Results for “computational intelligence” — Page 15

NASA NTRS2005

Using EMG to anticipate head motion for virtual-environment applications

In virtual environment (VE) applications, where virtual objects are presented in a see-through head-mounted display, virtual images must be continuously stabilized in space in response to user's head motion. Time delays in head-motion compensation cause virtual objects to "swim" around instead of being stable in space which results in misalignment errors when overlaying virtual and real objects. Visual update delays are a critical technical obstacle for implementing head-mounted displays in applications such as battlefield simulation/training, telerobotics, and telemedicine. Head motion is currently measurable by a head-mounted 6-degrees-of-freedom inertial measurement unit. However, even given this information, overall VE-system latencies cannot be reduced under about 25 ms. We present a novel approach to eliminating latencies, which is premised on the fact that myoelectric signals from a muscle precede its exertion of force, thereby limb or head acceleration. We thus suggest utilizing neck-muscles' myoelectric signals to anticipate head motion. We trained a neural network to map such signals onto equivalent time-advanced inertial outputs. The resulting network can achieve time advances of up to 70 ms.

Clinical Trial↗

NASA NTRS1992

Intelligent fuzzy controller for event-driven real time systems

Most of the known linguistic models are essentially static, that is, time is not a parameter in describing the behavior of the object's model. In this paper we show a model for synchronous finite state machines based on fuzzy logic. Such finite state machines can be used to build both event-driven, time-varying, rule-based systems and the control unit section of a fuzzy logic computer. The architecture of a pipelined intelligent fuzzy controller is presented, and the linguistic model is represented by an overall fuzzy relation stored in a single rule memory. A VLSI integrated circuit implementation of the fuzzy controller is suggested. At a clock rate of 30 MHz, the controller can perform 3 MFLIPS on multi-dimensional fuzzy data.

Grantner, Janos↗

NASA NTRS2012

Health-Enabled Smart Sensor Fusion Technology

A process was designed to fuse data from multiple sensors in order to make a more accurate estimation of the environment and overall health in an intelligent rocket test facility (IRTF), to provide reliable, high-confidence measurements for a variety of propulsion test articles. The object of the technology is to provide sensor fusion based on a distributed architecture. Specifically, the fusion technology is intended to succeed in providing health condition monitoring capability at the intelligent transceiver, such as RF signal strength, battery reading, computing resource monitoring, and sensor data reading. The technology also provides analytic and diagnostic intelligence at the intelligent transceiver, enhancing the IEEE 1451.x-based standard for sensor data management and distributions, as well as providing appropriate communications protocols to enable complex interactions to support timely and high-quality flow of information among the system elements.

Wang, Ray↗

NASA NTRS2008

Spaceborne Processor Array

A Spaceborne Processor Array in Multifunctional Structure (SPAMS) can lower the total mass of the electronic and structural overhead of spacecraft, resulting in reduced launch costs, while increasing the science return through dynamic onboard computing. SPAMS integrates the multifunctional structure (MFS) and the Gilgamesh Memory, Intelligence, and Network Device (MIND) multi-core in-memory computer architecture into a single-system super-architecture. This transforms every inch of a spacecraft into a sharable, interconnected, smart computing element to increase computing performance while simultaneously reducing mass. The MIND in-memory architecture provides a foundation for high-performance, low-power, and fault-tolerant computing. The MIND chip has an internal structure that includes memory, processing, and communication functionality. The Gilgamesh is a scalable system comprising multiple MIND chips interconnected to operate as a single, tightly coupled, parallel computer. The array of MIND components shares a global, virtual name space for program variables and tasks that are allocated at run time to the distributed physical memory and processing resources. Individual processor- memory nodes can be activated or powered down at run time to provide active power management and to configure around faults. A SPAMS system is comprised of a distributed Gilgamesh array built into MFS, interfaces into instrument and communication subsystems, a mass storage interface, and a radiation-hardened flight computer.

Chow, Edward T.↗

NASA NTRS1988

Intelligent tutoring in the spacecraft command/control environment

The spacecraft command/control environment is becoming increasingly complex. As we enter the era of Space Station and the era of more highly automated systems, it is evident that the critical roles played by operations personnel in supervising the many required control center system components is becoming more cognitively demanding. In addition, the changing and emerging roles in the operations picture have far-reaching effects on the achievement of mission objectives. Thus highly trained and competent operations personnel are mandatory for success. Keeping pace with these developments has been computer-aided instruction utilizing various artificial intelligence technologies. The impacts of this growing capability on the stringent requirements for efficient and effective control center operations personnel is an area of much concentrated study. Some of the research and development of automated tutoring systems for the spacecraft command/control environment is addressed.

Truszkowski, Walter F.↗

NASA NTRSDate not supplied

NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing

NASA’s Small Spacecraft & Distributed Systems (SSDS) within the Research and Technology Mission Directorate (RTMD) expands U.S. capability to execute unique missions through targeted investment, rapid development, and flight demonstration of small spacecraft technologies applicable to exploration, science and the commercial space sector. SSDS strategically invests in technology development and on-orbit demonstrations executed across NASA, other government agencies, industry, and academia. The program’s University SmallSat Technology Partnerships initiative awards academic researchers with the opportunity to collaborate with NASA to mature innovative technology. Capabilities aligned with RTMD’s technology shortfalls and interests - power, processing, propulsion, sensors, communications, autonomous navigation, architectures, and advanced applications like artificial intelligence (AI), machine learning, and edge computing - are prioritized in SSDS investments. These investments enable distributed, autonomous, and cooperative small spacecraft systems that support swarm missions extending beyond low Earth orbit into cislunar and deep space. This paper highlights representative SSDS flight demonstrations that mature these capabilities to enable a future operational infrastructure needed to support sustained exploration of the Moon and beyond. SSDS’s investment strategy emphasizes rapid development and on-orbit demonstration to validate spacecraft technologies required for swarms and distributed mission architectures. The Starling swarm technology demonstration mission exemplifies this approach by advancing distributed spacecraft autonomy, cooperative operations, and space situational awareness. Extended flight testing and ongoing studies of next generation swarm configurations and on-orbit space traffic monitoring and management continue to inform future swarm designs. DiskSat’s four-spacecraft demonstration mission represents SSDS’s strategic vision to expand the design space for future small spacecraft through its commitment to advance novel platform concepts that can impact how science is performed on orbit. Continuing to invest in future platforms, the notional PY12 concept is a 12-spacecraft swarm hosting neuromorphic processors and is envisioned as an on-orbit testbed for AI, edge computing, and positioning, navigation and timing technologies. SSDS also invests in single-spacecraft technology demonstrations that underpin the success of future swarm missions and accelerate the availability of validated technologies across the small spacecraft ecosystem. Examples of such demonstrations include Pathfinder Technology Demonstrator-3 (PTD-3), which performed high-rate optical communications; PTD-R, which demonstrated a camera capable of simultaneous ultraviolet and short-wave infrared optical sensing; and CAPSTONE, the Cislunar Autonomous Positioning System Technology and Operations Navigation Experiment, which validated autonomous navigation in cislunar space. Collectively, SSDS-funded demonstrations advance capabilities across swarms and illustrate a coordinated investment strategy to mature high-impact technologies required for autonomous, distributed, and cooperative small spacecraft systems for low Earth orbit, cislunar, and deep space applications. Technology demonstrations strengthen SSDS partnerships with industry, academia, and other government agencies, and promote small spacecraft community adoption of capabilities required to close technical gaps for swarm missions.

Jan Stupl↗

NASA NTRSDate not supplied

NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing

NASA’s Small Spacecraft & Distributed Systems (SSDS) within the Research and Technology Mission Directorate (RTMD) expands U.S. capability to execute unique missions through targeted investment, rapid development, and flight demonstration of small spacecraft technologies applicable to exploration, science and the commercial space sector. SSDS strategically invests in technology development and on-orbit demonstrations executed across NASA, other government agencies, industry, and academia. The program’s University SmallSat Technology Partnerships initiative awards academic researchers with the opportunity to collaborate with NASA to mature innovative technology. Capabilities aligned with RTMD’s technology shortfalls and interests - power, processing, propulsion, sensors, communications, autonomous navigation, architectures, and advanced applications like artificial intelligence (AI), machine learning, and edge computing - are prioritized in SSDS investments. These investments enable distributed, autonomous, and cooperative small spacecraft systems that support swarm missions extending beyond low Earth orbit into cislunar and deep space. This paper highlights representative SSDS flight demonstrations that mature these capabilities to enable a future operational infrastructure needed to support sustained exploration of the Moon and beyond. SSDS’s investment strategy emphasizes rapid development and on-orbit demonstration to validate spacecraft technologies required for swarms and distributed mission architectures. The Starling swarm technology demonstration mission exemplifies this approach by advancing distributed spacecraft autonomy, cooperative operations, and space situational awareness. Extended flight testing and ongoing studies of next generation swarm configurations and on-orbit space traffic monitoring and management continue to inform future swarm designs. DiskSat’s four-spacecraft demonstration mission represents SSDS’s strategic vision to expand the design space for future small spacecraft through its commitment to advance novel platform concepts that can impact how science is performed on orbit. Continuing to invest in future platforms, the notional PY12 concept is a 12-spacecraft swarm hosting neuromorphic processors and is envisioned as an on-orbit testbed for AI, edge computing, and positioning, navigation and timing technologies. SSDS also invests in single-spacecraft technology demonstrations that underpin the success of future swarm missions and accelerate the availability of validated technologies across the small spacecraft ecosystem. Examples of such demonstrations include Pathfinder Technology Demonstrator-3 (PTD-3), which performed high-rate optical communications; PTD-R, which demonstrated a camera capable of simultaneous ultraviolet and short-wave infrared optical sensing; and CAPSTONE, the Cislunar Autonomous Positioning System Technology and Operations Navigation Experiment, which validated autonomous navigation in cislunar space. Collectively, SSDS-funded demonstrations advance capabilities across swarms and illustrate a coordinated investment strategy to mature high-impact technologies required for autonomous, distributed, and cooperative small spacecraft systems for low Earth orbit, cislunar, and deep space applications. Technology demonstrations strengthen SSDS partnerships with industry, academia, and other government agencies, and promote small spacecraft community adoption of capabilities required to close technical gaps for swarm missions.

Jan Stupl↗

NASA NTRS2019

The NASA Urban Air Mobility Testbed Flight Research Aircraft

The National Aeronautics and Space Administration (NASA) is leading government, industry and academic research effort known as Urban Air Mobility (UAM). The UAM activity goal is to develop the technology needed to make possible an urban air transportation system that makes use of human-crewed and crewless vehicles using automation, artificial intelligence and other technologies to safely and efficiently air transport people and goods within an urban environment. The NASA Langley Research Center (LaRC) created a UAM Flight Research Testbed Aircraft from a Cessna LC40 general aviation aircraft. The testbed has updated digital avionics and research systems needed to conduct flight research. The aircraft has two separate autopilots; a standard Federal Aviation Ad-ministration (FAA) certified system, and a modified research autopilot. The research autopilot has modifications that allow increased authority and enhancements to allow more automation and artificial intelligence controls. A network of three research computers hosts software to research several activities including automated air traffic sense and avoid, ground collision avoidance and obstacle avoidance. These UAM research activities involve automation and artificial intelligence technologies developed at three NASA centers. The NASA Langley, NASA Armstrong, and NASA Ames Research Centers are working together to develop and test these UAM technologies. This paper provides details of the systems, capabilities and research projects of the UAM Testbed Research Aircraft.

Howell, Charles T.↗

NASA NTRS1991

Valve- And Switch-Monitoring Computer Program

Human operators freed from tedious, repetitive monitoring tasks. Computer program applies techniques of artificial intelligence to monitoring positions of many switches and valves. Uses combination of procedural and declarative programming techniques. NASA's C Language Integrated Production System (CLIPS) provides rule-processing capabilities. Host program, written in C, acquires necessary data and applies valuation algorithm to generate knowledge-based propositions. Written to assist human flight controllers in comparing actual with expected configuration of switches and valves in Space Shuttle; underlying programming concept applicable to other complicated systems as chemical-processing plants, power-plants, and automated assembly lines. Program works with present monitoring equipment and computers.

Barry, Matthew R.↗

NASA NTRS1981

Talking Wheelchair

Communication is made possible for disabled individuals by means of an electronic system, developed at Stanford University's School of Medicine, which produces highly intelligible synthesized speech. Familiarly known as the "talking wheelchair" and formally as the Versatile Portable Speech Prosthesis (VPSP). Wheelchair mounted system consists of a word processor, a video screen, a voice synthesizer and a computer program which instructs the synthesizer how to produce intelligible sounds in response to user commands. Computer's memory contains 925 words plus a number of common phrases and questions. Memory can also store several thousand other words of the user's choice. Message units are selected by operating a simple switch, joystick or keyboard. Completed message appears on the video screen, then user activates speech synthesizer, which generates a voice with a somewhat mechanical tone. With the keyboard, an experienced user can construct messages as rapidly as 30 words per minute.

Source record↗

NASA NTRS1979

TMS communications hardware. Volume 1: Computer interfaces

A prototpye coaxial cable bus communications system was designed to be used in the Trend Monitoring System (TMS) to connect intelligent graphics terminals (based around a Data General NOVA/3 computer) to a MODCOMP IV host minicomputer. The direct memory access (DMA) interfaces which were utilized for each of these computers are identified. It is shown that for the MODCOMP, an off-the-shell board was suitable, while for the NOVAs, custon interface circuitry was designed and implemented.

Brown, J. S.↗

NASA NTRS1994

A Spacelab Expert System for Remote Engineering and Science

NASA's space science program is based on strictly pre-planned activities. This approach does not always result in the best science. We describe an existing computer system that enables space science to be conducted in a more reactive manner through advanced automation techniques that have recently been used in SLS-2 October 1993 space shuttle flight. Advanced computing techniques, usually developed in the field of Artificial Intelligence, allow large portions of the scientific investigator's knowledge to be "packaged" in a portable computer to present advice to the astronaut operator. We strongly believe that this technology has wide applicability to other forms of remote science/engineering. In this brief article, we present the technology of remote science/engineering assistance as implemented for the SLS-2 space shuttle flight. We begin with a logical overview of the system (paying particular attention to the implementation details relevant to the use of the embedded knowledge for system reasoning), then describe its use and success in space, and conclude with ideas about possible earth uses of the technology in the life and medical sciences.

Groleau, Nick↗

NASA NTRS2004

Intelligent Systems: Shaping the Future of Aeronautics and Space Exploration

Intelligent systems are nature-inspired, mathematically sound, computationally intensive problem solving tools and methodologies that have become important for NASA's future roles in Aeronautics and Space Exploration. Intelligent systems will enable safe, cost and mission-effective approaches to air& control, system design, spacecraft autonomy, robotic space exploration and human exploration of Moon, Mars, and beyond. In this talk, we will discuss intelligent system technologies and expand on the role of intelligent systems in NASA's missions. We will also present several examples of which some are highlighted m this extended abstract.

Krishnakumar, Kalmanje↗

NASA NTRS1993

Evolution of a high-performance storage system based on magnetic tape instrumentation recorders

In order to provide transparent access to data in network computing environments, high performance storage systems are getting smarter as well as faster. Magnetic tape instrumentation recorders contain an increasing amount of intelligence in the form of software and firmware that manages the processes of capturing input signals and data, putting them on media and then reproducing or playing them back. Such intelligence makes them better recorders, ideally suited for applications requiring the high-speed capture and playback of large streams of signals or data. In order to make recorders better storage systems, intelligence is also being added to provide appropriate computer and network interfaces along with services that enable them to interoperate with host computers or network client and server entities. Thus, recorders are evolving into high-performance storage systems that become an integral part of a shared information system. Data tape has embarked on a program with the Caltech sponsored Concurrent Supercomputer Consortium to develop a smart mass storage system. Working within the framework of the emerging IEEE Mass Storage System Reference Model, a high-performance storage system that works with the STX File Server to provide storage services for the Intel Touchstone Delta Supercomputer is being built. Our objective is to provide the required high storage capacity and transfer rate to support grand challenge applications, such as global climate modeling.

Peters, Bruce↗

NASA NTRS1989

Diverter AI based decision aid, phases 1 and 2

It was determined that a system to incorporate artificial intelligence (AI) into airborne flight management computers is feasible. The AI functions that would be most useful to the pilot are to perform situational assessment, evaluate outside influences on the contemplated rerouting, perform flight planning/replanning, and perform maneuver planning. A study of the software architecture and software tools capable of demonstrating Diverter was also made. A skeletal planner known as the Knowledge Acquisition Development Tool (KADET), which is a combination script-based and rule-based system, was used to implement the system. A prototype system was developed which demonstrates advanced in-flight planning/replanning capabilities.

Sexton, George A.↗

NASA NTRS1990

Object oriented fault diagnosis system for space shuttle main engine redlines

A great deal of attention has recently been given to Artificial Intelligence research in the area of computer aided diagnostics. Due to the dynamic and complex nature of space shuttle red-line parameters, a research effort is under way to develop a real time diagnostic tool that will employ historical and engineering rulebases as well as a sensor validity checking. The capability of AI software development tools (KEE and G2) will be explored by applying object oriented programming techniques in accomplishing the diagnostic evaluation.

Rogers, John S.↗

NASA NTRS1989

Design and control of a multi-fingered robot hand provided with tactile feedback

The design, construction, control and application of a three fingered robot hand with nine degrees of freedom and built-in multi-component force sensors is described. The adopted gripper kinematics are justified and optimized with respect to grasping and manipulation flexibility. The hand was constructed with miniature motor drive systems imbedded into the fingers. The control is hierarchically structured and is implemented on a simple PC-AT computer. The hand's dexterity and intelligence are demonstrated with some experiments.

Vanbrussel, H.↗

NASA NTRS1991

Acquisition and improvement of human motor skills: Learning through observation and practice

Skilled movement is an integral part of the human existence. A better understanding of motor skills and their development is a prerequisite to the construction of truly flexible intelligent agents. We present MAEANDER, a computational model of human motor behavior, that uniformly addresses both the acquisition of skills through observation and the improvement of skills through practice. MAEANDER consists of a sensory-effector interface, a memory of movements, and a set of performance and learning mechanisms that let it recognize and generate motor skills. The system initially acquires such skills by observing movements performed by another agent and constructing a concept hierarchy. Given a stored motor skill in memory, MAEANDER will cause an effector to behave appropriately. All learning involves changing the hierarchical memory of skill concepts to more closely correspond to either observed experience or to desired behaviors. We evaluated MAEANDER empirically with respect to how well it acquires and improves both artificial movement types and handwritten script letters from the alphabet. We also evaluate MAEANDER as a psychological model by comparing its behavior to robust phenomena in humans and by considering the richness of the predictions it makes.

Iba, Wayne↗