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At least 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Analysis of airborne imaging spectrometer data for the Ruby Mountains, Montana, by use of absorption-band-depth images

Airborne Imaging Spectrometer-1 (AIS-1) data were obtained for an area of amphibolite grade metamorphic rocks that have moderate rangeland vegetation cover. Although rock exposures are sparse and patchy at this site, soils are visible through the vegetation and typically comprise 20 to 30 percent of the surface area. Channel averaged low band depth images for diagnostic soil rock absorption bands. Sets of three such images were combined to produce color composite band depth images. This relative simple approach did not require extensive calibration efforts and was effective for discerning a number of spectrally distinctive rocks and soils, including soils having high talc concentrations. The results show that the high spectral and spatial resolution of AIS-1 and future sensors hold considerable promise for mapping mineral variations in soil, even in moderately vegetated areas.

Brickey, David W.↗
NASA NTRS1987

Cooperative analysis expert situation assessment research

For the past few decades, Rome Air Development Center (RADC) has been conducting research in Artificial Intelligence (AI). When the recent advances in hardware technology made many AI techniques practical, the Intelligence and Reconnaissance Directorate of RADC initiated an applications program entitled Knowledge Based Intelligence Systems (KBIS). The goal of the program is the development of a generic Intelligent Analyst System, an open machine with the framework for intelligence analysis, natural language processing, and man-machine interface techniques, needing only the specific problem domain knowledge to be operationally useful. The development of KBIS is described.

Mccown, Michael G.↗
NASA NTRS1988

Expert system application education project

Artificial intelligence (AI) technology, and in particular expert systems, has shown potential applicability in many areas of operation at the Kennedy Space Center (KSC). In an era of limited resources, the early identification of good expert system applications, and their segregation from inappropriate ones can result in a more efficient use of available NASA resources. On the other hand, the education of students in a highly technical area such as AI requires an extensive hands-on effort. The nature of expert systems is such that proper sample applications for the educational process are difficult to find. A pilot project between NASA-KSC and the University of Central Florida which was designed to simultaneously address the needs of both institutions at a minimum cost. This project, referred to as Expert Systems Prototype Training Project (ESPTP), provided NASA with relatively inexpensive development of initial prototype versions of certain applications. University students likewise benefit by having expertise on a non-trivial problem accessible to them at no cost. Such expertise is indispensible in a hands-on training approach to developing expert systems.

Gonzelez, Avelino J.↗
NASA NTRS1988

Second Conference on Artificial Intelligence for Space Applications

The proceedings of the conference are presented. This second conference on Artificial Intelligence for Space Applications brings together a diversity of scientific and engineering work and is intended to provide an opportunity for those who employ AI methods in space applications to identify common goals and to discuss issues of general interest in the AI community.

Dollman, Thomas↗
NASA NTRS1988

Distributed cooperating processes in a mobile robot control system

A mobile inspection robot has been proposed for the NASA Space Station. It will be a free flying autonomous vehicle that will leave a berthing unit to accomplish a variety of inspection tasks around the Space Station, and then return to its berth to recharge, refuel, and transfer information. The Flying Eye robot will receive voice communication to change its attitude, move at a constant velocity, and move to a predefined location along a self generated path. This mobile robot control system requires integration of traditional command and control techniques with a number of AI technologies. Speech recognition, natural language understanding, task and path planning, sensory abstraction and pattern recognition are all required for successful implementation. The interface between the traditional numeric control techniques and the symbolic processing to the AI technologies must be developed, and a distributed computing approach will be needed to meet the real time computing requirements. To study the integration of the elements of this project, a novel mobile robot control architecture and simulation based on the blackboard architecture was developed. The control system operation and structure is discussed.

Skillman, Thomas L., Jr.↗
NASA NTRS1988

Artificial intelligence costs, benefits, risks for selected spacecraft ground system automation scenarios

In response to a number of high-level strategy studies in the early 1980s, expert systems and artificial intelligence (AI/ES) efforts for spacecraft ground systems have proliferated in the past several years primarily as individual small to medium scale applications. It is useful to stop and assess the impact of this technology in view of lessons learned to date, and hopefully, to determine if the overall strategies of some of the earlier studies both are being followed and still seem relevant. To achieve that end four idealized ground system automation scenarios and their attendant AI architecture are postulated and benefits, risks, and lessons learned are examined and compared. These architectures encompass: (1) no AI (baseline), (2) standalone expert systems, (3) standardized, reusable knowledge base management systems (KBMS), and (4) a futuristic unattended automation scenario. The resulting artificial intelligence lessons learned, benefits, and risks for spacecraft ground system automation scenarios are described.

Truszkowski, Walter F.↗
NASA NTRS1987

Remote mineralogical and vegetation mapping using imaging spectrometry

Two airborne flight systems were developed for geological studies: the airborne imaging spectrometer (AIS) and the advanced visible and infrared imaging spectrometer. A satellite imaging spectrometer system, the high resolution imaging spectrometer, is currently being developed and is planned for flight on the proposed NASA Earth Orbiting System. Examples of the application of these instruments in mineral and vegetation studies are described. Experiments conducted with AIS over the Cuprite Mining District (Nevada) and the Owens Valley (California) demonstrate the power of this new approach to remote sensing.

King, James, Jr.↗
NASA NTRS1988

Fourth Conference on Artificial Intelligence for Space Applications

Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming.

Odell, Stephen L.↗
NASA NTRS1988

Preliminary analysis of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) for mineralogic mapping at sites in Nevada and Colorado

Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data for sites in Nevada and Colorado were evaluated to determine their utility for mineralogical mapping in support of geologic investigations. Equal energy normalization is commonly used with imaging spectrometer data to reduce albedo effects. Spectra, profiles, and stacked, color-coded spectra were extracted from the AVIRIS data using an interactive analysis program (QLook) and these derivative data were compared to Airborne Imaging Spectrometer (AIS) results, field and laboratory spectra, and geologic maps. A feature extraction algorithm was used to extract and characterize absorption features from AVIRIS and laboratory spectra, allowing direct comparison of the position and shape of absorption features. Both muscovite and carbonate spectra were identified in the Nevada AVIRIS data by comparison with laboratory and AIS spectra, and an image was made that showed the distribution of these minerals for the entire site. Additional, distinctive spectra were located for an unknown mineral. For the two Colorado sites, the signal-to-noise problem was significantly worse and attempts to extract meaningful spectra were unsuccessful. Problems with the Colorado AVIRIS data were accentuated by the IAR reflectance technique because of moderate vegetation cover. Improved signal-to-noise and alternative calibration procedures will be required to produce satisfactory reflectance spectra from these data. Although the AVIRIS data were useful for mapping strong mineral absorption features and producing mineral maps at the Nevada site, it is clear that significant improvements to the instrument performance are required before AVIRIS will be an operational instrument.

Kruse, Fred A.↗
NASA NTRS1989

A knowledge-based tool for multilevel decomposition of a complex design problem

Although much work has been done in applying artificial intelligence (AI) tools and techniques to problems in different engineering disciplines, only recently has the application of these tools begun to spread to the decomposition of complex design problems. A new tool based on AI techniques has been developed to implement a decomposition scheme suitable for multilevel optimization and display of data in an N x N matrix format.

Rogers, James L.↗
NASA NTRS1989

STRUTEX: A prototype knowledge-based system for initially configuring a structure to support point loads in two dimensions

Only recently have engineers begun making use of Artificial Intelligence (AI) tools in the area of conceptual design. To continue filling this void in the design process, a prototype knowledge-based system, called STRUTEX has been developed to initially configure a structure to support point loads in two dimensions. This prototype was developed for testing the application of AI tools to conceptual design as opposed to being a testbed for new methods for improving structural analysis and optimization. This system combines numerical and symbolic processing by the computer with interactive problem solving aided by the vision of the user. How the system is constructed to interact with the user is described. Of special interest is the information flow between the knowledge base and the data base under control of the algorithmic main program. Examples of computed and refined structures are presented during the explanation of the system.

Robers, James L.↗
NASA NTRS1987

A space systems perspective of graphics simulation integration

Creation of an interactive display environment can expose issues in system design and operation not apparent from nongraphics development approaches. Large amounts of information can be presented in a short period of time. Processes can be simulated and observed before committing resources. In addition, changes in the economics of computing have enabled broader graphics usage beyond traditional engineering and design into integrated telerobotics and Artificial Intelligence (AI) applications. The highly integrated nature of space operations often tend to rely upon visually intensive man-machine communication to ensure success. Graphics simulation activities at the Mission Planning and Analysis Division (MPAD) of NASA's Johnson Space Center are focusing on the evaluation of a wide variety of graphical analysis within the context of present and future space operations. Several telerobotics and AI applications studies utilizing graphical simulation are described. The presentation includes portions of videotape illustrating technology developments involving: (1) coordinated manned maneuvering unit and remote manipulator system operations, (2) a helmet mounted display system, and (3) an automated rendezous application utilizing expert system and voice input/output technology.

Brown, R.↗
NASA NTRS1989

Shared resource control between human and computer

The advantages of an AI system of actively monitoring human control of a shared resource (such as a telerobotic manipulator) are presented. A system is described in which a simple AI planning program gains efficiency by monitoring human actions and recognizing when the actions cause a change in the system's assumed state of the world. This enables the planner to recognize when an interaction occurs between human actions and system goals, and allows maintenance of an up-to-date knowledge of the state of the world and thus informs the operator when human action would undo a goal achieved by the system, when an action would render a system goal unachievable, and efficiently replans the establishment of goals after human intervention.

Hendler, James↗
NASA NTRS1989

A situated reasoning architecture for space-based repair and replace tasks

Space-based robots need low level control for collision detection and avoidance, short-term load management, fine-grained motion, and other physical tasks. In addition, higher level control is required to focus strategic decision making as missions are assigned and carried out. Reasoning and control must be responsive to ongoing changes in the environment. Research aimed at bridging the gap between high level artificial intelligence (AI) planning techniques and task-level robot programming for telerobotic systems is described. Situated reasoning is incorporated into AI and Robotics systems in order to coordinate a robot's activity within its environment. An integrated system under development in a component maintenance domain is described. It is geared towards replacing worn and/or failed Orbital Replacement Units (ORUs) designed for use aboard NASA's Space Station Freedom based on the collection of components available at a given time. High level control reasons in component space in order to maximize the number operational component-cells over time, while the task-level controls sensors and effectors, detects collisions, and carries out pick and place tasks in physical space. Situated reasoning is used throughout the system to cope with component failures, imperfect information, and unexpected events.

Bloom, Ben↗
NASA NTRS1988

Estimation of forest canopy characteristics and nitrogen cycling using imaging spectrometry

Canopy lignin concentration is strongly related to annual rates of nitrogen mineralization in a series of Wisconsin forest ecosystems. High spectral resolution Airborne Imaging Spectrometer (AIS) data were acquired over these forests to investigate the potential of remotely estimating canopy lignin content. Analysis of the data using a derivative transformation and correlative techniques suggests that lignin or a closely associated cellular constituent is influential in canopy reflectance. Spatial distributions of percent canopy lignin and annual rates of nitrogen mineralization for Blackhawk Island, WI, have been estimated from a mosaic of AIS imagery.

Wessman, Carol A.↗
NASA NTRS1988

Launch vehicle operations cost reduction through artificial intelligence techniques

NASA's Kennedy Space Center has attempted to develop AI methods in order to reduce the cost of launch vehicle ground operations as well as to improve the reliability and safety of such operations. Attention is presently given to cost savings estimates for systems involving launch vehicle firing-room software and hardware real-time diagnostics, as well as the nature of configuration control and the real-time autonomous diagnostics of launch-processing systems by these means. Intelligent launch decisions and intelligent weather forecasting are additional applications of AI being considered.

Davis, Tom C., Jr.↗
NASA NTRS1988

Artificial intelligence costs, benefits, and risks for selected spacecraft ground system automation scenarios

In response to a number of high-level strategy studies in the early 1980s, expert systems and artificial intelligence (AI/ES) efforts for spacecraft ground systems have proliferated in the past several years primarily as individual small to medium scale applications. It is useful to stop and assess the impact of this technology in view of lessons learned to date, and hopefully, to determine if the overall strategies of some of the earlier studies both are being followed and still seem relevant. To achieve that end four idealized ground system automation scenarios and their attendant AI architecture are postulated and benefits, risks, and lessons learned are examined and compared. These architectures encompass: (1) no AI (baseline); (2) standalone expert systems; (3) standardized, reusable knowledge base management systems (KBMS); and (4) a futuristic unattended automation scenario. The resulting artificial intelligence lessons learned, benefits, and risks for spacecraft ground system automation scenarios are described.

Truszkowski, Walter F.↗
NASA NTRS1988

Machine intelligence and autonomy for aerospace systems

The present volume discusses progress toward intelligent robot systems in aerospace applications, NASA Space Program automation and robotics efforts, the supervisory control of telerobotics in space, machine intelligence and crew/vehicle interfaces, expert-system terms and building tools, and knowledge-acquisition for autonomous systems. Also discussed are methods for validation of knowledge-based systems, a design methodology for knowledge-based management systems, knowledge-based simulation for aerospace systems, knowledge-based diagnosis, planning and scheduling methods in AI, the treatment of uncertainty in AI, vision-sensing techniques in aerospace applications, image-understanding techniques, tactile sensing for robots, distributed sensor integration, and the control of articulated and deformable space structures.

Heer, Ewald↗