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At least 937 records · Page 52

Health monitoring of Japanese payload specialist: Autonomic nervous and cardiovascular responses under reduced gravity condition (L-0)

In addition to health monitoring of the Japanese Payload Specialists (PS) during the flight, this investigation also focuses on the changes of cardiovascular hemodynamics during flight which will be conducted under the science collaboration with the Lower Body Negative Pressure (LBNP) Experiment of NASA. For the Japanese, this is an opportunity to examine firsthand the effects of microgravity of human physiology. We are particularly interested in the adaption process and how it relates to space motion sickness and cardiovascular deconditioning. By comparing data from our own experiment to data collected by others, we hope to understand the processes involved and find ways to avoid these problems for future Japanese astronauts onboard Space Station Freedom and other Japanese space ventures. The primary objective of this experiment is to monitor the health condition of Japanese Payload Specialists to maintain a good health status during and after space flight. The second purpose is to investigate the autonomic nervous system's response to space motion sickness. To achieve this, the function of the autonomic nervous system will be monitored using non-invasive techniques. Data obtained will be employed to evaluate the role of autonomic nervous system in space motion sickness and to predict susceptibility to space motion sickness. The third objective is evaluation of the adaption process of the cardiovascular system to microgravity. By observation of the hemodynamics using an echocardiogram we will gain insight on cardiovascular deconditioning. The last objective is to create a data base for use in the health care of Japanese astronauts by obtaining control data in experiment L-O in the SL-J mission.

Sekiguchi, Chiharu↗

Global Precipitation Measurement (GPM) Orbit Design and Autonomous Maneuvers

The NASA Goddard Space Flight Center's Global Precipitation Measurement (GPM) mission must meet the challenge of measuring worldwide precipitation every three hours. The GPM core spacecraft, part of a constellation, will be required to maintain a circular orbit in a high drag environment at a near-critical inclination. Analysis shows that a mean orbit altitude of 407 km is necessary to prevent ground track repeating. Combined with goals to minimize maneuver operation impacts to science data collection and to enable reasonable long-term orbit predictions, the GPM project has decided to fly the GSFC autonomous maneuver system, AutoCon(TM). This system is a follow-up version of the highly successful New Millennium Program technology flown onboard the Earth Observing-1 formation flying mission. This paper presents the driving science requirements and goals of the GPM mission and shows how they will be met. Selection of the mean semi-major axis, eccentricity, and the AV budget for several ballistic properties are presented. The architecture of the autonomous maneuvering system to meet the goals and requirements is presented along with simulations using GPM parameters. Additionally, the use of the GPM autonomous system to mitigate possible collision avoidance and to aid other spacecraft systems during navigation outages is explored.

Folta, David↗

Science Goal Monitor: Science Goal Driven Automation for NASA Missions

Infusion of automation technologies into NASA s future missions will be essential because of the need to: (1) effectively handle an exponentially increasing volume of scientific data, (2) successfully meet dynamic, opportunistic scientific goals and objectives, and (3) substantially reduce mission operations staff and costs. While much effort has gone into automating routine spacecraft operations to reduce human workload and hence costs, applying intelligent automation to the science side, i.e., science data acquisition, data analysis and reactions to that data analysis in a timely and still scientifically valid manner, has been relatively under-emphasized. In order to introduce science driven automation in missions, we must be able to: capture and interpret the science goals of observing programs, represent those goals in machine interpretable language; and allow spacecrafts onboard systems to autonomously react to the scientist's goals. In short, we must teach our platforms to dynamically understand, recognize, and react to the scientists goals. The Science Goal Monitor (SGM) project at NASA Goddard Space Flight Center is a prototype software tool being developed to determine the best strategies for implementing science goal driven automation in missions. The tools being developed in SGM improve the ability to monitor and react to the changing status of scientific events. The SGM system enables scientists to specify what to look for and how to react in descriptive rather than technical terms. The system monitors streams of science data to identify occurrences of key events previously specified by the scientist. When an event occurs, the system autonomously coordinates the execution of the scientist s desired reactions. Through SGM, we will improve om understanding about the capabilities needed onboard for success, develop metrics to understand the potential increase in science returns, and develop an operational prototype so that the perceived risks associated with increased use of automation can be reduced.

Koratkar, Anuradha↗

Random attractor of non-autonomous stochastic Boussinesq lattice system

In this paper, we first consider the existence of tempered random attractor for second-order non-autonomous stochastic lattice dynamical system of nonlinear Boussinesq equations effected by time-dependent coupled coefficients and deterministic forces and multiplicative white noise. Then, we establish the upper semicontinuity of random attractors as the intensity of noise approaches zero.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Present Challenges, Critical Needs, and Future Technological Directions for NASA's GN and C Engineering Discipline

The National Aeronautics and Space Administration (NASA) is currently undergoing a substantial redirection. Notable among the changes occurring within NASA is the stated emphasis on technology development, integration, and demonstration. These new changes within the Agency should have a positive impact on the GN&C discipline given the potential for sizeable investments for technology development and in-space demonstrations of both Autonomous Rendezvous & Docking (AR&D) systems and Autonomous Precision Landing (APL) systems. In this paper the NASA Technical Fellow for Guidance, Navigation and Control (GN&C) provides a summary of the present technical challenges, critical needs, and future technological directions for NASA s GN&C engineering discipline. A brief overview of the changes occurring within NASA that are driving a renewed emphasis on technology development will be presented as background. The potential benefits of the planned GN&C technology developments will be highlighted. This paper will provide a GN&C State-of-the-Discipline assessment. The discipline s readiness to support the goals & objectives of each of the four NASA Mission Directorates is evaluated and the technical challenges and barriers currently faced by the discipline are summarized. This paper will also discuss the need for sustained investments to sufficiently mature the several classes of GN&C technologies required to implement NASA crewed exploration and robotic science missions.

Dennehy, Cornelius J.↗

Sampling Technique for Robust Odorant Detection Based on MIT RealNose Data

This technique enhances the detection capability of the autonomous Real-Nose system from MIT to detect odorants and their concentrations in noisy and transient environments. The lowcost, portable system with low power consumption will operate at high speed and is suited for unmanned and remotely operated long-life applications. A deterministic mathematical model was developed to detect odorants and calculate their concentration in noisy environments. Real data from MIT's NanoNose was examined, from which a signal conditioning technique was proposed to enable robust odorant detection for the RealNose system. Its sensitivity can reach to sub-part-per-billion (sub-ppb). A Space Invariant Independent Component Analysis (SPICA) algorithm was developed to deal with non-linear mixing that is an over-complete case, and it is used as a preprocessing step to recover the original odorant sources for detection. This approach, combined with the Cascade Error Projection (CEP) Neural Network algorithm, was used to perform odorant identification. Signal conditioning is used to identify potential processing windows to enable robust detection for autonomous systems. So far, the software has been developed and evaluated with current data sets provided by the MIT team. However, continuous data streams are made available where even the occurrence of a new odorant is unannounced and needs to be noticed by the system autonomously before its unambiguous detection. The challenge for the software is to be able to separate the potential valid signal from the odorant and from the noisy transition region when the odorant is just introduced.

Duong, Tuan A.↗

Establishing the Assurance Efficacy of Automated Risk Mitigation Strategies

Verification and validation of increasingly autonomous aviation systems is a major challenge. Traditional techniques for the assurance of high-confidence, safety-critical systems are not equipped to handle the complexity, uncertainty, and lack of predictability inherent in non-deterministic systems. Techniques such as run time monitoring, formal methods, and testing and simulation have been applied to some effect, but it is difficult to properly assess the success of such measures. The authors propose the concept of Assurance Efficacy to address this gap. Assurance Efficacy is seen as a parameter, criteria, or perspective by which to evaluate, identify and explore safety risk mitigation strategies and operational assurance architectures. Validation of the utility of this concept through flight testing is a first step in determining its potential role in assessing the overall safety of complex, increasingly autonomous systems that cannot be fully assured in the design phase.

system safety↗

Autonomous rendezvous and capture system design

Marshall Space Flight Center has a long history of involvement in the design of Autonomous Rendezvous and Capture (AR&C) systems. The first extensive studies were begun in the late seventies, incrementally leading to the development of an assortment of Guidance, Navigation, and Control (GN&C) concepts and algorithms suitable for a variety of mission requirements and spacecraft capabilities, with a strong emphasis placed upon flexible system-level design. These efforts have led to the development of sophisticated algorithms for docking with tumbling targets, and simple but efficient algorithms for stabilized spacecraft; each has been tested and validated using dynamic system simulation, with hardware in the loop when practical. Recent investigations include the use of neural networks for video image interpretation, and fuzzy logic for control system implementation.

Dabney, Richard W.↗

Formal Methods in the Development of Highly Assured Software for Unmanned Aircraft Systems

In traditional software development methodologies, operational and functional requirements of systems are often specified in structured natural language notations. These restricted notations provide good documentation support, but only provide limited support for semantic analysis. These notations are generally not rich enough to unambiguously specify the requirements of safety-critical systems that, for example, involve complex numerical computations or that interact with the physical environment. Examples of these safety-critical systems are autonomous vehicles such as unmanned aircraft systems. This talk advocates the use of expressive formal logics, such as higher-order logic, to specify the operational and functional requirement of unmanned systems and to prove the correctness of these requirements. Semantic analysis of requirements written in higher-order logic is supported through the use of interactive theorem provers. Formal models serve as ideal reference implementations of functional requirements. Hence, formal logics enable software validation techniques where software implementations can be checked against functional requirements in a mechanical way. The Formal Methods group in the Safety-Critical Avionics Systems Branch at NASA Langley Research Center has conducted research on the development and application of formal verification techniques to safety-critical applications of interest to NASA for more than 30 years. This talk illustrates the use of formal methods in the development of highly-assured autonomous unmanned aircraft systems.

Formal Methods↗

Control Algorithms and Simulated Environment Developed and Tested for Multiagent Robotics for Autonomous Inspection of Propulsion Systems

The NASA Glenn Research Center and academic partners are developing advanced multiagent robotic control algorithms that will enable the autonomous inspection and repair of future propulsion systems. In this application, on-wing engine inspections will be performed autonomously by large groups of cooperative miniature robots that will traverse the surfaces of engine components to search for damage. The eventual goal is to replace manual engine inspections that require expensive and time-consuming full engine teardowns and allow the early detection of problems that would otherwise result in catastrophic component failures. As a preliminary step toward the long-term realization of a practical working system, researchers are developing the technology to implement a proof-of-concept testbed demonstration. In a multiagent system, the individual agents are generally programmed with relatively simple controllers that define a limited set of behaviors. However, these behaviors are designed in such a way that, through the localized interaction among individual agents and between the agents and the environment, they result in self-organized, emergent group behavior that can solve a given complex problem, such as cooperative inspection. One advantage to the multiagent approach is that it allows for robustness and fault tolerance through redundancy in task handling. In addition, the relatively simple agent controllers demand minimal computational capability, which in turn allows for greater miniaturization of the robotic agents.

Wong, Edmond↗

CID-720 aircraft high-environment flight instrumentation system

The high-environment flight instrumentation system was designed to acquire Langley's structural response data during the full scale transport-controlled impact demonstration test. There was only one opportunity for data acquisition. Thus, a high reliability and crashworthy design approach was implemented. The approach featured multi-level redundancy and a vigorous quality assurance testing program. Complying with an accelerated schedule, the instrumentation system was developed, tested and shipped within 18 months to Dryden Flight Research Facility. The flight instrumentation system consists of two autonomous data systems, DAS #1 and #2, and an excellent checkout subsystem. Each data system is partitioned into four pallets. The system was designed to operate on manned and unmanned flights. There are 176 data channels per data system. These channels are sequentially sampled and encoded into 1 megabit/sec pulse code modulation (PCM) data signal. To increase the probability of success, a special PCM distribution subsystem was developed. This subsystem distributes the PCM signal to two transmitters, one delay memory, and eight recorder tracks. The data on four of these trackes was digitally delayed approximately 300 msec to maximize data acquisition during impact. Therefore each data system's data is redundantly recorded onboard and on the ground. There are two time code generators. Parallel time from each is encoded into both data systems. Serial time from each is redundantly recorded on both onboard recorders. Instrumentation power is independent of aircraft power and self-contained.

Calloway, R. S.↗

Intelligent fault isolation and diagnosis for communication satellite systems

Discussed here is a prototype diagnosis expert system to provide the Advanced Communication Technology Satellite (ACTS) System with autonomous diagnosis capability. The system, the Fault Isolation and Diagnosis EXpert (FIDEX) system, is a frame-based system that uses hierarchical structures to represent such items as the satellite's subsystems, components, sensors, and fault states. This overall frame architecture integrates the hierarchical structures into a lattice that provides a flexible representation scheme and facilitates system maintenance. FIDEX uses an inexact reasoning technique based on the incrementally acquired evidence approach developed by Shortliffe. The system is designed with a primitive learning ability through which it maintains a record of past diagnosis studies.

Tallo, Donald P.↗

Autonomous Science: Simulated Solar System Mission to Enceladus, Icy Ocean Moon of Saturn

Future NASA missions to icy ocean worlds such as Europa, Titan, or Enceladus will collect mass spectrometry (MS) data from exospheres, atmospheres, and plume volatiles to assess their geochemistry and potential for microbial life1. These remote missions face challenges related to limited bandwidth, communication, and power, highlighting the need for science autonomy2 to prioritize data for timely downlink. We extend our previous work on machine learning (ML) biosignature detection from isotope ratio mass spectrometry (IRMS) data to distributed systems missions (DSM) by incorporating the ML and autonomous data quality control and prioritization code into an onboard decision-making platform. We use simulated orbital telemetry for eight orbiters around Enceladus with a mothership to illustrate an automated biosignature and novel seawater chemistry detection with data prioritization from MS analyses of volatile CO2.

Conor Williams↗

Using Generative AI to implement the discrepancy checker for a Nearly Autonomous Management and Control System for Advanced Reactors

Developments related to generative artificial intelligence (AI) have brought a major breakthrough in AI. These developments are rapidly accelerating developments in different science and engineering applications. Nearly Autonomous Management and Control (NAMAC) system provides recommendations to the operator for maintaining the safety and performance of the reactor. The discrepancy checker (DC) is an important component of the NAMAC) system, whose goal is to determine if the plant is moving towards the expected system state after the control actions are injected. In this work, we explore generative AI methods, particularly, a generative pretrained transformer (GPT) for implementing the DC function in NAMAC. The GPT-based DC aims to alert the operator in situations outside NAMAC’s scope and act as a chatbot the operator can use to retrieve relevant information. This study involves two versions of GPT developed by OpenAI: GPT-3.5 and GPT-4. These GPTs are trained on huge amounts of undisclosed general domain datasets. We explored two methods to adapt GPTs for DC implementation in NAMAC: fine-tuning and retrieval augmented generation. A small knowledge base (information file) that encompasses rules for DC implementation and some general information related to NAMAC has been created to support DC implementation using GPT. In this work, the GPT-based DC implementations have been tested for their reasoning abilities, comprehension, information retrieval, and extraction abilities. It should be noted that this paper only presents a preliminary study to test the feasibility of DC implementation using generative AI technology. Given the potential risks and severe consequences associated with nuclear reactor applications, combined with the black-box nature of AI, extensive offline and online testing and reliability analyses of GPT-based DCs are needed for further developing such capabilities.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Compute Element and Interface Box for the Hazard Detection System

The Autonomous Landing and Hazard Avoidance Technology (ALHAT) program is building a sensor that enables a spacecraft to evaluate autonomously a potential landing area to generate a list of hazardous and safe landing sites. It will also provide navigation inputs relative to those safe sites. The Hazard Detection System Compute Element (HDS-CE) box combines a field-programmable gate array (FPGA) board for sensor integration and timing, with a multicore computer board for processing. The FPGA does system-level timing and data aggregation, and acts as a go-between, removing the real-time requirements from the processor and labeling events with a high resolution time. The processor manages the behavior of the system, controls the instruments connected to the HDS-CE, and services the "heavy lifting" computational requirements for analyzing the potential landing spots.

Villalpando, Carlos Y.↗

Perception system and functions for autonomous navigation in a natural environment

This paper presents the approach, algorithms, and processes we developed for the perception system of a cross-country autonomous robot. After a presentation of the tele-programming context we favor for intervention robots, we introduce an adaptive navigation approach, well suited for the characteristics of complex natural environments. This approach lead us to develop a heterogeneous perception system that manages several different terrain representatives. The perception functionalities required during navigation are listed, along with the corresponding representations we consider. The main perception processes we developed are presented. They are integrated within an on-board control architecture we developed. First results of an ambitious experiment currently underway at LAAS are then presented.

Chatila, Raja↗