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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Mobile Agents: A Distributed Voice-Commanded Sensory and Robotic System for Surface EVA Assistance

A model-based, distributed architecture integrates diverse components in a system designed for lunar and planetary surface operations: spacesuit biosensors, cameras, GPS, and a robotic assistant. The system transmits data and assists communication between the extra-vehicular activity (EVA) astronauts, the crew in a local habitat, and a remote mission support team. Software processes ("agents"), implemented in a system called Brahms, run on multiple, mobile platforms, including the spacesuit backpacks, all-terrain vehicles, and robot. These "mobile agents" interpret and transform available data to help people and robotic systems coordinate their actions to make operations more safe and efficient. Different types of agents relate platforms to each other ("proxy agents"), devices to software ("comm agents"), and people to the system ("personal agents"). A state-of-the-art spoken dialogue interface enables people to communicate with their personal agents, supporting a speech-driven navigation and scheduling tool, field observation record, and rover command system. An important aspect of the engineering methodology involves first simulating the entire hardware and software system in Brahms, and then configuring the agents into a runtime system. Design of mobile agent functionality has been based on ethnographic observation of scientists working in Mars analog settings in the High Canadian Arctic on Devon Island and the southeast Utah desert. The Mobile Agents system is developed iteratively in the context of use, with people doing authentic work. This paper provides a brief introduction to the architecture and emphasizes the method of empirical requirements analysis, through which observation, modeling, design, and testing are integrated in simulated EVA operations.

Clancey, William J.↗
NASA NTRS2000

Multi-Agent Diagnosis and Control of an Air Revitalization System for Life Support in Space

An architecture of interoperating agents has been developed to provide control and fault management for advanced life support systems in space. In this adjustable autonomy architecture, software agents coordinate with human agents and provide support in novel fault management situations. This architecture combines the Livingstone model-based mode identification and reconfiguration (MIR) system with the 3T architecture for autonomous flexible command and control. The MIR software agent performs model-based state identification and diagnosis. MIR identifies novel recovery configurations and the set of commands required for the recovery. The AZT procedural executive and the human operator use the diagnoses and recovery recommendations, and provide command sequencing. User interface extensions have been developed to support human monitoring of both AZT and MIR data and activities. This architecture has been demonstrated performing control and fault management for an oxygen production system for air revitalization in space. The software operates in a dynamic simulation testbed.

Malin, Jane T.↗
NASA NTRS1994

Coordinated scheduling for dynamic real-time systems

In this project, we addressed issues in coordinated scheduling for dynamic real-time systems. In particular, we concentrated on design and implementation of a new distributed real-time system called R-Shell. The design objective of R-Shell is to provide computing support for space programs that have large, complex, fault-tolerant distributed real-time applications. In R-shell, the approach is based on the concept of scheduling agents, which reside in the application run-time environment, and are customized to provide just those resource management functions which are needed by the specific application. With this approach, we avoid the need for a sophisticated OS which provides a variety of generalized functionality, while still not burdening application programmers with heavy responsibility for resource management. In this report, we discuss the R-Shell approach, summarize the achievement of the project, and describe a preliminary prototype of R-Shell system.

Natarajan, Swaminathan↗
NASA NTRS1995

Bicarbonate of soda paint stripping process validation and material characterization

The Aircraft Production Division at San Antonio Air Logistics Center has conducted extensive investigation into the replacement of hazardous chemicals in aircraft component cleaning, degreasing, and depainting. One of the most viable solutions is process substitution utilizing abrasive techniques. SA-ALC has incorporated the use of Bicarbonate of Soda Blasting as one such substitution. Previous utilization of methylene chloride based chemical strippers and carbon removal agents has been replaced by a walk-in blast booth in which we remove carbon from engine nozzles and various gas turbine engine parts, depaint cowlings, and perform various other functions on a variety of parts. Prior to implementation of this new process, validation of the process was performed, and materials and waste stream characterization studies were conducted. These characterization studies examined the effects of the blasting process on the integrity of the thin-skinned aluminum substrates, the effects of the process on both air emissions and effluent disposal, and the effects on the personnel exposed to the process.

Haas, Michael N.↗
NASA NTRS2000

Remote Agent Experiment

Remote Agent (RA) is a model-based, reusable artificial intelligence (At) software system that enables goal-based spacecraft commanding and robust fault recovery. RA was flight validated during an experiment on board of DS1 between May 17th and May 21th, 1999.

Benard, Doug↗
NASA NTRS2008

Nano-Aramid Fiber Reinforced Polyurethane Foam

Closed cell polyurethane and, particularly, polyisocyanurate foams are a large family of flexible and rigid products the result of a reactive two part process wherein a urethane based polyol is combined with a foaming or "blowing" agent to create a cellular solid at room temperature. The ratio of reactive components, the constituency of the base materials, temperature, humidity, molding, pouring, spraying and many other processing techniques vary greatly. However, there is no known process for incorporating reinforcing fibers small enough to be integrally dispersed within the cell walls resulting in superior final products. The key differentiating aspect from the current state of art resides in the many processing technologies to be fully developed from the novel concept of milled nano pulp aramid fibers and their enabling entanglement capability fully enclosed within the cell walls of these closed cell urethane foams. The authors present the results of research and development of reinforced foam processing, equipment development, strength characteristics and the evolution of its many applications.

Semmes, Edmund B.↗
NASA NTRS2015

An Integrated Framework for Modeling Air Carrier Behavior, Policy, and Impacts in the U.S. Air Transportation System

The implementation of the Next Generation Air Transportation System (NextGen) in the United States is an ongoing challenge for policymakers due to the complexity of the air transportation system (ATS) with its broad array of stakeholders and dynamic interdependencies between them. The successful implementation of NextGen has a hard dependency on the active participation of U.S. commercial airlines. To assist policymakers in identifying potential policy designs that facilitate the implementation of NextGen, the National Aeronautics and Space Administration (NASA) and LMI developed a research framework called the Air Transportation System Evolutionary Simulation (ATS-EVOS). This framework integrates large empirical data sets with multiple specialized models to simulate the evolution of the airline response to potential future policies and explore consequential impacts on ATS performance and market dynamics. In the ATS-EVOS configuration presented here, we leverage the Transportation Systems Analysis Model (TSAM), the Airline Evolutionary Simulation (AIRLINE-EVOS), the Airspace Concept Evaluation System (ACES), and the Aviation Environmental Design Tool (AEDT), all of which enable this research to comprehensively represent the complex facets of the ATS and its participants. We validated this baseline configuration of ATS-EVOS against Airline Origin and Destination Survey (DB1B) data and subject matter expert opinion, and we verified the ATS-EVOS framework and agent behavior logic through scenario-based experiments that explored potential implementations of a carbon tax, congestion pricing policy, and the dynamics for equipage of new technology by airlines. These experiments demonstrated ATS-EVOS's capabilities in responding to a wide range of potential NextGen-related policies and utility for decision makers to gain insights for effective policy design.

Horio, Brant M.↗
NASA NTRS2020

Multi-Agent Motion Planning using Deep Learning for Space Applications

State-of-the-art motion planners cannot scale to a large number of systems. Motion planning for multiple agents is an NP (non-deterministic polynomial-time) hard problem, so the computation time increases exponentially with each addition of agents. This computational demand is a major stumbling block to the motion planner's application to future NASA missions involving the swarm of space vehicles. We applied a deep neural network to transform computationally demanding mathematical motion planning problems into deep learning-based numerical problems. We showed optimal motion trajectories can be accurately replicated using deep learning-based numerical models in several 2D and 3D systems with multiple agents. The deep learning-based numerical model demonstrates superior computational efficiency with plans generated 1000 times faster than the mathematical model counterpart.

Madani, Ramtin↗
NASA NTRS1995

Building an adaptive agent to monitor and repair the electrical power system of an orbital satellite

Over several years we have developed a multistrategy apprenticeship learning methodology for building knowledge-based systems. Recently we have developed and applied our methodology to building intelligent agents. This methodology allows a subject matter expert to build an agent in the same way in which the expert would teach a human apprentice. The expert will give the agent specific examples of problems and solutions, explanations of these solutions, or supervise the agent as it solves new problems. During such interactions, the agent learns general rules and concepts, continuously extending and improving its knowledge base. In this paper we present initial results on applying this methodology to build an intelligent adaptive agent for monitoring and repair of the electrical power system of an orbital satellite, stressing the interaction with the expert during apprenticeship learning.

Tecuci, Gheorghe↗
NASA NTRS1993

Undecidability in macroeconomics

In this paper we study the difficulty of solving problems in economics. For this purpose, we adopt the notion of undecidability from recursion theory. We show that certain problems in economics are undecidable, i.e., cannot be solved by a Turing Machine, a device that is at least as powerful as any computational device that can be constructed. In particular, we prove that even in finite closed economies subject to a variable initial condition, in which a social planner knows the behavior of every agent in the economy, certain important social planning problems are undecidable. Thus, it may be impossible to make effective policy decisions. Philosophically, this result formally brings into question the Rational Expectations Hypothesis which assumes that each agent is able to determine what it should do if it wishes to maximize its utility. We show that even when an optimal rational forecast exists for each agency (based on the information currently available to it), agents may lack the ability to make these forecasts. For example, Lucas describes economic models as 'mechanical, artificial world(s), populated by ... interacting robots'. Since any mechanical robot can be at most as computationally powerful as a Turing Machine, such economies are vulnerable to the phenomenon of undecidability.

Chandra, Siddharth↗
NASA NTRS1990

Agent independent task planning

Agent-Independent Planning is a technique that allows the construction of activity plans without regard to the agent that will perform them. Once generated, a plan is then validated and translated into instructions for a particular agent, whether a robot, crewmember, or software-based control system. Because Space Station Freedom (SSF) is planned for orbital operations for approximately thirty years, it will almost certainly experience numerous enhancements and upgrades, including upgrades in robotic manipulators. Agent-Independent Planning provides the capability to construct plans for SSF operations, independent of specific robotic systems, by combining techniques of object oriented modeling, nonlinear planning and temporal logic. Since a plan is validated using the physical and functional models of a particular agent, new robotic systems can be developed and integrated with existing operations in a robust manner. This technique also provides the capability to generate plans for crewmembers with varying skill levels, and later apply these same plans to more sophisticated robotic manipulators made available by evolutions in technology.

Davis, William S.↗
NASA NTRS2022

Neurosymbolic Hybrid Approach to Driver Collision Warning

There are two main algorithmic approaches to autonomous driving systems: (1) An end-to-end system in which a single deep neural network learns to map sensory input directly into appropriate warning and driving responses. (2) A mediated hybrid recognition system in which a system is created by combining independent modules that detect each semantic feature. While some researchers believe that deep learning can solve any problem, others believe that a more engineered and symbolic approach is needed to cope with complex environments with less data. Deep learning alone has achieved state-of-the-art results in many areas, from complex gameplay to predicting protein structures. In particular, in image classification and recognition, deep learning models have achieved accuracies as high as humans. But sometimes it can be very difficult to debug if the deep learning model doesn't work. Deep learning models can be vulnerable and are very sensitive to changes in data distribution. Generalization can be problematic. It's usually hard to prove why it works or doesn't. Deep learning models can also be vulnerable to adversarial attacks. Here, we combine deep learning-based object recognition and tracking with an adaptive neurosymbolic network agent, called the Non-Axiomatic Reasoning System (NARS), that can adapt to its environment by building concepts based on perceptual sequences. We achieved an improved intersection-over-union (IOU) object recognition performance of 0.65 in the adaptive retraining model compared to IOU 0.31 in the COCO data pre-trained model. We improved the object detection limits using RADAR sensors in a simulated environment, and demonstrated the weaving car detection capability by combining deep learning-based object detection and tracking with a neurosymbolic model.

Wang, Pei↗
NASA NTRS1973

Evaluation of proposed Skylab and SSP soap products.

Four candidate cleansing agents evaluated in terms of potential hazards to crew members included two soaps (Neutrogena bar soap and Olive Leaf Liquid), one nonfoaming surfactant (Miranol JEM), and one laundry detergent (sodium dodecylbenzene sulfonate). None of the four exhibited adverse dermatological effects from skin patch tests or supported growth of potentially pathogenic microorganisms. Aqueous solutions of Neutrogena did support a mold species. Neutrogena and Miranol JEM were used in a simulated Skylab personal hygiene regimen with no adverse effects on skin or skin microflora. Based on our results, each of these agents appear to be a promising candidate material for the use intended.

Durfee, R. L.↗
NASA NTRS2015

Evaluation of Two Ionic Liquid-Based Epoxies from the MISSE-8 (Materials International Space Station Experiment-8) Sample Carrier

Two sets of epoxy mixtures, both containing the same ionic liquid (IL) based resin but utilizing two different curing agents, were evaluated after spending more than two years of continual space exposure outside of the International Space Station on the MISSE-8 sample rack. During this period the samples, positioned on nadir side, also experienced some 12,500 thermal cycles between approximately -40C and +40 C. Initial examination showed some color change, a miniscule weight variance, and no cracks or de-bonding from the sample substrate. Microscopic examination of the surface reveled some slight deformities and pitting. These observations, and others, are discussed in view of the ground-based control samples. Finally, the impetus of this study in terms of space applications is presented.

Rabenberg, Ellen↗
NASA NTRS2006

Multi-Agent System for Managing Human Activities in Space Operations

In manned space operations today, the astronauts' activity schedules are preplanned and adjusted daily on Earth. We have developed the Distributed Collaboration and Interaction (DCI) multi-agent system to investigate automating aspects of human activity management. The DCI System assists (1) plan generation, (2) human activity tracking, (3) plan revision, and (4) mixed initiative interaction with the plan. We have deployed and evaluated the DCI system at JSC to assist control engineers in managing anomaly handling activities for automated life support systems. DCI operated round the clock for 20 months in the Water Research Facility at JSC. Using this software, we reduced anomaly response time by engineers from up to 10 hours in previous tests to under an hour. Based on this evaluation, we conclude that agent assistance for schedule management has potential to improve astronaut activity awareness and reduce response time in situations where crew are interrupted to handle anomalies.

Schrenkenghost, Debra↗
NASA NTRS1986

Ultrasonic Mixing of Epoxy Curing Agents

New ultrasonic mixing technique used to mix several curing agents/epoxy combinations. Major component of commercially available base epoxy resin used in tetraglycidylmethylenedianiline (TGMDA). In ultrasonic mixing system cup holds resin and curing agent during acoustic excitation. Samples placed in cup with top to ultrasonic horn forming bottom of cup. Ultrasonically treated until amber colored and transparent. Because ultrasonic agitation drives out entrapped air, degassing not necessary before cure.

Hodges, W. T.↗
NASA NTRS2003

Experimental And Numerical Evaluation Of Gaseous Agents For Suppressing Cup-Burner Flames In Low Gravity

Longer duration missions to the moon, to Mars, and on the International Space Station (ISS) increase the likelihood of accidental fires. NASA's fire safety program for human-crewed space flight is based largely on removing ignition sources and controlling the flammability of the material on-board. There is ongoing research to improve the flammability characterization of materials in low gravity; however, very little research has been conducted on fire suppression in the low-gravity environment. Although the existing suppression systems aboard the Space Shuttle (halon 1301, CF3Br) and the ISS (CO2 or water-based form) may continue to be used, alternative effective agents or techniques are desirable for long-duration missions. The goal of the present investigation is to: (1) understand the physical and chemical processes of fire suppression in various gravity and O2 levels simulating spacecraft, Mars, and moon missions; (2) provide rigorous testing of analytical models, which include detailed combustion-suppression chemistry and radiation sub-models, so that the model can be used to interpret (and predict) the suppression behavior in low gravity; and (3) provide basic research results useful for advances in space fire safety technology, including new fire-extinguishing agents and approaches.

Takahashi, Fumiaki↗
NASA NTRS1989

Cooperating intelligent systems

Some of the issues connected to the development of a bureaucratic system are discussed. Emphasis is on a layer multiagent approach to distributed artificial intelligence (DAI). The division of labor in a bureaucracy is considered. The bureaucratic model seems to be a fertile model for further examination since it allows for the growth and change of system components and system protocols and rules. The first part of implementing the system would be the construction of a frame based reasoner and the appropriate B-agents and E-agents. The agents themselves should act as objects and the E-objects in particular should have the capability of taking on a different role. No effort was made to address the problems of automated failure recovery, problem decomposition, or implementation. Instead what has been achieved is a framework that can be developed in several distinct ways, and which provides a core set of metaphors and issues for further research.

Rochowiak, Daniel↗