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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

A Future Vision of a Data Acquisition: Distributed Sensing, Processing, and Health Monitoring

This paper presents a vision fo a highly enhanced data acquisition and health monitoring system at NASA Stennis Space Center (SSC) rocket engine test facility. This vision includes the use of advanced processing capabilities in conjunction with highly autonomous distributed sensing and intelligence, to monitor and evaluate the health of data in the context of it's associated process. This method is expected to significantly reduce data acquisitions costs and improve system reliability. A Universal Signal Conditioning Amplifier (USCA) based system, under development at Kennedy Space Center, is being evaluated for adaptation to the SSC testing infrastructure. Kennedy's USCA architecture offers many advantages including flexible and auto-configuring data acquisition with improved calibration and verifiability. Possible enhancements at SSC may include multiplexing the distributed USCAs to reduce per channel cost, and the use of IEEE-485 to Allen-Bradley Control Net Gateways for interfacing with the resident control systems.

Figueroa, Fernando↗

Compact Video Microscope Imaging System Implemented in Colloid Studies

Long description Photographs showing fiber-optic light source, microscope and charge-coupled discharge (CCD) camera head connected to camera body, CCD camera body feeding data to image acquisition board in PC, and Cartesian robot controlled via PC board. The Compact Microscope Imaging System (CMIS) is a diagnostic tool with intelligent controls for use in space, industrial, medical, and security applications. CMIS can be used in situ with a minimum amount of user intervention. This system can scan, find areas of interest in, focus on, and acquire images automatically. Many multiple-cell experiments require microscopy for in situ observations; this is feasible only with compact microscope systems. CMIS is a miniature machine vision system that combines intelligent image processing with remote control. The software also has a user-friendly interface, which can be used independently of the hardware for further post-experiment analysis. CMIS has been successfully developed in the SML Laboratory at the NASA Glenn Research Center and adapted for use for colloid studies and is available for telescience experiments. The main innovations this year are an improved interface, optimized algorithms, and the ability to control conventional full-sized microscopes in addition to compact microscopes. The CMIS software-hardware interface is being integrated into our SML Analysis package, which will be a robust general-purpose image-processing package that can handle over 100 space and industrial applications.

McDowell, Mark↗

Full-Scale Flight Research Testbeds: Adaptive and Intelligent Control

This viewgraph presentation describes the adaptive and intelligent control methods used for aircraft survival. The contents include: 1) Motivation for Adaptive Control; 2) Integrated Resilient Aircraft Control Project; 3) Full-scale Flight Assets in Use for IRAC; 4) NASA NF-15B Tail Number 837; 5) Gen II Direct Adaptive Control Architecture; 6) Limited Authority System; and 7) 837 Flight Experiments. A simulated destabilization failure analysis along with experience and lessons learned are also presented.

Pahle, Joe W.↗

Using fuzzy logic to integrate neural networks and knowledge-based systems

Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.

Yen, John↗

Investigation of Integrated Vehicle Health Management Approaches

This report is to present the work that was performed during the summer in the Advance Computing Application office. The NFFP (NASA Faculty Fellow Program) had ten summer faculty members working on IVHM (Integrated Vehicle Health Management) technologies. The objective of this project was two-fold: 1) to become familiar with IVHM concepts and key demonstrated IVHM technologies; and 2) to integrate the research that has been performed by IVHM faculty members into the MASTLAB (Marshall Avionic Software Test Lab). IVHM is a NASA-wide effort to coordinate, integrate and apply advanced software, sensors and design technologies to increase the level of intelligence, autonomy, and health state of future vehicles. IVHM is an important concept because it is consistent with the current plan for NASA to go to the moon, mars, and beyond. In order for NASA to become more involved in deep exploration, avionic systems will need to be highly adaptable and autonomous.

Paris, Deidre↗

Adaptive MGS Phase Retrieval

Adaptive MGS Phase Retrieval software uses the Modified Gerchberg-Saxton (MGS) algorithm, an image-based sensing method that can turn any focal plane science instrument into a wavefront sensor, avoiding the need to use external metrology equipment. Knowledge of the wavefront enables intelligent control of active optical systems.

Basinger, Scott A.↗

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↗

More About Architecture For Intelligent Robotic Control

Boolean neural networks proposed to implement part of intermediate level of hierarchical architecture of control system for artificially intelligent control of robot hand. Concept described in "Architecture for Intelligent Control of Robotic Tasks" (NPO-17871). Rule level of architecture implemented in two Boolean neural networks operated and updated in alternation. No explicit programming of network. Internal configuration not unique but, depends on initial state and history of previous adaptations. Accepts new rules sequentially presented by external controller.

Fiorini, Paolo↗

Intelligent tutoring using HyperCLIPS

HyperCard is a popular hypertext-like system used for building user interfaces to databases and other applications, and CLIPS is a highly portable government-owned expert system shell. We developed HyperCLIPS in order to fill a gap in the U.S. Army's computer-based instruction tool set; it was conceived as a development environment for building adaptive practical exercises for subject-matter problem-solving, though it is not limited to this approach to tutoring. Once HyperCLIPS was developed, we set out to implement a practical exercise prototype using HyperCLIPS in order to demonstrate the following concepts: learning can be facilitated by doing; student performance evaluation can be done in real-time; and the problems in a practical exercise can be adapted to the individual student's knowledge.

Hill, Randall W., Jr.↗

Learning and tuning fuzzy logic controllers through reinforcements

This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Berenji, Hamid R.↗

Learning and tuning fuzzy logic controllers through reinforcements

A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Berenji, Hamid R.↗

Intelligent fault-tolerant controllers

A system with fault tolerant controls is one that can detect, isolate, and estimate failures and perform necessary control reconfiguration based on this new information. Artificial intelligence (AI) is concerned with semantic processing, and it has evolved to include the topics of expert systems and machine learning. This research represents an attempt to apply AI to fault tolerant controls, hence, the name intelligent fault tolerant control (IFTC). A generic solution to the problem is sought, providing a system based on logic in addition to analytical tools, and offering machine learning capabilities. The advantages are that redundant system specific algorithms are no longer needed, that reasonableness is used to quickly choose the correct control strategy, and that the system can adapt to new situations by learning about its effects on system dynamics.

Huang, Chien Y.↗

An automated system for spacecraft proximity operations

With the advent of multiple-vehicle operations in support of the space station, on-orbit refurbishment, and several other missions, there is a need to intelligently plan proximity operations trajectories that will conserve limited available fuel while avoiding collisions. Upon reaching the objective, the capture process entails several unique considerations, such as coordinating motion with a tumbling target, the capture itself, and adapting to control of the new configuration resulting from the capture operation. This paper outlines a systematic process of technical development over several years at the Draper laboratory, culminating in a capability to perform manual augmented or fully autonomous rendezvous, capture, and control of the resulting configuration.

Bergmann, E.↗

Integration of task level planning and diagnosis for an intelligent robot

The use of robots in the future must go beyond present applications and will depend on the ability of a robot to adapt to a changing environment and to deal with unexpected scenarios (i.e., picking up parts that are not exactly where they were expected to be). The objective of this research is to demonstrate the feasibility of incorporating high level planning into a robot enabling it to deal with anomalous situations in order to minimize the need for constant human instruction. The heuristics can be used by a robot to apply information about previous actions towards accomplishing future objectives more efficiently. The system uses a decision network that represents the plan for accomplishing a task. This enables the robot to modify its plan based on results of previous actions. The system serves as a method for minimizing the need for constant human instruction in telerobotics. This paper describes the integration of expert systems and simulation as a valuable tool that goes far beyond this project. Simulation can be expected to be used increasingly as both hardware and software improve. Similarly, the ability to merge an expert system with simulation means that we can add intelligence to the system. A malfunctioning space satellite is described. The expert system uses a series of heuristics in order to guide the robot to the proper location. This is part of task level planning. The final part of the paper suggests directions for future research. Having shown the feasibility of an expert system embedded in a simulation, the paper then discusses how the system can be integrated with the MSFC graphics system.

Gerstenfeld, Arthur↗

PRIME: A bottom-up approach to probabilistic rule development

PRIME is a system to be used by an intelligent machine to allow it to operate in an abstract but uncertain (or stochastic) environment. It maintains a model of the effects of the machine's actions in the form of a rule base, which is induced from experience. This bottom-up approach to rule development allows the model to adapt to changes in the environment. Each rule consists of a condition under which the rule is active, an action, the effect of the action on the environment, and an estimate of the probability of this effect occurring. The effect probabilities are used to model the uncertainty in the environment, permitting multiple possible effects for a single action under a particular set of conditions. The objective of the intelligent machine is to satisfy user-specified goals with maximum probability of success. PRIME fulfills this requirement in two ways: it continuously updates the rule base with the most recent information, to ensure the validity of the model; and it generates plans which have the maximum probability of achieving the goals, based on the probability estimates in the rule base. PRIME is composed of three main processes: exploration, generalization, and planning. In exploration, the machine executes various randomly chosen actions, observes the effects on the environment, and updates the rule base accordingly. This process is used to develop the rule base in simulation, as well as to supplement the current knowledge during normal operation. Generalization is the procedure used to induce general rules from experience, which is encoded in the form of specific rules. These general rules extend the machine's knowledge to situations which have not been encountered yet, thereby increasing the capability of the machine to plan effectively. Planning is the process of constructing an optimal sequence of actions to satisfy a goal, using the rule base to predict the effects of these actions and to determine the probability of success of the plan. The rule representation and many other data structures were specifically chosen to maximize the efficiency of these processes. A simulated environment was designed to test the performance of PRIME. The results of experimentation were largely negative. The main problem was that the domain coverage of the rules was inadequate for the number of rules stored in the rule base, due to redundancies in general rules and numerous rules covering ineffective actions. It was determined that a more efficient generalization, and some form of goal-directed exploration, are necessary in order to solve most of the current deficiencies in PRIME.

Miller, Scott A.↗

Adaptive Behavior for Mobile Robots

The term "System for Mobility and Access to Rough Terrain" (SMART) denotes a theoretical framework, a control architecture, and an algorithm that implements the framework and architecture, for enabling a land-mobile robot to adapt to changing conditions. SMART is intended to enable the robot to recognize adverse terrain conditions beyond its optimal operational envelope, and, in response, to intelligently reconfigure itself (e.g., adjust suspension heights or baseline distances between suspension points) or adapt its driving techniques (e.g., engage in a crabbing motion as a switchback technique for ascending steep terrain). Conceived for original application aboard Mars rovers and similar autonomous or semi-autonomous mobile robots used in exploration of remote planets, SMART could also be applied to autonomous terrestrial vehicles to be used for search, rescue, and/or exploration on rough terrain.

Huntsberger, Terrance↗

An expert system based intelligent control scheme for space bioreactors

An expert system based intelligent control scheme is being developed for the effective control and full automation of bioreactor systems in space. The scheme developed will have the capability to capture information from various resources including heuristic information from process researchers and operators. The knowledge base of the expert system should contain enough expertise to perform on-line system identification and thus be able to adapt the controllers accordingly with minimal human supervision.

San, Ka-Yiu↗

Blindness in designing intelligent systems

New investigations of the foundations of artificial intelligence are challenging the hypothesis that problem solving is the cornerstone of intelligence. New distinctions among three domains of concern for humans--description, action, and commitment--have revealed that the design process for programmable machines, such as expert systems, is based on descriptions of actions and induces blindness to nonanalytic action and commitment. Design processes focusing in the domain of description are likely to yield programs like burearcracies: rigid, obtuse, impersonal, and unable to adapt to changing circumstances. Systems that learn from their past actions, and systems that organize information for interpretation by human experts, are more likely to be successful in areas where expert systems have failed.

Denning, Peter J.↗