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All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Overview of Intelligent Systems and Operations Development

To achieve NASA's ambitious mission objectives for the future, aircraft and spacecraft will need intelligence to take the correct action in a variety of circumstances. Vehicle intelligence can be defined as the ability to "do the right thing" when faced with a complex decision-making situation. It will be necessary to implement integrated autonomous operations and low-level adaptive flight control technologies to direct actions that enhance the safety and success of complex missions despite component failures, degraded performance, operator errors, and environment uncertainty. This paper will describe the array of technologies required to meet these complex objectives. This includes the integration of high-level reasoning and autonomous capabilities with multiple subsystem controllers for robust performance. Future intelligent systems will use models of the system, its environment, and other intelligent agents with which it interacts. They will also require planners, reasoning engines, and adaptive controllers that can recommend or execute commands enabling the system to respond intelligently. The presentation will also address the development of highly dependable software, which is a key component to ensure the reliability of intelligent systems.

Pallix, Joan↗
NASA NTRS2002

Monitoring of Structural Integrity of Composite Structures by Embedded Optical Fiber Sensors

Real time monitoring of the mechanical integrity and stresses on key aerospace composite structures like aircraft wings, walls of pressure vessels and fuel tanks or any other structurally extended components and panels as in space telescopes is very important to NASA. Future military and commercial aircraft as well as NASA space systems such as Space Based Radar and International Space Station will incorporate a monitoring system to sense any degradation to the structure. In the extreme flight conditions of an aerospace vehicle it might be desirable to measure the strain every ten centimeters and thus fully map out the strain field of a composite component. A series of missions and vehicle health management requirements call for these measurements. At the moment thousands of people support a few vehicle launches per year. This number can be significantly reduced by implementing intelligent vehicles with integral nervous systems (smart structures). This would require maintenance to be performed only as needed. Military and commercial aircrafts have an equally compelling case. Maintenance yearly costs are currently reaching astronomical heights. Monitoring techniques are therefore required that allow for maintenance to be performed only when needed. This would allow improved safety by insuring that necessary tasks are performed while reducing costs by eliminating procedures that are costly and not needed. The advantages fiber optical sensors have over conventional electro-mechanical systems like strain gauges have been widely extolled in the research literature. These advantages include their small size, low weight, immunity to electrical resistance, corrosion resistance, compatibility with composite materials and process conditions, and multiplexing capabilities. One fiber optic device which is suitable for distributed sensing is the fiber Bragg grating (FBG). Researchers at NASA MSFC are currently developing techniques for using FBGs for monitoring the integrity of advanced structural materials expected to become the mainstay of the current and future generation space structures. Since carbon-epoxy composites are the materials of choice for the current space structures, the initial study is concentrated on this type of composite. The goals of this activity are to use embedded FBG sensors for measuring strain and temperature of composite structures, and to investigate the effects of various parameters such as composite fiber orientation with respect to the optical sensor, unidirectional fiber composite, fabrication process etc., on the optical performance of the sensor. This paper describes an experiment to demonstrate the use of an embedded FBG for measuring strain in a composite material. The performance of the fiber optic sensor is determined by direct comparison with results from more conventional instrumentation.

Albert J. Osei↗
NASA NTRS2003

Investigation of Structural Properties of Carbon-Epoxy Composites Using Embedded Fiber-Optic Bragg Gratings

Real time monitoring of the mechanical integrity and stresses on key aerospace composite structures like aircraft wings, walls of pressure vessels and fuel tanks or any other structurally extended components and panels as in space telescopes is very important to NASA. Future military and commercial aircraft as well as NASA space systems such as Space Based Radar and International Space Station will incorporate a monitoring system to sense any degradation to the structure. In the extreme flight conditions of an aerospace vehicle it might be desirable to measure the strain every ten centimeters and thus fully map out the strain field of a composite component. A series of missions and vehicle health management requirements call for these measurements. At the moment thousands of people support a few vehicle launches per year. This number can be significantly reduced by implementing intelligent vehicles with integral nervous systems (smart structures). This would require maintenance to be performed only as needed. Military and commercial aircrafts have an equally compelling case. Annual maintenance costs are currently reaching astronomical heights. Monitoring techniques are therefore required that allow for maintenance to be performed only when needed. This would allow improved safety by insuring that necessary tasks are performed while reducing costs by eliminating procedures that are costly and not needed. The advantages fiber optical sensors have over conventional electro-mechanical systems like strain gauges have been widely extolled in the research literature. These advantages include their small size, low weight, immunity to electrical resistance, corrosion resistance, compatibility with composite materials and process conditions, and multiplexing capabilities. One fiber optic device which is suitable for distributed sensing is the fiber Bragg grating (FBG). This is a periodic perturbation in the refractive index of the fiber core. When a broadband light is coupled into the optical fiber sensor, a reflection peak will be obtained centered around a wavelength called Bragg-wavelength. The Bragg-wavelength depends on the refractive index and the period of the grating, which both change due to mechanical and thermal strain applied to the sensor. The shift in the Bragg-wavelength is directly proportional to the strain. Researchers at NASA MSFC are currently developing techniques for using FBGs for monitoring the integrity of advanced structural materials expected to become the mainstay of the current and future generation space structures. Since carbon-epoxy composites are the materials of choice for the current space structures, the initial study is concentrated on this type of composite. The goals of this activity are to use embedded FBG sensors for measuring strain and temperature of composite structures, and to investigate the effects of various parameters such as composite fiber orientation with respect to the optical sensor, unidirectional fiber composite, fabrication process etc., on the optical performance of the sensor. This paper describes an experiment to demonstrate the use of an embedded FBG for measuring strain in a composite material. The performance of the fiber optic sensor is determined by direct comparison with results from more conventional instrumentation.

Osei, Albert J.↗
DOE OSTI2023

Adaptive Path-Following Control for Ground Vehicles Using a Switching Non-Quadratic Lyapunov Function

The application of adaptive control techniques in the development of control systems for intelligent vehicles, especially for ground vehicle path-following controllers, has gained popularity due to their ability to handle large-scale parametric uncertainties. However, the use of a standard quadratic Lyapunov function in existing adaptive control-based path-following controllers can lead to poor transient performance, such as slow convergence and/or large overshoot. To address this limitation, this study proposes the use of a switching non-quadratic Lyapunov function to design a model reference adaptive path-following controller that aims to provide superior transient performance. The stability and signal convergence of the closed-loop system are demonstrated through a Lyapunov-like analysis. Through dSPACE ASM simulation, the effectiveness of the proposed controller is illustrated, which confirms improved tracking performance over a baseline solution.

Zhou, Xingyu↗
NASA NTRS1994

An update on Lab Rover: A hospital material transporter

The development of a hospital material transporter, 'Lab Rover', is described. Conventional material transport now utilizes people power, push carts, pneumatic tubes and tracked vehicles. Hospitals are faced with enormous pressure to reduce operating costs. Cyberotics, Inc. developed an Autonomous Intelligent Vehicle (AIV). This battery operated service robot was designed specifically for health care institutions. Applications for the AIV include distribution of clinical lab samples, pharmacy drugs, administrative records, x-ray distribution, meal tray delivery, and certain emergency room applications. The first AIV was installed at Lahey Clinic in Burlington, Mass. Lab Rover was beta tested for one year and has been 'on line' for an additional 2 years.

Mattaboni, Paul↗
NASA NTRS1987

Robotic air vehicle. Blending artificial intelligence with conventional software

The Robotic Air Vehicle (RAV) system is described. The program's objectives were to design, implement, and demonstrate cooperating expert systems for piloting robotic air vehicles. The development of this system merges conventional programming used in passive navigation with Artificial Intelligence techniques such as voice recognition, spatial reasoning, and expert systems. The individual components of the RAV system are discussed as well as their interactions with each other and how they operate as a system.

Mcnulty, Christa↗
NASA NTRS2003

The H-Metaphor as a Guideline for Vehicle Automation and Interaction

Good design is not free of form. It does not necessarily happen through a mere sampling of technologies packaged together, through pure analysis, or just by following procedures. Good design begins with inspiration and a vision, a mental image of the end product, which can sometimes be described with a design metaphor. A successful example from the 20th century is the desktop metaphor, which took a real desktop as an orientation for the manipulation of electronic documents on a computer. Initially defined by Xerox, then refined by Apple and others, it could be found on almost every computer by the turn of the 20th century. This paper sketches a specific metaphor for the emerging field of highly automated vehicles, their interactions with human users and with other vehicles. In the introduction, general questions on vehicle automation are raised and related to the physical control of conventional vehicles and to the automation of some late 20th century vehicles. After some words on design metaphors, the H-Metaphor is introduced. More details of the metaphor's source are described and their application to human-machine interaction, automation and management of intelligent vehicles sketched. Finally, risks and opportunities to apply the metaphor to technical applications are discussed.

Flemisch, Frank O.↗
NASA NTRS1987

SOLON: An autonomous vehicle mission planner

The State-Operator Logic Machine (SOLON) Planner provides an architecture for effective real-time planning and replanning for an autonomous vehicle. The highlights of the system, which distinguish it from other AI-based planners that have been designed previously, are its hybrid application of state-driven control architecture and the use of both schematic representations and logic programming for the management of its knowledge base. SOLON is designed to provide multiple levels of planning for a single autonomous vehicle which is supplied with a skeletal, partially-specified mission plan at the outset of the vehicle's operations. This mission plan consists of a set of objectives, each of which will be decomposable by the planner into tasks. These tasks are themselves comparatively complex sets of actions which are executable by a conventional real-time control system which does not perform planning but which is capable of making adjustments or modifications to the provided tasks according to constraints and tolerances provided by the Planner. The current implementation of the SOLON is in the form of a real-time simulation of the Planner module of an Intelligent Vehicle Controller (IVC) on-board an autonomous underwater vehicle (AUV). The simulation is embedded within a larger simulator environment known as ICDS (Intelligent Controller Development System) operating on a Symbolics 3645/75 computer.

Dudziak, M. J.↗
NASA NTRS2008

Development and Evaluation of Sensor Concepts for Ageless Aerospace Vehicles: Report 4 - Phase 1 Implementation of the Concept Demonstrator

This report describes the first phase of the implementation of the Concept Demonstrator. The Concept Demonstrator system is a powerful and flexible experimental test-bed platform for developing sensors, communications systems, and multi-agent based algorithms for an intelligent vehicle health monitoring system for deployment in aerospace vehicles. The Concept Demonstrator contains sensors and processing hardware distributed throughout the structure, and uses multi-agent algorithms to characterize impacts and determine an appropriate response to these impacts.

Abbott, David↗
DOE OSTI2022

Development and Demonstration of a Fuel-Efficient, Class 8 Tractor & Trailer Engine System (SuperTruck II)

Navistar presents the SuperTruck II (ST II) Final Report to the Unites States Department of Energy (US DOE), which covers the five Budget Periods (BPs) from 10-1-2016 through 6-30-2022. For ST II, Navistar built on the achievements of the SuperTruck I (ST I) Program as a catalyst to continue critical research, design and development, testing, and operations to reach the ambitious goals of the ST II project. This approach allowed Navistar to continue contributing to the essential needs of our nation for safe, efficient, and cost-effective delivery of goods and services, as we reduced negative environmental effects and improved operational productivity. This document contains information specified in DOE F 4600.2, Final Scientific/Technical Report DOE F 241.3, B. SCIENTIFIC/TECHNICAL REPORTS, explaining how we met and exceeded program requirements. Throughout this Final Report, Navistar extracted information from documents prepared during the project that represent our management, design and development, building, and testing efforts to meet and exceed SuperTruck II project goals. Navistar followed Plan requirements to achieve / exceed Project Objectives: a) >100% improvement in vehicle freight efficiency (FE) (on ton-MPG basis) relative to 2009 baseline with stretch goal of 140% improvement [actual: 170%); b) >55% engine brake thermal efficiency (BTE) demonstrated in operational engine at a 65-mph cruise point on a dynamometer – ≥31% increase from 2009 baseline [actual: 55.20% of combined BTE) ; and c) development and implementation of commercially cost effective technologies (in terms of a simple payback). Technology selection / development path focused on developing technologies applicable for production within 3-year approach, while ensuring technology readiness and cost of ownership for end users. The Program was organized into five budget periods: Requirements / Technology Assessment and Initial Hardware Testing; Technology Development and Concept Readiness Demonstration; Technology Finalization and Validation Tractor / Trailer Fabrication, Integration and Commissioning Demonstration; and Fuel Economy (FE) and Brake Thermal Efficiency (BTE) and Program Completion. Leadership was provided by DOE, with tasks performed by laboratories (Argonne National Laboratory, Lawrence Livermore National Laboratory); partners at Bosch, TPI, Dana, and J.B. Hunt; , and support from University of Michigan and Clemson University. Navistar lead this team with Principal Investigator / Contracting Officer; Project Manager (PM); Vehicle, Engine, and Aftertreatment Engineers; Finance Manager, Technical Program Leads, and Legal/IP; and other key personnel. Work also included personnel in risk management; funding / budget / finance. Work involved analysis, development, testing, and down selection of individual/system engine, aftertreatment, and vehicle technologies, with integration of selected technologies into a prototype vehicle for demonstration of fuel-efficiency gain. Work also included component/integrated system level development of truck and trailer aerodynamics, base engine efficiency, advanced aftertreatment, combustion efficiency, waste heat recovery, hybrid powertrain, reduced rolling resistance, weight reduction, idle reduction, and driver feedback. As ST II progressed, Navistar performed computer-based modeling / simulations of technologies focused on the primary operational areas: Engine, Aftertreatment, and Vehicle. During the ST II Program, the COVID Virus outbreak unexpectedly challenged by the effects of, which affected staffing, scheduling, design, supplies, availability of materials, production procedures, and testing. The DOE responded by extending the program by three quarters to ensure that project tasks were completed for this vital project. Focus continued on analyzing, developing, testing, and down selecting individual-/system-level engine and vehicle technologies for integration of the final selected technologies into a prototype vehicle that would demonstrate fuel-efficiency gains made possible through these technologies. This included component/integrated system-level development of truck and trailer aerodynamics, base engine efficiency, advanced aftertreatment, combustion efficiency, waste heat recovery, solar power, distributed and intelligent vehicle power, hybrid powertrain, reduced rolling resistance, weight reduction, idle reduction, and driver feedback. Throughout the program, function, reliability, and performance at all levels were ensured through testing. Proof of this approach was demonstrated in multiple, on-road demonstrations: Scenario A (Flatland) Fuel Economy, Scenario B (Hilly) Fuel Economy, and City Cycle Tests. Other benefits derived from ST II included new/improved products, publications, patents, and next-step capabilities related to electric/hydrogen vehicles and autonomous driving.

Zukouski, Russ↗
DOE OSTI2022

Driver Distraction Behavior Detection using a Vision Transformer Model based on Transfer Learning Strategy

Driver distraction behavior is one of the critical factors in traffic accidents. Thus, advanced driver state detection system has become the focus in the field of intelligent vehicle. However, in practical applications, insufficient samples of driving distraction behaviors bring great challenges to training a personalized behavior distraction detection model for a specific driver. To this end, a novel transformer model based on a transfer learning strategy is proposed in this paper to accurately recognize driver distraction behavior. Inspired by the effect of the transformer network in visual recognition, we firstly present a transformer behavior distraction detection system to identify the behavior categories that cause driver distraction. Then, for the specific driving dataset in practical application scenarios, the transfer learning strategy is introduced into the driver distraction detection model to further train the general transformer network. The effectiveness of the transformer based on the transfer learning strategy is validated compared with other traditional deep learning methods. The results show that the proposed detection method has better generalization ability and higher accuracy.

Fang, Zhenwu↗
DOE OSTI2020

A Minimum Principle-Based Algorithm for Energy-Efficient Eco-Driving of Electric Vehicles in Various Traffic and Road Conditions

Our report presents an optimization algorithm for energy-efficient driving of electric vehicles. The algorithm is based on Pontryagin’s Minimum Principle by considering the driving mission as an optimal control problem. On a planned route, diverse application cases may occur due to various physical traits such as powertrain (e.g., switching between motor and generator operation), environmental variations (e.g., terrain grade change), traffic laws (e.g., speed limits), and safety concerns (e.g., headway to the leading vehicle). The proposed algorithm handles these real-world challenges by considering the perturbation to the system dynamics and the constraints to the control and state variables.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1979

Space Shuttle Solid Rocket Booster decelerator subsystem - Air drop test vehicle/B-52 design

The air drop development test program for the Space Shuttle Solid Rocket Booster Recovery System required the design of a large drop test vehicle that would meet all the stringent requirements placed on it by structural loads, safety considerations, flight recovery system interfaces, and sequence. The drop test vehicle had to have the capability to test the drogue and the three main parachutes both separately and in the total flight deployment sequence and still be low-cost to fit in a low-budget development program. The design to test large ribbon parachutes to loads of 300,000 pounds required the detailed investigation and integration of several parameters such as carrier aircraft mechanical interface, drop test vehicle ground transportability, impact point ground penetration, salvageability, drop test vehicle intelligence, flight design hardware interfaces, and packaging fidelity.

Runkle, R. E.↗
NASA NTRS2007

Array Phase Shifters: Theory and Technology

While there are a myriad of applications for microwave phase shifters in instrumentation and metrology, power combining, amplifier linearization, and so on, the most prevalent use is in scanning phased-array antennas. And while this market continues to be dominated by military radar and tracking platforms, many commercial applications have emerged in the past decade or so. These new and potential applications span low-Earth-orbit (LEO) communications satellite constellations and collision warning radar, an aspect of the Intelligent Vehicle Highway System or Automated Highway System. In any case, the phase shifters represent a considerable portion of the overall antenna cost, with some estimates approaching 40 percent for receive arrays. Ferrite phase shifters continue to be the workhorse in military-phased arrays, and while there have been advances in thin film ferrite devices, the review of this device technology in the previous edition of this book is still highly relevant. This chapter will focus on three types of phase shifters that have matured in the past decade: GaAs MESFET monolithic microwave integrated circuit (MMIC), micro-electromechanical systems (MEMS), and thin film ferroelectric-based devices. A brief review of some novel devices including thin film ferrite phase shifters and superconducting switches for phase shifter applications will be provided. Finally, the effects of modulo 2 phase shift limitations, phase errors, and transient response on bit error rate degradation will be considered.

Romanofsky, Robert R.↗
NASA NTRS2010

Micro/Nanoscale Chemicalsensor Systems for Aerospace Applications

The aerospace industry requires development of a range of chemical-sensor technologies for applications including emissions monitoring as well as fuel-leak and fire detection. Improvements in sensing technology are necessary to increase safety, reduce emissions, and increase performance. The overall aim is to develop intelligent-vehicle systems that can autonomously monitor their state and respond to environmental changes. A range of chemical sensors is under development to meet these needs, based in part on microfabrication technology which produces sensors of minimal size, weight, and power consumption. We have fabricated a range of sensor platforms, integrated them with hardware to form complete sensor systems, and demonstrated their applicability.

Hunter, Gary↗
NASA NTRS2016

Towards Autonomous Aviation Operations: What Can We Learn from Other Areas of Automation?

Rapid advances in automation has disrupted and transformed several industries in the past 25 years. Automation has evolved from regulation and control of simple systems like controlling the temperature in a room to the autonomous control of complex systems involving network of systems. The reason for automation varies from industry to industry depending on the complexity and benefits resulting from increased levels of automation. Automation may be needed to either reduce costs or deal with hazardous environment or make real-time decisions without the availability of humans. Space autonomy, Internet, robotic vehicles, intelligent systems, wireless networks and power systems provide successful examples of various levels of automation. NASA is conducting research in autonomy and developing plans to increase the levels of automation in aviation operations. This paper provides a brief review of levels of automation, previous efforts to increase levels of automation in aviation operations and current level of automation in the various tasks involved in aviation operations. It develops a methodology to assess the research and development in modeling, sensing and actuation needed to advance the level of automation and the benefits associated with higher levels of automation. Section II describes provides an overview of automation and previous attempts at automation in aviation. Section III provides the role of automation and lessons learned in Space Autonomy. Section IV describes the success of automation in Intelligent Transportation Systems. Section V provides a comparison between the development of automation in other areas and the needs of aviation. Section VI provides an approach to achieve increased automation in aviation operations based on the progress in other areas. The final paper will provide a detailed analysis of the benefits of increased automation for the Traffic Flow Management (TFM) function in aviation operations.

Sridhar, Banavar↗
NASA NTRS1994

Predictability in space launch vehicle anomaly detection using intelligent neuro-fuzzy systems

Included in this viewgraph presentation on intelligent neuroprocessors for launch vehicle health management systems (HMS) are the following: where the flight failures have been in launch vehicles; cumulative delay time; breakdown of operations hours; failure of Mars Probe; vehicle health management (VHM) cost optimizing curve; target HMS-STS auxiliary power unit location; APU monitoring and diagnosis; and integration of neural networks and fuzzy logic.

Gulati, Sandeep↗
NASA NTRS2003

Application of Artificial Intelligence Techniques in Unmanned Aerial Vehicle Flight

This paper describes the development of an application of Artificial Intelligence for Unmanned Aerial Vehicle (UAV) control. The project was done as part of the requirements for a class in Artificial Intelligence (AI) at Nova southeastern University and as an adjunct to a project at NASA Goddard Space Flight Center's Wallops Flight Facility for a resilient, robust, and intelligent UAV flight control system. A method is outlined which allows a base level application for applying an AI method, Fuzzy Logic, to aspects of Control Logic for UAV flight. One element of UAV flight, automated altitude hold, has been implemented and preliminary results displayed. A low cost approach was taken using freeware, gnu, software, and demo programs. The focus of this research has been to outline some of the AI techniques used for UAV flight control and discuss some of the tools used to apply AI techniques. The intent is to succeed with the implementation of applying AI techniques to actually control different aspects of the flight of an UAV.

Bauer, Frank H.↗