of the observations but also in the processing of the resulting data. To address this need, we are developing a planner-based agent to automatically generate and execute data-flow programs to produce the requested data products.

Golden, Keith↗
NASA NTRS2024

Optimized bandpasses for the Habitable Worlds Observatory's exoEarth survey

A primary scientific goal of the future Habitable Worlds Observatory will be the direct detection and characterization of Earth-like planets. Estimates of the exoplanet yields for this concept will help guide mission design through detailed trade studies. It is therefore critical that yield estimation codes optimally adapt observations to the mission’s performance parameters to ensure accurate trade studies. To aid in this, we implement wavelength optimization in yield calculations for the first time, allowing the yield code to determine the ideal detection and characterization bandpasses. We use this capability to confirm the observational wavelength assumptions made for the large UV/Optical/IR surveyor, design B (LUVOIR-B) study, namely that the optimum detection wavelength is 500 nm for the majority of targets and the optimum wavelength to detect water is near 1000 nm, given LUVOIR-B’s assumed instrument performance parameters. We show that including the wavelength-dependent albedo of an Earth twin as a prior provides no significant benefit to the yields of exoEarth candidates and caution against tuning observations to modern Earth twins. We also show that coronagraphs whose inner working angles are similar to step functions may benefit from wavelength optimization and demonstrate how wavelength-dependent instrument performance can impact the optimum wavelengths for detection and characterization. The optimization methods we implement automate wavelength selection and remove uncertainties regarding these choices, helping to adapt the observations to the instrument’s performance parameters.

telescopes↗
NASA NTRS2003

Automated Data Processing as an AI Planning Problem

NASA s vision for Earth Science is to build a "sensor web"; an adaptive array of heterogeneous satellites and other sensors that will track important events, such as storms, and provide real-time information about the state of the Earth to a wide variety of customers. Achieving his vision will require automation not only in the scheduling of the observations but also in the processing af tee resulting data. Ta address this need, we have developed a planner-based agent to automatically generate and execute data-flow programs to produce the requested data products. Data processing domains are substantially different from other planning domains that have been explored, and this has led us to substantially different choices in terms of representation and algorithms. We discuss some of these differences and discuss the approach we have adopted.

Golden, Keith↗
NASA NTRS2016

Automating Microbial Directed Evolution For Bioengineering Applications

From a micro-biology perspective, directed evolution is a technique that uses controlled environmental pressures to select for a desired phenotype. Directed evolution has the distinct advantage over rational design of not needing extensive knowledge of the genome or pathways associated with a microorganism to induce phenotypes. However, there are currently limitations to the applicability of this technique including being time-consuming, error-prone, and dependent on existing assays that may lack selectivity for the given phenotype. The AADEC (Autonomous Adaptive Directed Evolution Chamber) system is a proof-of-concept instrument to automate and improve the technique such that directed evolution can be used more effectively as a general bioengineering tool. A series of tests using the automated system and comparable by-hand survival assay measurements have been carried out using UV-C radiation and Escherichia coli cultures in order to demonstrate the advantages of the AADEC versus traditional implementations of directed evolution such as random mutagenesis. AADEC uses UV-C exposure as both a source of environmental stress and mutagenesis, so in order to evaluate the UV-C tolerance obtained from the cultures, a manual UV-C exposure survival assay was developed alongside the device to compare the survival fractions at a fixed dosage. This survival assay involves exposing E. coli to UV-C radiation using a custom-designed exposure hood to control the flux and dose. Surviving cells are counted then transferred to the next iteration and so on for several iterations to calculate the survival fractions for each exposure iteration.This survival assay primarily serves as a baseline for the AADEC device, allowing quantification of the differences between the AADEC system over the manual approach. The primary data of comparison is survival fractions; this is obtained by optical density and plate counts in the manual assay and by optical density growth curve fits pre- and post-exposure in the automated case. This data can then be compiled to calculate trends over the iterations to characterize increasing UV-C resistance of the E.coli strains. The observed trends are statistically indistinguishable through several iterations from both sources.

Bioengineering↗
NASA NTRS2008

Object Recognition using Feature- and Color-Based Methods

An improved adaptive method of processing image data in an artificial neural network has been developed to enable automated, real-time recognition of possibly moving objects under changing (including suddenly changing) conditions of illumination and perspective. The method involves a combination of two prior object-recognition methods one based on adaptive detection of shape features and one based on adaptive color segmentation to enable recognition in situations in which either prior method by itself may be inadequate. The chosen prior feature-based method is known as adaptive principal-component analysis (APCA); the chosen prior color-based method is known as adaptive color segmentation (ACOSE). These methods are made to interact with each other in a closed-loop system to obtain an optimal solution of the object-recognition problem in a dynamic environment. One of the results of the interaction is to increase, beyond what would otherwise be possible, the accuracy of the determination of a region of interest (containing an object that one seeks to recognize) within an image. Another result is to provide a minimized adaptive step that can be used to update the results obtained by the two component methods when changes of color and apparent shape occur. The net effect is to enable the neural network to update its recognition output and improve its recognition capability via an adaptive learning sequence. In principle, the improved method could readily be implemented in integrated circuitry to make a compact, low-power, real-time object-recognition system. It has been proposed to demonstrate the feasibility of such a system by integrating a 256-by-256 active-pixel sensor with APCA, ACOSE, and neural processing circuitry on a single chip. It has been estimated that such a system on a chip would have a volume no larger than a few cubic centimeters, could operate at a rate as high as 1,000 frames per second, and would consume in the order of milliwatts of power.

Duong, Tuan↗
NASA NTRS2009

Auto-Generated Semantic Processing Services

Auto-Generated Semantic Processing (AGSP) Services is a suite of software tools for automated generation of other computer programs, denoted cross-platform semantic adapters, that support interoperability of computer-based communication systems that utilize a variety of both new and legacy communication software running in a variety of operating- system/computer-hardware combinations. AGSP has numerous potential uses in military, space-exploration, and other government applications as well as in commercial telecommunications. The cross-platform semantic adapters take advantage of common features of computer- based communication systems to enforce semantics, messaging protocols, and standards of processing of streams of binary data to ensure integrity of data and consistency of meaning among interoperating systems. The auto-generation aspect of AGSP Services reduces development time and effort by emphasizing specification and minimizing implementation: In effect, the design, building, and debugging of software for effecting conversions among complex communication protocols, custom device mappings, and unique data-manipulation algorithms is replaced with metadata specifications that map to an abstract platform-independent communications model. AGSP Services is modular and has been shown to be easily integrable into new and legacy NASA flight and ground communication systems.

Davis, Rodney↗
NASA NTRSDate not supplied

Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility

Air transportation is undergoing a rapid evolution globally with the introduction of Advanced Air Mobility (AAM) and with it comes novel challenges and opportunities for transforming aviation. As AAM operations introduce increasing heterogeneity in vehicle capabilities and density, increased levels of automation are likely necessary to achieve operational safety and efficiency goals. This paper focuses on one example where increased automation has been suggested. Autonomous operations will need contingency management systems that can monitor evolving risk across a span of interrelated (or interdependent) hazards and, if necessary, execute appropriate control interventions via supervised or automated decision making. Accommodating this complex environment may require automated functions (autonomy) that apply artificial intelligence (AI) techniques that can adapt and respond to a quickly changing environment. This paper explores the use of Deep Reinforcement Learning (DRL) which has shown promising performance in complex and high-dimensional environments where the objective can be constructed as a sequential decision-making problem. An extension of a prior formulation of the contingency management problem as a Markov Decision Process (MDP) is presented and uses a DRL framework to train agents that mitigate hazards present in the simulation environment. A comparison of these learning-based agents and classical techniques is presented in terms of their performance, verification difficulties, and development process.

machine learningautonomous systems; flight simulat↗
NASA NTRSDate not supplied

Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility

Air transportation is undergoing a rapid evolution globally with the introduction of Advanced Air Mobility (AAM) and with it comes novel challenges and opportunities for transforming aviation. As AAM operations introduce increasing heterogeneity in vehicle capabilities and density, increased levels of automation are likely necessary to achieve operational safety and efficiency goals. This paper focuses on one example where increased automation has been suggested. Autonomous operations will need contingency management systems that can monitor evolving risk across a span of interrelated (or interdependent) hazards and, if necessary, execute appropriate control interventions via supervised or automated decision making. Accommodating this complex environment may require automated functions (autonomy) that apply artificial intelligence (AI) techniques that can adapt and respond to a quickly changing environment. This paper explores the use of Deep Reinforcement Learning (DRL) which has shown promising performance in complex and high-dimensional environments where the objective can be constructed as a sequential decision-making problem. An extension of a prior formulation of the contingency management problem as a Markov Decision Process (MDP) is presented and uses a DRL framework to train agents that mitigate hazards present in the simulation environment. A comparison of these learning-based agents and classical techniques is presented in terms of their performance, verification difficulties, and development process.

machine learning↗