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NASA NTRS2019

Standardizing Microprocessor and GPU Radiation Test Approaches

Microprocessor, Graphics Processing Units (GPUs) and DDRx memory devices have emerged as promising next-generation technologies that enables both high performance processing and acceleration of complex algorithms for the latest challenges in human spaceflight, autonomous vehicles and artificial intelligence (AI). The feature sets of these devices offer exponential increases to throughput, calculation capability and system autonomy when compared to legacy flight systems. NASA's Electronic Part and Packaging (NEPP) Program has conducted an investigation into the radiation susceptibility of leading edge devices and process technologies by establishing standardized test approaches. Unlike most discrete devices, these require state of the art test systems to induce specific hardware activity similar to application software, thus allowing the characterization of failure modes within the system. To best characterize the tested part, NEPP eliminates variables that may impact device performance under radiation. Simplification of remaining system-level variables leads to an improved understanding of complex computational devices and their intended applications. The failure modes and error signatures that are recorded during testing are used to determine radiation sensitivity of the semiconductor process and the microcode architecture of the design. This presentation will discuss the test methodology that NASA Electronic Parts and Packaging (NEPP) is working to establish for its microprocessor, GPU and DDRx memory test programs to provide guidance on these devices and their underlying technology, in regards to their potential usage in future space flight systems.

GPU↗
NASA NTRS2019

SAFE50 Reference Design Study for Large-Scale High-Density Low-Altitude UAS Operations in Urban Areas

Enabling safe, routine, and high-density flight operations of small UAS at low-altitude over heavily populated urban centers presents a difficult challenge for emerging UAS Traffic Management (UTM) system concepts. Urban operations by definition involve flight over people, property, and infrastructure. Low-altitude urban environments - such as urban canyons – are one of the most difficult areas for UTM to consider. Mission concepts require routine operations in a cluttered radio-frequency (RF) environment with degraded or denied Global Positioning System (GPS) reception. Flights with any appreciable distance will be beyond visual and communications line-of-sight from ground operators. Timely detection and response to emergencies and onboard failures, which is critical for safe aircraft operation, will be difficult. This work seeks to establish a feasible reference autonomy architecture for autonomous vehicles in an urban UTM system, then verifying and validating this architecture within a complete UTM concept point-design and systems analysis study. In this paper, we present the results from the NASA SAFE50 conceptual design and systems study that investigates the trade-space of urban UTM operations. This advanced conceptual design study develops a feasible, verified, validated point-design solution. The SAFE50 point-design concept places emphasis on advanced, highly-autonomous, and highly-capable vehicles that favors intelligent onboard autonomy over direct human control with today's technologies and operating in today's urban environments. This paper focuses on an general overview of the design study, highlighting decisions made in the architectural solution. This paper will presents a summary of the study, architectures, and requirements. We present an overview of the architecture designs as derived from the top-level UTM system. The point-design has been implemented in both simulation and through flight testing of hardware design prototypes. The results from simulation and flight testing as part of the verification and validation process of the reference design study.

Ippolito, Corey A.↗
NASA NTRS2006

System Analysis Applied to Autonomy: Application to Human-Rated Lunar/Mars Landers

System analysis is an essential technical discipline for the modern design of spacecraft and their associated missions. Specifically, system analysis is a powerful aid in identifying and prioritizing the required technologies needed for mission and/or vehicle development efforts. Maturation of intelligent systems technologies, and their incorporation into spacecraft systems, are dictating the development of new analysis tools, and incorporation of such tools into existing system analysis methodologies, in order to fully capture the trade-offs of autonomy on vehicle and mission success. A "system analysis of autonomy" methodology will be outlined and applied to a set of notional human-rated lunar/Mars lander missions toward answering these questions: 1. what is the optimum level of vehicle autonomy and intelligence required? and 2. what are the specific attributes of an autonomous system implementation essential for a given surface lander mission/application in order to maximize mission success? Future human-rated lunar/Mars landers, though nominally under the control of their crew, will, nonetheless, be highly automated systems. These automated systems will range from mission/flight control functions, to vehicle health monitoring and prognostication, to life-support and other "housekeeping" functions. The optimum degree of autonomy afforded to these spacecraft systems/functions has profound implications from an exploration system architecture standpoint.

Young, Larry A.↗
NASA NTRS1992

Microrover Operates With Minimal Computation

Small, light, highly mobile robotic vehicles called "microrovers" use sensors and artificial intelligence to perform complicated tasks autonomously. Vehicle navigates, avoids obstacles, and picks up objects using reactive control scheme selected from among few preprogrammed behaviors to respond to environment while executing assigned task. Under development for exploration and mining of other planets. Also useful in firefighting, cleaning up chemical spills, and delivering materials in factories. Reactive control scheme and principle of behavior-description language useful in reducing computational loads in prosthetic limbs and automotive collision-avoidance systems.

Miller, David P.↗
NASA NTRSDate not supplied

AEGIS: Autonomous Entity Global Intelligence System for Urban Air Mobility

This paper presents a global intelligence system that synthesizes aerial vehicles’ real-time physical data, planned actions, and historical behavior into engineered data frames representing the collective state of the airspace and suitable for efficient machine learning consumption. These data frames are then learnt by a deep neural net to build a prediction model that estimates the expected evolution path of the current state, thereby identifying potential future conflicts. This approach lends itself to an automated early warning system that the aerial vehicles can implement onboard with a suitable edge computing module more efficiently and effectively than non-AI methods, and eventually take preventive or corrective measures towards self/collaborative resolution of the issues. Contrary to a centralized early warning system where all vehicles’ task-space eventually converges to a global optimum state, the presented distributed global intelligence system brings in a balance between local utility functions of each vehicle and the global operating framework. This contributes to effectively handle the potential massive scaling in urban air mobility in the near future.

Artificial Intelligence↗
NASA NTRS2006

Experiences applying Formal Approaches in the Development of Swarm-Based Space Exploration Systems

NASA is researching advanced technologies for future exploration missions using intelligent swarms of robotic vehicles. One of these missions is the Autonomous Nan0 Technology Swarm (ANTS) mission that will explore the asteroid belt using 1,000 cooperative autonomous spacecraft. The emergent properties of intelligent swarms make it a potentially powerful concept, but at the same time more difficult to design and ensure that the proper behaviors will emerge. NASA is investigating formal methods and techniques for verification of such missions. The advantage of using formal methods is the ability to mathematically verify the behavior of a swarm, emergent or otherwise. Using the ANTS mission as a case study, we have evaluated multiple formal methods to determine their effectiveness in modeling and ensuring desired swarm behavior. This paper discusses the results of this evaluation and proposes an integrated formal method for ensuring correct behavior of future NASA intelligent swarms.

Rouff, Christopher A.↗
NASA NTRS2006

AERCam Autonomy: Intelligent Software Architecture for Robotic Free Flying Nanosatellite Inspection Vehicles

The NASA Johnson Space Center has developed a nanosatellite-class Free Flyer intended for future external inspection and remote viewing of human spacecraft. The Miniature Autonomous Extravehicular Robotic Camera (Mini AERCam) technology demonstration unit has been integrated into the approximate form and function of a flight system. The spherical Mini AERCam Free Flyer is 7.5 inches in diameter and weighs approximately 10 pounds, yet it incorporates significant additional capabilities compared to the 35-pound, 14-inch diameter AERCam Sprint that flew as a Shuttle flight experiment in 1997. Mini AERCam hosts a full suite of miniaturized avionics, instrumentation, communications, navigation, power, propulsion, and imaging subsystems, including digital video cameras and a high resolution still image camera. The vehicle is designed for either remotely piloted operations or supervised autonomous operations, including automatic stationkeeping, point-to-point maneuvering, and waypoint tracking. The Mini AERCam Free Flyer is accompanied by a sophisticated control station for command and control, as well as a docking system for automated deployment, docking, and recharge at a parent spacecraft. Free Flyer functional testing has been conducted successfully on both an airbearing table and in a six-degree-of-freedom closed-loop orbital simulation with avionics hardware in the loop. Mini AERCam aims to provide beneficial on-orbit views that cannot be obtained from fixed cameras, cameras on robotic manipulators, or cameras carried by crewmembers during extravehicular activities (EVA s). On Shuttle or International Space Station (ISS), for example, Mini AERCam could support external robotic operations by supplying orthogonal views to the intravehicular activity (IVA) robotic operator, supply views of EVA operations to IVA and/or ground crews monitoring the EVA, and carry out independent visual inspections of areas of interest around the spacecraft. To enable these future benefits with minimal impact on IVA operators and ground controllers, the Mini AERCam system architecture incorporates intelligent systems attributes that support various autonomous capabilities. 1) A robust command sequencer enables task-level command scripting. Command scripting is employed for operations such as automatic inspection scans over a region of interest, and operator-hands-off automated docking. 2) A system manager built on the same expert-system software as the command sequencer provides detection and smart-response capability for potential system-level anomalies, like loss of communications between the Free Flyer and control station. 3) An AERCam dynamics manager provides nominal and off-nominal management of guidance, navigation, and control (GN&C) functions. It is employed for safe trajectory monitoring, contingency maneuvering, and related roles. This paper will describe these architectural components of Mini AERCam autonomy, as well as the interaction of these elements with a human operator during supervised autonomous control.

Fredrickson, Steven E.↗
NASA NTRS1994

Ground vehicle control at NIST: From teleoperation to autonomy

NIST is applying their Real-time Control System (RCS) methodology for control of ground vehicles for both the U.S. Army Researh Lab, as part of the DOD's Unmanned Ground Vehicles program, and for the Department of Transportation's Intelligent Vehicle/Highway Systems (IVHS) program. The actuated vehicle, a military HMMWV, has motors for steering, brake, throttle, etc. and sensors for the dashboard gauges. For military operations, the vehicle has two modes of operation: a teleoperation mode--where an operator remotely controls the vehicle over an RF communications network; and a semi-autonomous mode called retro-traverse--where the control system uses an inertial navigation system to steer the vehicle along a prerecorded path. For the IVHS work, intelligent vision processing elements replace the human teleoperator to achieve autonomous, visually guided road following.

Murphy, Karl N.↗
NASA NTRS2010

Stereoscopic Machine-Vision System Using Projected Circles

A machine-vision system capable of detecting obstacles large enough to damage or trap a robotic vehicle is undergoing development. The system includes (1) a pattern generator that projects concentric circles of laser light forward onto the terrain, (2) a stereoscopic pair of cameras that are aimed forward to acquire images of the circles, (3) a frame grabber and digitizer for acquiring image data from the cameras, and (4) a single-board computer that processes the data. The system is being developed as a prototype of machine- vision systems to enable robotic vehicles ( rovers ) on remote planets to avoid craters, large rocks, and other terrain features that could capture or damage the vehicles. Potential terrestrial applications of systems like this one could include terrain mapping, collision avoidance, navigation of robotic vehicles, mining, and robotic rescue. This system is based partly on the same principles as those of a prior stereoscopic machine-vision system in which the cameras acquire images of a single stripe of laser light that is swept forward across the terrain. However, this system is designed to afford improvements over some of the undesirable features of the prior system, including the need for a pan-and-tilt mechanism to aim the laser to generate the swept stripe, ambiguities in interpretation of the single-stripe image, the time needed to sweep the stripe across the terrain and process the data from many images acquired during that time, and difficulty of calibration because of the narrowness of the stripe. In this system, the pattern generator does not contain any moving parts and need not be mounted on a pan-and-tilt mechanism: the pattern of concentric circles is projected steadily in the forward direction. The system calibrates itself by use of data acquired during projection of the concentric-circle pattern onto a known target representing flat ground. The calibration- target image data are stored in the computer memory for use as a template in processing terrain images. During operation on terrain, the images acquired by the left and right cameras are analyzed. The analysis includes (1) computation of the horizontal and vertical dimensions and the aspect ratios of rectangles that bound the circle images and (2) comparison of these aspect ratios with those of the template. Coordinates of distortions of the circles are used to identify and locate objects. If the analysis leads to identification of an object of significant size, then stereoscopicvision algorithms are used to estimate the distance to the object. The time taken in performing this analysis on a single pair of images acquired by the left and right cameras in this system is a fraction of the time taken in processing the many pairs of images acquired in a sweep of the laser stripe across the field of view in the prior system. The results of the analysis include data on sizes and shapes of, and distances and directions to, objects. Coordinates of objects are updated as the vehicle moves so that intelligent decisions regarding speed and direction can be made. The results of the analysis are utilized in a computational decision-making process that generates obstacle-avoidance data and feeds those data to the control system of the robotic vehicle.

Mackey, Jeffrey R.↗
NASA NTRS1991

EXODUS: Integrating intelligent systems for launch operations support

Kennedy Space Center (KSC) is developing knowledge-based systems to automate critical operations functions for the space shuttle fleet. Intelligent systems will monitor vehicle and ground support subsystems for anomalies, assist in isolating and managing faults, and plan and schedule shuttle operations activities. These applications are being developed independently of one another, using different representation schemes, reasoning and control models, and hardware platforms. KSC has recently initiated the EXODUS project to integrate these stand alone applications into a unified, coordinated intelligent operations support system. EXODUS will be constructed using SOCIAL, a tool for developing distributed intelligent systems. EXODUS, SOCIAL, and initial prototyping efforts using SOCIAL to integrate and coordinate selected EXODUS applications are described.

Adler, Richard M.↗
NASA NTRS2007

Circuit and Method for Communication Over DC Power Line

A circuit and method for transmitting and receiving on-off-keyed (OOK) signals with fractional signal-to-noise ratios uses available high-temperature silicon- on-insulator (SOI) components to move computational, sensing, and actuation abilities closer to high-temperature or high-ionizing radiation environments such as vehicle engine compartments, deep-hole drilling environments, industrial control and monitoring of processes like smelting, and operations near nuclear reactors and in space. This device allows for the networking of multiple, like nodes to each other and to a central processor. It can do this with nothing more than the already in-situ power wiring of the system. The device s microprocessor allows it to make intelligent decisions within the vehicle operational loop and to effect control outputs to its associated actuators. The figure illustrates how each node converts digital serial data to OOK 18-kHz in transmit mode and vice-versa in receive mode; though operations at lower frequencies or up to a megahertz are within reason using this method and these parts. This innovation s technique modulates a DC power bus with millivolt-level signals through a MOSFET (metal oxide semiconductor field effect transistor) and resistor by OOK. It receives and demodulates this signal from the DC power bus through capacitive coupling at high temperature and in high ionizing radiation environments. The demodulation of the OOK signal is accomplished by using an asynchronous quadrature detection technique realized by a quasi-discrete Fourier transform through use of the quadrature components (0 and 90 phases) of the carrier frequency as generated by the microcontroller and as a function of the selected crystal frequency driving its oscillator. The detected signal is rectified using an absolute-value circuit containing no diodes (diodes being non-operational at high temperatures), and only operational amplifiers. The absolute values of the two phases of the received signal are then summed and hard limited (digitized) by comparing them to a reference level and are then input into a microprocessor as a serial bit stream. The quasi-discrete Fourier transform is performed in high-temperature components (operational amplifiers, analog switches, resistors, and capacitors). The demodulated signal is a serial data stream that is input to the UART (universal asynchronous receiver transmitter) receiver pin of the microprocessor. The OOK of the carrier frequency uses the output of the UART pin as an enabling signal that drives the gate of the MOSFET. Logic low bits enable the carrier frequency (realized by using the 0 phase signal from the microcontroller, though either phase may be used) to be DC-coupled to the power supply bus through a current-limiting resistor mounted between the MOSFET drain and the supply rail. The presence of logic lows on the power supply rail is realized by carrier bursts while logic highs are realized by the absence of bursts.

Krasowski, Michael J.↗
NASA NTRS2014

Full Scale Advanced Systems Testbed (FAST): Capabilities and Recent Flight Research

At the NASA Armstrong Flight Research Center research is being conducted into flight control technologies that will enable the next generation of air and space vehicles. The Full Scale Advanced Systems Testbed (FAST) aircraft provides a laboratory for flight exploration of these technologies. In recent years novel but simple adaptive architectures for aircraft and rockets have been researched along with control technologies for improving aircraft fuel efficiency and control structural interaction. This presentation outlines the FAST capabilities and provides a snapshot of the research accomplishments to date. Flight experimentation allows a researcher to substantiate or invalidate their assumptions and intuition about a new technology or innovative approach Data early in a development cycle is invaluable for determining which technology barriers are real and which ones are imagined Data for a technology at a low TRL can be used to steer and focus the exploration and fuel rapid advances based on real world lessons learned It is important to identify technologies that are mature enough to benefit from flight research data and not be tempted to wait until we have solved all the potential issues prior to getting some data Sometimes a stagnated technology just needs a little real world data to get it going One trick to getting data for low TRL technologies is finding an environment where it is okay to take risks, where occasional failure is an expected outcome Learning how things fail is often as valuable as showing that they work FAST has been architected to facilitate this type of testing for control system technologies, specifically novel algorithms and sensors Rapid prototyping with a quick turnaround in a fly-fix-fly paradigm Sometimes it's easier and cheaper to just go fly it than to analyze the problem to death The goal is to find and test control technologies that would benefit from flight data and find solutions to the real barriers to innovation. The FAST vehicle is a flexible laboratory for nascent technologies that would benefit from early life cycle flight research data It provides a robust and safe environment where innovative techniques can be explored in a fly-fix-fly rapid prototyping paradigm IRAC Simple adaptive control technologies can provide real benefits without undo complexity Adverse pilot/adaptive system interactions can be mitigated and tools have been developed to evaluate those interactions ICP Substantial fuel savings can be achieved over a broad range of vehicles and configurations with intelligent control solutions LVAC The AAC design is robust and effective for the SLS mission, and promises to provide benefits to other platforms as well OCLA Hopefully will show that structural feedback can be seamlessly integrated with performance and stability objectives All of these control technologies have been implemented into the same baseline control law and could be combined into one control solution that answers many pressing questions for modern vehicle configurations

flight controls↗
NASA NTRS2001

The Advanced Technology Development Center (ATDC)

NASA is building the Advanced Technology Development Center (ATDC) to provide a 'national resource' for the research, development, demonstration, testing, and qualification of Spaceport and Range Technologies. The ATDC will be located at Space Launch Complex 20 (SLC-20) at Cape Canaveral Air Force Station (CCAFS) in Florida. SLC-20 currently provides a processing and launch capability for small-scale rockets; this capability will be augmented with additional ATDC facilities to provide a comprehensive and integrated in situ environment. Examples of Spaceport Technologies that will be supported by ATDC infrastructure include densified cryogenic systems, intelligent automated umbilicals, integrated vehicle health management systems, next-generation safety systems, and advanced range systems. The ATDC can be thought of as a prototype spaceport where industry, government, and academia, in partnership, can work together to improve safety of future space initiatives. The ATDC is being deployed in five separate phases. Major ATDC facilities will include a Liquid Oxygen Area; a Liquid Hydrogen Area, a Liquid Nitrogen Area, and a multipurpose Launch Mount; 'Iron Rocket' Test Demonstrator; a Processing Facility with a Checkout and Control System; and Future Infrastructure Developments. Initial ATDC development will be completed in 2006.

Clements, G. R.↗
NASA NTRS2019

Autonomy @ NASA for Aeronautics

NASA has been exploring the use of artificial intelligence technologies to improve vehicle and airspace capabilities for over 20 years. This research has ranged from the use of neural nets to allow adaptive control, to autonomous rotorcraft operations, to real-time prognostics, to an eventual goal of autonomous vehicles operating in an autonomous airspace, in the context of smart communities. Examples of past research and future directions in autonomy for aeronautics will be presented.

Van Dalsem, William R.↗
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 NTRS2023

Ch. 12. A Theoretical Approach to Management of Limited Attentional Resources to Support the m:N Operation in Advanced Air Mobility Ecosystem

Advanced air mobility (AAM) technologies incorporate increasingly autonomous systems that allow fully remote, independent, and intelligent operation of air vehicles to support the transportation of goods and passengers within and across urban and rural areas. With a myriad of automated technologies enabling the AAM ecosystem, the human operator’s role will likely be a passive supervisory monitor of the air vehicles, involving increasingly fewer humans (m) that manage many more autonomous systems (N), or m:N operations. Unfortunately, the human performance literature suggests that human operators will exhibit poor supervision of numerous autonomous agents due to the limits of attentional resources in the operators. In the general human information-processing model, a human operator exercises a limited pool of attentional resources to engage various information-processing stages including detecting, perceiving, comprehending, and predicting objects around them. Yamani and Horrey (2018) expanded the human information-processing model to characterize a tradeoff between information-processing demand and resource relief that automation brings in the context of automated driving. In their model, a driver interacting with an automated driving system is assumed to reallocate resources “freed” by automation to support other information-processing stages required for successful task performance. A future AAM ecosystem enabled by an orchestration of advanced automated systems, however, requires a single operator to interact with more than one air vehicle with varying levels and degrees of automated systems, making the traditional framework of human-automation interaction insufficient. To address this gap, we provide a review of the literature on situation assessment and trust, two constructs identified as critical for a fuller understanding of intimate and intricate interactions between a human operator and multiple air vehicles equipped with increasingly autonomous systems. Then, we propose an expansion of Yamani and Horrey’s (2018) model to motivate systematic research on the human operator’s role, identify factors that influence resource allocation and guide human-centered design of an interface supporting the m:N operation in the AAM environment.

Advanced Air Mobility↗
NASA NTRS1976

System for imposing directional stability on a rocket-propelled vehicle

An improved system for use in imposing directional stability on a rocket-propelled vehicle is described. The system includes a pivotally supported engine-mounting platform, a gimbal ring mounted on the platform and adapted to pivotally support a rocket engine and an hydraulic actuator connected to the platform for imparting selected pivotal motion. An accelerometer and a signal comparator circuit for providing error intelligence indicative of aberration in vehicle acceleration is included along with an actuator control circuit connected with the actuator and responsive to error intelligence for imparting pivotal motion to the platform. Relocation of the engine's thrust vector is thus achieved for imparting directional stability to the vehicle.

Perkins, H.↗
NASA NTRSDate not supplied

Benchmark Problem Development for Testing Maturity of Intelligent Contingency Management Tools

Increasingly autonomous Advanced Air Mobility (AAM) vehicles will be required to handle diverse conditions with limited human intervention. Intelligent contingency management (iCM) approaches are under development to address how automated agents can handle unforeseen, unplanned, and unanticipated events. Benchmark scenario is needed to test the maturity of the developed iCM tools and techniques.

Jon Holbrook↗