Results for “intelligent vehicles” — Page 27

NASA NTRS2007

Hybrid Modeling Improves Health and Performance Monitoring

Scientific Monitoring Inc. was awarded a Phase I Small Business Innovation Research (SBIR) project by NASA's Dryden Flight Research Center to create a new, simplified health-monitoring approach for flight vehicles and flight equipment. The project developed a hybrid physical model concept that provided a structured approach to simplifying complex design models for use in health monitoring, allowing the output or performance of the equipment to be compared to what the design models predicted, so that deterioration or impending failure could be detected before there would be an impact on the equipment's operational capability. Based on the original modeling technology, Scientific Monitoring released I-Trend, a commercial health- and performance-monitoring software product named for its intelligent trending, diagnostics, and prognostics capabilities, as part of the company's complete ICEMS (Intelligent Condition-based Equipment Management System) suite of monitoring and advanced alerting software. I-Trend uses the hybrid physical model to better characterize the nature of health or performance alarms that result in "no fault found" false alarms. Additionally, the use of physical principles helps I-Trend identify problems sooner. I-Trend technology is currently in use in several commercial aviation programs, and the U.S. Air Force recently tapped Scientific Monitoring to develop next-generation engine health-management software for monitoring its fleet of jet engines. Scientific Monitoring has continued the original NASA work, this time under a Phase III SBIR contract with a joint NASA-Pratt & Whitney aviation security program on propulsion-controlled aircraft under missile-damaged aircraft conditions.

Source record↗

DOE OSTI2025

Harnessing the Power of AI: Status and Expansion of Current Domestic Transport Security Through Flexible Embedded Hardware

As applications of Artificial Intelligence (AI) continue to expand, there are increasing opportunities to leverage applied AI methodologies with mobile transportation focused embedded systems. Current applications of AI in transportation focus on a variety of areas, including fuel efficiency, safety, security, and other broad fields of optimization or detection. To leverage these AI workflows and methodologies in the field, teams must utilize complex embedded systems capable of implementing these AI-enabled algorithms in real-time. In this paper, we will investigate how these algorithms can be integrated into existing technologies leveraging vehicle data - such as the Controller Area Network Transport Security Tracking and Reporting Unit (C-STAR). The C-STAR technology is an embedded platform with onboard computation capable of running next generation algorithms in vehicle systems AI, such as preventative maintenance, driver authentication, and transport security. As deployed in the field, the C-STAR has a limited AI functionality –this paper will directly discuss how a device like C-STAR can be utilized and the advantages of integrating these new technologies. We will open with relevant background information and transportation projects that leverage AI, focusing specifically on those around transport security such as vehicle identification, anomaly detection, and deterrence. We will then extend this into potential opportunities and scaling for AI methodologies using platforms like the C-STAR. Finally, we will speak directly to the challenges of deploying AI-powered workflows, such as computing power needs, bandwidth, hallucinations, and other regulatory considerations.

Cook, Adian [ORNL] (ORCID:0000000160825395)↗

NASA NTRS1986

Investigation of crew performance in a multi-vehicle supervisory control task

Crew information processing and decision making in a supervisory control task which is loosely based on the mission of future generation helicopters is measured and represented. Subjects control the motion and activities of their own vehicle and direct the activities of four additional craft. The task involves searching an uncertain environment for cargo and enemies, returning cargo to home base and destroying enemies while attempting to avoid destruction of the scout and the supervised vehicles. A series of experiments with two-person crews and one-person crews were performed. Resulting crew performance was modeled with the objective of describing and understanding the information processing strategies utilized. Of particular interest are problem simplification strategies under time stress and high work load, simplification and compensation in the one-person cases, crew coordination in the two-person cases, and the relationship between strategy and errors in all cases. The results should provide some insight into the effective use of aids, particularly aids based on artificial intelligence, for similar tasks. The simulation is described which is used for the study and some preliminary results from the first two-person crew study are discussed.

Miller, R. A.↗

NASA NTRS2008

Development and Evaluation of Sensor Concepts for Ageless Aerospace Vehicles: Report 3 - Design of the Concept Demonstrator

This report provides an outline of the essential features of a Structural Health Monitoring Concept Demonstrator (CD) that will be constructed during the next eight months. It is emphasized that the design cannot be considered to be complete, and that design work will continue in parallel with construction and testing. A major advantage of the modular design is that small modules of the system can be developed, tested and modified before a commitment is made to full system development. The CD is expected to develop and evolve for a number of years after its initial construction. This first stage will, of necessity, be relatively simple and have limited capabilities. Later developments will improve all aspects of the functionality of the system, including sensing, processing, communications, intelligence and response. The report indicates the directions this later development will take.

Abbott, David↗

NASA NTRS1988

Expert systems for adaptive control of large space structures

It is expected that space systems for the future will evolve to structures of unprecedented size with associated extreme control requirements. A method is necessary that is sufficiently general to initiate stable control of a vehicle and subsequently learn the true nature of the structure. It is suggested that a suitable constructed expert system (ES) would be capable of learning by appending observations to a knowledge base. To verify that an ES can control a large space structure, numerical simulations of a simple structure subjected to periodic vibrations and the performance of a classical controllers were performed. The ES was then exercised to show its ability to truthfully mimic nominal control and to demonstrate its superiority to the classical controller, given sensor failures. An ES generating software package, The Intelligent Machine Model, was employed. It uses the pattern matching technique. Results of this program are discussed.

Gartrell, Charles F.↗

NASA NTRS2001

Team Expo: A State-of-the-Art JSC Advanced Design Team

In concert with the NASA-wide Intelligent Synthesis Environment Program, the Exploration Office at the Johnson Space Center has assembled an Advanced Design Team. The purpose of this team is two-fold. The first is to identify, use, and develop software applications, tools, and design processes that streamline and enhance a collaborative engineering environment. The second is to use this collaborative engineering environment to produce conceptual, system-level-of-detail designs in a relatively short turnaround time, using a standing team of systems and integration experts. This includes running rapid trade studies on varying mission architectures, as well as producing vehicle and/or subsystem designs. The standing core team is made up of experts from all of the relevant engineering divisions (e.g. Power, Thermal, Structures, etc.) as well as representatives from Risk and Safety, Mission Operations, and Crew Life Sciences among others. The Team works together during 2- hour sessions in the same specially enhanced room to ensure real-time integration/identification of cross-disciplinary issues and solutions. All subsystem designs are collectively reviewed and approved during these same sessions. In addition there is an Information sub-team that captures and formats all data and makes it accessible for use by the following day. The result is Team Expo: an Advanced Design Team that is leading the change from a philosophy of "over the fence" design to one of collaborative engineering that pushes the envelope to achieve the next-generation analysis and design environment.

Tripathi, Abhishek↗

NASA NTRS2019

Overview of the PLEXIL Plan Execution Technology and its Applications in Autonomous Piloting Projects at NASA

Automated planning is a key Artificial Intelligence technology enabling Unmanned Aerial Systems (UAS) and the eminent reality of Urban Air Mobility (UAM). It produces plans, which formalize procedures often performed by humans. Plans differ from other kinds of computer programs in their ability to react and interact with a dynamically changing environment. Aviation plans must encode the procedural knowledge, reasoning capability, and capacity for multi-tasking held by competent human pilots. Correct execution of these plans (performed by software called an executive) in the dynamic airspace environment is vital to the success of each automated flight, and the safety of the vehicle and all things in its path. In the early 2000s NASA developed a plan representation language and executive called PLEXIL (Plan Execution Interchange Language) that has successfully been applied in several NASA aviation and UAS projects. Autonomy Operating System (AOS), Cockpit Hierarchical Automated Planning and Execution (CHAP-E), and ICAROUS are all projects that have used PLEXIL to help encode and automatically execute flight procedures, some normally performed by human pilots. AOS also automates a subset of pilot/Air Traffic Control communication towards enabling UAS entry into the National Airspace. PLEXIL has been open-source software since 2008 and has seen usage in a wide range of prototypical autonomy applications in academia, government, and industry. In this presentation, we describe PLEXIL and highlight its significant accomplishments in the aviation domain.

Dalal, Michael↗

NASA NTRS1994

Intelligent computer-aided training authoring environment

Although there has been much research into intelligent tutoring systems (ITS), there are few authoring systems available that support ITS metaphors. Instructional developers are generally obliged to use tools designed for creating on-line books. We are currently developing an authoring environment derived from NASA's research on intelligent computer-aided training (ICAT). The ICAT metaphor, currently in use at NASA has proven effective in disciplines from satellite deployment to high school physics. This technique provides a personal trainer (PT) who instructs the student using a simulated work environment (SWE). The PT acts as a tutor, providing individualized instruction and assistance to each student. Teaching in an SWE allows the student to learn tasks by doing them, rather than by reading about them. This authoring environment will expedite ICAT development by providing a tool set that guides the trainer modeling process. Additionally, this environment provides a vehicle for distributing NASA's ICAT technology to the private sector.

Way, Robert D.↗

NASA NTRSDate not supplied

Dynamic Channel Assignments for Efficient Use of Aviation Spectrum Allocations

The demand for voice and data communications continues to rise with the emergence of new aerial vehicles into the airspace and the continued growth of aviation operations throughout the National Airspace System (NAS). Recent studies have shown that the anticipated growing demand for spectrum resources will exceed the capacity of existing aviation spectrum allocations. Further, airspace configurations, via assignment of fixed channel allocations within standard service volumes, do not allow for the dynamic and efficient distribution of spectrum resources based on airspace demand; as a result, a new approach to aviation spectrum management is needed to support the forecasted needs of new airspace users. The National Aeronautics and Space Administration (NASA) is investigating applications of artificial intelligence (AI), machine learning (ML), and other advanced concepts to solve a dynamic constraint satisfaction problem which is analogous to the frequency assignment problem faced by aviation. Procedures and strategies for dynamic channel allocation can be borrowed from other large-scale mobile services (i.e., 4G/5G applications) and can provide a novel spectrum management approach that allows for the intelligent utilization of aviation spectrum throughout the airspace while maintaining the strict quality of service prescribed by aeronautical standards.

Communications↗

NASA NTRSDate not supplied

Dynamic Channel Assignments for Efficient Use of Aviation Spectrum Allocations

The demand for voice and data communications continues to rise with the emergence of new aerial vehicles into the airspace and the continued growth of aviation operations throughout the National Airspace System (NAS). Recent studies have shown that the anticipated growing demand for spectrum resources will exceed the capacity of existing aviation spectrum allocations. Further, airspace configurations, via assignment of fixed channel allocations within standard service volumes, do not allow for the dynamic and efficient distribution of spectrum resources based on airspace demand; as a result, a new approach to aviation spectrum management is needed to support the forecasted needs of new airspace users. The National Aeronautics and Space Administration (NASA) is investigating applications of artificial intelligence (AI), machine learning (ML), and other advanced concepts to solve a dynamic constraint satisfaction problem which is analogous to the frequency assignment problem faced by aviation. Procedures and strategies for dynamic channel allocation can be borrowed from other large-scale mobile services (i.e., 4G/5G applications) and can provide a novel spectrum management approach that allows for the intelligent utilization of aviation spectrum throughout the airspace while maintaining the strict quality of service prescribed by aeronautical standards.

communications↗

NASA NTRS2019

Overview of the Communications and Intelligent Systems Division at the NASA Glenn Research Center

NASA's plans for advancing aeronautics and space exploration requires optimization of current capabilities and development of new ones to support its mission. A key requirement associated with these efforts is the constant upgrading and optimization of communication systems. This presentation will provide a brief overview of NASA efforts, with emphasis on those being performed at the NASA Glenn Research Center, to develop communications technologies and systems in support of NASA aerospace communications needs. Status of specific technology examples in the area of cognitive communication systems, Advanced RF Technologies, Networks, and Systems Architectures will be presented and their impact in the overall scheme of needs will be discussed. In addition, cross- cutting technologies which enable sensing, control and communications technologies to perform in extreme environments as well as non-destructive evaluations techniques for vehicle health monitoring and related purposes will be presented.

Miranda, Felix A.↗

NASA NTRSDate not supplied

Datascope to Enable Earth Independent Medical Operations (EIMO)

BACKGROUND: NASA has amassed sixty years of knowledge and experience relevant to maintenance of crew health and performance in low earth orbit. The Apollo Program introduced the importance of ensuring progressively autonomous operational capability. Earth Independent Medical Operations (EIMO) will require a gradual shift in the balance of medical responsibility, management, and authority from terrestrial to space-based assets. Terrestrial assets will continue to be essential for pre-mission screening and planning in addition to maintenance of crew health and performance. However, new capabilities are needed to enable EIMO and the amount of data required to support these systems, and mitigate the impacts of data transmission delays and reduced bandwidth coupled with lack of cloud-like resources and on-board computing capacity that is currently unclear or operationally insufficient. OVERVIEW: The overall goal of EIMO is to develop artificial intelligence (AI)-based solutions to analyze crew health and performance data utilizing a clinical decision support system (CDSS) to provide crew medical officers (CMO) with the equivalent of real-time, on-board medical consults. The EIMO ecosystem is envisioned as a “system of systems” where embedded reference databases and real-time data streams from multiple input vectors continuously and seamlessly assess crew health and performance. EIMO will be designed to make recommendations to the CMO using multi-modal AI-based natural language processing and machine learning methods with interoperability to push/pull data within and between multiple vehicle and habitat architectures. DISCUSSION: Data flows and storage/retrieval capacity are severely constrained during space missions and the challenges will become even greater during exploration missions. Just as each past program from Mercury to the International Space Station (ISS) required rethinking the interaction between ground-based controllers and space-based crew, so too will future missions to the Moon and Mars. While the NASA High-Performance Spaceflight Computing Processor project aims to increase computational capacity by 100 times over current spaceflight computers, the projected deliverable still lags considerably behind what will be needed to enable an AI-driven CDSS. Restrictions in processing speed and data storage capacity, coupled with transmission bottlenecks and delays, necessitate definition and optimization of an integrated data architecture to enable a progressively autonomous medical capability.

Medical operations↗

NASA NTRSDate not supplied

A 3D Simulation Platform for Decentralized Decision-Making in Advanced Air Mobility

This paper presents a general purpose, plug-and-play simulation platform for the use of future aviation stakeholders, such as urban airspace planners, air vehicle operators, ground operation managers, air traffic controllers and aviation researchers. The presented simulator platform is envisioned to serve as a toolkit to visualize, evaluate, and configure future advanced air mobility (AAM) operations. Highlighting features of this toolkit include a modular architecture that allows multiple smart unmanned aerial systems (UASs) to remotely connect to the simulation server and participate in decentralized decision-making scenario simulations. As an example of the decentralized decision-making scenario, an inter-agent negotiation-based conflict resolution use case is considered in this paper, where the UASs leverage the on-board/on-the-edge artificial intelligence (AI) capability to continually build situational awareness, and use this information to predict future conflicts and resolve them through machine-to-machine negotiation. As such operations are non-existent at scale currently, the presented simulation platform offers a viable and cost-effective alternative for assessing the efficacy of AAM research outcomes and challenges in future shared airspace usage. The simulation platform allows plug-n-play connectivity with AI and non-AI compute modules representing individual UAS’s flight control. Each module can interact with the simulation platform independently to communicate current and desired future states, situational awareness, and conflict resolution utilization costs for inter-agent negotiation. The simulation environment orchestrates realistic operational scenarios with spatiotemporal details, dynamic events, tactical conflict-resolution methods, interfaces for customizing air traffic control parameters, and information exchange uncertainties. In the future, this can serve as a community focused cloud simulation platform, incorporating multi-stakeholder airspace constraints from regulatory, government, city, and local agencies.

Aditya N Das↗

NASA NTRS2022

A 3D Simulation Platform for Decentralized Decision-Making in Advanced Air Mobility

This paper presents a general purpose, plug-and-play simulation platform for the use of future aviation stakeholders, such as urban airspace planners, air vehicle operators, ground operation managers, air traffic controllers and aviation researchers. The presented simulator platform is envisioned to serve as a toolkit to visualize, evaluate, and configure future advanced air mobility (AAM) operations. Highlighting features of this toolkit include a modular architecture that allows multiple smart unmanned aerial systems (UASs) to remotely connect to the simulation server and participate in decentralized decision-making scenario simulations. As an example of the decentralized decision-making scenario, an inter-agent negotiation-based conflict resolution use case is considered in this paper, where the UASs leverage the on-board/on-the-edge artificial intelligence (AI) capability to continually build situational awareness, and use this information to predict future conflicts and resolve them through machine-to-machine negotiation. As such operations are non-existent at scale currently, the presented simulation platform offers a viable and cost-effective alternative for assessing the efficacy of AAM research outcomes and challenges in future shared airspace usage. The simulation platform allows plug-n-play connectivity with AI and non-AI compute modules representing individual UAS’s flight control. Each module can interact with the simulation platform independently to communicate current and desired future states, situational awareness, and conflict resolution utilization costs for inter-agent negotiation. The simulation environment orchestrates realistic operational scenarios with spatiotemporal details, dynamic events, tactical conflict-resolution methods, interfaces for customizing air traffic control parameters, and information exchange uncertainties. In the future, this can serve as a community focused cloud simulation platform, incorporating multi-stakeholder airspace constraints from regulatory, government, city, and local agencies.

Aditya Das↗

NASA NTRS2003

Characterization of Model-Based Reasoning Strategies for Use in IVHM Architectures

Open architectures are gaining popularity for Integrated Vehicle Health Management (IVHM) applications due to the diversity of subsystem health monitoring strategies in use and the need to integrate a variety of techniques at the system health management level. The basic concept of an open architecture suggests that whatever monitoring or reasoning strategy a subsystem wishes to deploy, the system architecture will support the needs of that subsystem and will be capable of transmitting subsystem health status across subsystem boundaries and up to the system level for system-wide fault identification and diagnosis. There is a need to understand the capabilities of various reasoning engines and how they, coupled with intelligent monitoring techniques, can support fault detection and system level fault management. Researchers in IVHM at NASA Ames Research Center are supporting the development of an IVHM system for liquefying-fuel hybrid rockets. In the initial stage of this project, a few readily available reasoning engines were studied to assess candidate technologies for application in next generation launch systems. Three tools representing the spectrum of model-based reasoning approaches, from a quantitative simulation based approach to a graph-based fault propagation technique, were applied to model the behavior of the Hybrid Combustion Facility testbed at Ames. This paper summarizes the characterization of the modeling process for each of the techniques.

Poll, Scott↗

NASA NTRS1990

Diverter Decision Aiding for In-Flight Diversions

It was determined that artificial intelligence technology can provide pilots with the help they need in making the complex decisions concerning en route changes in a flight plan. A diverter system should have the capability to take all of the available information and produce a recommendation to the pilot. Phase three illustrated that using Joshua to develop rules for an expert system and a Statice database provided additional flexibility by permitting the development of dynamic weighting of diversion relevant parameters. This increases the fidelity of the AI functions cited as useful in aiding the pilot to perform situational assessment, navigation rerouting, flight planning/replanning, and maneuver execution. Additionally, a prototype pilot-vehicle interface (PVI) was designed providing for the integration of both text and graphical based information. Advanced technologies were applied to PVI design, resulting in a hierarchical menu based architecture to increase the efficiency of information transfer while reducing expected workload. Additional efficiency was gained by integrating spatial and text displays into an integrated user interface.

Rudolph, Frederick M.↗

NASA NTRS1994

An Integrated Fault Tolerant Robotic Controller System for High Reliability and Safety

This paper describes the concepts and features of a fault-tolerant intelligent robotic control system being developed for applications that require high dependability (reliability, availability, and safety). The system consists of two major elements: a fault-tolerant controller and an operator workstation. The fault-tolerant controller uses a strategy which allows for detection and recovery of hardware, operating system, and application software failures.The fault-tolerant controller can be used by itself in a wide variety of applications in industry, process control, and communications. The controller in combination with the operator workstation can be applied to robotic applications such as spaceborne extravehicular activities, hazardous materials handling, inspection and maintenance of high value items (e.g., space vehicles, reactor internals, or aircraft), medicine, and other tasks where a robot system failure poses a significant risk to life or property.

robotics fault-tolerant control systems↗

NASA NTRS2009

Telemetry Boards Interpret Rocket, Airplane Engine Data

For all the data gathered by the space shuttle while in orbit, NASA engineers are just as concerned about the information it generates on the ground. From the moment the shuttle s wheels touch the runway to the break of its electrical umbilical cord at 0.4 seconds before its next launch, sensors feed streams of data about the status of the vehicle and its various systems to Kennedy Space Center s shuttle crews. Even while the shuttle orbiter is refitted in Kennedy s orbiter processing facility, engineers constantly monitor everything from power levels to the testing of the mechanical arm in the orbiter s payload bay. On the launch pad and up until liftoff, the Launch Control Center, attached to the large Vehicle Assembly Building, screens all of the shuttle s vital data. (Once the shuttle clears its launch tower, this responsibility shifts to Mission Control at Johnson Space Center, with Kennedy in a backup role.) Ground systems for satellite launches also generate significant amounts of data. At Cape Canaveral Air Force Station, across the Banana River from Kennedy s location on Merritt Island, Florida, NASA rockets carrying precious satellite payloads into space flood the Launch Vehicle Data Center with sensor information on temperature, speed, trajectory, and vibration. The remote measurement and transmission of systems data called telemetry is essential to ensuring the safe and successful launch of the Agency s space missions. When a launch is unsuccessful, as it was for this year s Orbiting Carbon Observatory satellite, telemetry data also provides valuable clues as to what went wrong and how to remedy any problems for future attempts. All of this information is streamed from sensors in the form of binary code: strings of ones and zeros. One small company has partnered with NASA to provide technology that renders raw telemetry data intelligible not only for Agency engineers, but also for those in the private sector.

Source record↗