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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9

Anomaly Detection in Connected and Autonomous Vehicle Trajectories Using LSTM Autoencoder and Gaussian Mixture Model

Connected and Autonomous Vehicles (CAVs) technology has the potential to transform the transportation system. Although these new technologies have many advantages, the implementation raises significant concerns regarding safety, security, and privacy. Anomalies in sensor data caused by errors or cyberattacks can cause severe accidents. To address the issue, this study proposed an innovative anomaly detection algorithm, namely the LSTM Autoencoder with Gaussian Mixture Model (LAGMM). This model supports anomalous CAV trajectory detection in the real-time leveraging communication capabilities of CAV sensors. The LSTM Autoencoder is applied to generate low-rank representations and reconstruct errors for each input data point, while the Gaussian Mixture Model (GMM) is employed for its strength in density estimation. The proposed model was jointly optimized for the LSTM Autoencoder and GMM simultaneously. The study utilizes realistic CAV data from a platooning experiment conducted for Cooperative Automated Research Mobility Applications (CARMAs). The experiment findings indicate that the proposed LAGMM approach enhances detection accuracy by 3% and precision by 6.4% compared to the existing state-of-the-art methods, suggesting a significant improvement in the field.

33 ADVANCED PROPULSION SYSTEMS↗

Apollo experience report: Guidance and control systems: Automated control system for unmanned mission AS-201

The Apollo command module heat shield and Apollo command and service module/Saturn launch vehicle structural integrity were evaluated in an unmanned test flight. An automated control system was developed to provide the mission event sequencing, the real-time ground control interface, and the backup attitude reference system for the unmanned flight. The required mission events, the design logic, the redundancy concept, and the ground-support-equipment concept are described and some development problem areas are discussed. The mission event time line and the real-time ground command list are included to provide an outline of the control system capabilities and requirements. The mission was accomplished with the automated control system, which functioned without flight anomalies.

Holloway, G. F.↗

Human-Automation Allocations for Current Robotic Space Operations

Within the Human Research Program, one risk delineates the uncertainty surrounding crew working with automation and robotics in spaceflight. The Risk of Inadequate Design of Human and Automation/Robotic Integration (HARI) is concerned with the detrimental effects on crew performance due to ineffective user interfaces, system designs and/or functional task allocation, potentially compromising mission success and safety. Risk arises because we have limited experience with complex automation and robotics. One key gap within HARI, is the gap related to functional allocation. The gap states: We need to evaluate, develop, and validate methods and guidelines for identifying human-automation/robot task information needs, function allocation, and team composition for future long duration, long distance space missions. Allocations determine the human-system performance as it identifies the functions and performance levels required by the automation/robotic system, and in turn, what work the crew is expected to perform and the necessary human performance requirements. Allocations must take into account each of the human, automation, and robotic systems capabilities and limitations. Some functions may be intuitively assigned to the human versus the robot, but to optimize efficiency and effectiveness, purposeful role assignments will be required. The role of automation and robotics will significantly change in future exploration missions, particularly as crew becomes more autonomous from ground controllers. Thus, we must understand the suitability of existing function allocation methods within NASA as well as the existing allocations established by the few robotic systems that are operational in spaceflight. In order to evaluate future methods of robotic allocations, we must first benchmark the allocations and allocation methods that have been used. We will present 1) documentation of human-automation-robotic allocations in existing, operational spaceflight systems; and 2) To gather existing lessons learned and best practices in these role assignments, from spaceflight operational experience of crew and ground teams that may be used to guide development for future systems. NASA and other space agencies have operational spaceflight experience with two key Human-Automation-Robotic (HAR) systems: heavy lift robotic arms and planetary robotic explorers. Additionally, NASA has invested in high-fidelity rover systems that can carry crew, building beyond Apollo's lunar rover. The heavy lift robotic arms reviewed are: Space Station Remote Manipulator System (SSRMS), Japanese Remote Manipulator System (JEMRMS), and the European Robotic Arm (ERA, designed but not deployed in space). The robotic rover systems reviewed are: Mars Exploration Rovers, Mars Science Laboratory rover, and the high-fidelity K10 rovers. Much of the design and operational feedback for these systems have been communicated to flight controllers and robotic design teams. As part of the mitigating the HARI risk for future human spaceflight operations, we must document function allocations between robots and humans that have worked well in practice.

robotic allocation↗

An Experimental Study of the Effects of Automation on Pilot Situational Awareness in the Datalink ATC Environment

An experiment to study how automation, when used in conjunction with datalink for the delivery of air traffic control (ATC) clearance amendments, affects the situational awareness of aircrews was conducted. The study was focused on the relationship of situational awareness to automated Flight Management System (FMS) programming and the readback of ATC clearances. Situational awareness was tested by issuing nominally unacceptable ATC clearances and measuring whether the error was detected by the subject pilots. The experiment also varied the mode of clearance delivery: Verbal, Textual, and Graphical. The error detection performance and pilot preference results indicate that the automated programming of the FMS may be superior to manual programming. It is believed that automated FMS programming may relieve some of the cognitive load, allowing pilots to concentrate on the strategic implications of a clearance amendment. Also, readback appears to have value, but the small sample size precludes a definite conclusion. Furthermore, because textual and graphical modes of delivery offer different but complementary advantages for cognitive processing, a combination of these modes of delivery may be advantageous in a datalink presentation.

Hahn, Edward C.↗

Experimental studies on the effect of automation on pilot situational awareness in the datalink ATC environment

An experiment to study how automation, when used in conjunction with datalink for the delivery of ATC clearance amendments, affects the situational awareness of aircrews was conducted. The study was focused on the relationship of situational awareness to automated Flight Management System (FMS) programming of datalinked clearances and the readback of ATC clearances. Situational awareness was tested by issuing nominally unacceptable ATC clearances and measuring whether the error was detected by the subject pilots. The experiment also varied the mode of clearance delivery: Verbal, Textual, and Graphical. The error detection performance and pilot preference results indicate that the automated programming of the FMS may be superior to manual programming. It is believed that automated FMS programming may relieve some of the cognitive load, allowing pilots to concentrate on the strategic implications of a clearance amendment. Also, readback appears to have value, but the small sample size precludes a definite conclusion. Furthermore, because textual and graphical modes of delivery offer different but complementary advantages for cognitive processing, a combination of these modes of delivery may be advantageous in a datalink presentation.

Hahn, Edward C.↗

A teleoperated robotic manipulator system for materials processing experiment servicing

In 1984 Congress authorized NASA to begin the Space Station Program, and requested that 10 percent of program funds be spent in implementing automation and robotics (A and R) on the Space Station. In response to that request, Boeing established several Independent Research and Development (IR and D) projects to explore possible uses for A and R on the Space Station. One of those projects, and automated materials processing experiment, is discussed. The project uses a teleoperated robot to demonstrate telescience applied to a Chemical Vapor Transport materials processing experiment.

Suchting, Steven↗

Mid-Deck Experiments, STS-26

Phase partitioning, ISO electric focusing, automated directional solidification furnace, mesoscale experiment, and others are explained.

Source record↗

Level of Automation and Failure Frequency Effects on Simulated Lunar Lander Performance

A human-in-the-loop experiment was conducted at the NASA Ames Research Center Vertical Motion Simulator, where instrument-rated pilots completed a simulated terminal descent phase of a lunar landing. Ten pilots participated in a 2 x 2 mixed design experiment, with level of automation as the within-subjects factor and failure frequency as the between subjects factor. The two evaluated levels of automation were high (fully automated landing) and low (manual controlled landing). During test trials, participants were exposed to either a high number of failures (75% failure frequency) or low number of failures (25% failure frequency). In order to investigate the pilots' sensitivity to changes in levels of automation and failure frequency, the dependent measure selected for this experiment was accuracy of failure diagnosis, from which D Prime and Decision Criterion were derived. For each of the dependent measures, no significant difference was found for level of automation and no significant interaction was detected between level of automation and failure frequency. A significant effect was identified for failure frequency suggesting failure frequency has a significant effect on pilots' sensitivity to failure detection and diagnosis. Participants were more likely to correctly identify and diagnose failures if they experienced the higher levels of failures, regardless of level of automation

performance↗

Learning About Cockpit Automation: From Piston Trainer to Jet Transport

Two experiments explored the idea of providing cockpit automation training to airline-bound student pilots using cockpit automation equipment commonly found in small training airplanes. In a first experiment, pilots mastered a set of tasks and maneuvers using a GPS navigation computer, autopilot, and flight director system installed in a small training airplane Students were then tested on their ability to complete a similar set of tasks using the cockpit automation system found in a popular jet transport aircraft. Pilot were able to successfully complete 77% of all tasks in the jet transport on their first attempt. An analysis of a control group suggests that the pilot's success was attributable to the application of automation principles they had learned in the small airplane. A second experiment looked at two different ways of delivering small-aeroplane cockpit automation training: a self-study method, and a dual instruction method. The results showed a slight advantage for the self-study method. Overall, the results of the two studies cast a strong vote for the incorporation of cockpit automation training in curricula designed for pilot who will later transition to the jet fleet.

Casner, Stephen M.↗

Human-Automation Allocations for Current Robotic Space Operations: Space Station Remote Manipulator System

NASA’s Human Research Program’s Risk of Inadequate Design of Human and Automation/Robotic Integration (HARI) delineates the uncertainty surrounding crew work with automation and robotics in spaceflight. HARI is concerned with detrimental effects of ineffective user interfaces, system designs and/or functional task allocation on crew performance, potentially compromising mission success and safety. This risk arises because of limited experience with complex automation and robotics in spaceflight. One key knowledge gap within the HARI risk is related to function allocation.

Chang, Mai Lee↗

Demonstration of an AI-driven workflow for dynamic x-ray spectroscopy

X-ray absorption near edge structure (XANES) spectroscopy is a powerful technique for characterizing the chemical state and symmetry of individual elements within materials, but requires collecting data at many energy points which can be time-consuming. While adaptive sampling methods exist for efficiently collecting spectroscopic data, they often lack domain-specific knowledge about the structure of XANES spectra. Here we demonstrate a knowledge-injected Bayesian optimization approach for adaptive XANES data collection that incorporates understanding of spectral features like absorption edges and pre-edge peaks. We show this method accurately reconstructs the absorption edge of XANES spectra using only 15–20% of the measurement points typically needed for conventional sampling, while maintaining the ability to determine the x-ray energy of the sharp peak after the absorption edge with errors less than 0.03 eV, the absorption edge with errors less than 0.1 eV; and overall root-mean-square errors less than 0.005 compared to traditionally sampled spectra. Our experiments on battery materials and catalysts demonstrate the method’s effectiveness for both static and dynamic XANES measurements, improving data collection efficiency and enabling better time resolution for tracking chemical changes. This approach advances the degree of automation in XANES experiments, reducing the common errors of under- or over-sampling points near the absorption edge and enabling dynamic experiments that require high temporal resolution or limited measurement time.

Bayesian optimization↗

AEcroscopy: A Software–Hardware Framework Empowering Microscopy Toward Automated and Autonomous Experimentation

Microscopy has been pivotal in improving the understanding of structure-function relationships at the nanoscale and is by now ubiquitous in most characterization labs. However, traditional microscopy operations are still limited largely by a human-centric click-and-go paradigm utilizing vendor-provided software, which limits the scope, utility, efficiency, effectiveness, and at times reproducibility of microscopy experiments. Here, in this work, a coupled software–hardware platform is developed that consists of a software package termed AEcroscopy (short for Automated Experiments in Microscopy), along with a field-programmable-gate-array device with LabView-built customized acquisition scripts, which overcome these limitations and provide the necessary abstractions toward full automation of microscopy platforms. The platform works across multiple vendor devices on scanning probe microscopes and electron microscopes. It enables customized scan trajectories, processing functions that can be triggered locally or remotely on processing servers, user-defined excitation waveforms, standardization of data models, and completely seamless operation through simple Python commands to enable a plethora of microscopy experiments to be performed in a reproducible, automated manner. This platform can be readily coupled with existing machine-learning libraries and simulations, to provide automated decision-making and active theory-experiment optimization to turn microscopes from characterization tools to instruments capable of autonomous model refinement and physics discovery.

47 OTHER INSTRUMENTATION↗

Frhodo: A program for simulating chemical kinetic measurements and optimizing kinetic mechanisms

Chemical kinetic simulations are frequently used to extract kinetic and mechanistic information from experimental data by fitting simulations to the data. For simple systems of consecutive single channel reactions this is a relatively straightforward task. However, as the complexity of the system increases and particularly for systems involving competitive multi-channel reactions manually optimizing a mechanism to obtain the best fits to a range of experimental data quickly becomes a time-intensive and challenging process, especially when rate coefficients have pressure and temperature dependencies. There is considerable potential for exploiting automated optimization methods to rapidly screen reaction mechanisms and optimize the fit by adjusting rate coefficients within well-defined constraints. A new chemical kinetics simulation program, Frhodo, has been developed for this purpose. The program allows either manual simulation of individual experiments or fully automated optimization against a range of experimental data by adjusting user selected reactions within user defined constraints. Frhodo incorporates machine learning-based optimization routines that work by either minimizing standardized residuals or through Bayesian parameter estimation. Frhodo's optimization capabilities are demonstrated by reexamining two previously studied systems of relevance to combustion. Dissociation of diacetyl followed by recombination of methyl radicals is an example of a sequence of single channel, consecutive reactions. Pyrolysis of 2-methyl furan exemplifies multi-channel, unimolecular reactions that are often encountered and have temperature and pressure dependencies in the rate coefficients of the channels and branching ratios between them. For both systems the optimization routines resulted in solutions similar to the original studies. Finally, comments are made on the strengths and limitations of the approach.

0D simulation↗

The Washington experience (evaluation and selection of hardware for automated geo-based information systems)

A Washington geographic information system is described. The system has thrust from two different angles: the proprietary need for information to support the management of five million acres of thrust lands by the Department of Natural Resources, and the need for data over the entire state because of statewide governmental responsibilities. The data base includes information relevant to agricultural, forest, industrial, business, and community growth with emphasis on assembling information useful in setting intermediate and long-range goals. System selection procedures and system feasibility studies are discussed.

Harding, R. A.↗

"Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs"

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,"are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment leveraged a heterogeneous fleet of 100 longitudinally-controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this paper. The MegaController is a hierarchical control architecture, which consists of two main layers. The upper layer is called Speed Planner, and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock on-board sensors. The Speed Planner ingests live data feeds provided by third parties, as well as data from our own control vehicles, and uses both to perform the speed assignment. The architecture of the speed planner allows for modular use of standard control techniques, such as optimal control, model predictive control, kernel methods and others, including Deep RL, model predictive control and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers, or only some. Control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars, and electronic selection of ACC set points in others. The proposed architecture allows for the combination of all possible settings proposed above. Most configurations were tested throughout the ramp up to the MegaVandertest.

Lee, Jonathan↗

Improving designer productivity

Designer and design team productivity improves with skill, experience, and the tools available. The design process involves numerous trials and errors, analyses, refinements, and addition of details. Computerized tools have greatly speeded the analysis, and now new theories and methods, emerging under the label Artificial Intelligence (AI), are being used to automate skill and experience. These tools improve designer productivity by capturing experience, emulating recognized skillful designers, and making the essence of complex programs easier to grasp. This paper outlines the aircraft design process in today's technology and business climate, presenting some of the challenges ahead and some of the promising AI methods for meeting these challenges.

Hill, Gary C.↗