SEARCH · Engineering Papers

Results for “autonomous experiment”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 451 records · Page 25

All sources NASA NTRS DOE OSTI

NASA NTRS1998

Operational Concepts for Uninhabited Tactical Aircraft

This paper describes experiences with five remotely piloted flight research vehicle projects in the developmental flight test phase. These projects include the Pathfinder, Perseus B, Altus, and X-36 aircraft and the Highly Maneuverable Aircraft Technology (HiMAT). Each of these flight projects was flown at the NASA Dryden Flight Research Center. With the exception of the HIMAT, these projects are a part of the Flight Research Base Research and Technology (R&T) Program of the NASA Aeronautics and Space Transportation Technology Enterprise. Particularly with respect to operational interfaces between the ground-based pilot or operator, this paper draws from those experiences, then provides some rationale for extending the lessons learned during developmental flight research to the possible situations involved in the developmental flights proceeding deployed uninhabited tactical aircraft (UTA) operations. Two types of UTA control approaches are considered: autonomous and remotely piloted. In each of these cases, some level of human operator or pilot control blending is recommended. Additionally, "best practices" acquired over years of piloted aircraft experience are drawn from and presented as they apply to operational UTA.

Deets, Dwain A.↗

NASA NTRS1987

Visual monitoring of autonomous life sciences experimentation

The design and implementation of a computerized visual monitoring system to aid in the monitoring and control of life sciences experiments on board a space station was investigated. A likely multiprocessor design was chosen, a plausible life science experiment with which to work was defined, the theoretical issues involved in the programming of a visual monitoring system for the experiment was considered on the multiprocessor, a system for monitoring the experiment was designed, and simulations of such a system was implemented on a network of Apollo workstations.

Blank, G. E.↗

DOE OSTI2022

Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

Recent progress in machine learning methods and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs) have propelled automated and autonomous microscopies to the forefront of attention of the scientific community. However, enabling automated microscopy requires the development of task-specific machine learning methods, understanding the interplay between physics discovery and machine learning, and fully defined discovery workflows. This, in turn, requires balancing the physical intuition and prior knowledge of the domain scientist with rewards that define experimental goals and machine learning algorithms that can translate these to specific experimental protocols. Here, we discuss the basic principles of Bayesian active learning and illustrate its applications for SPM. We progress from the Gaussian process as a simple data-driven method and Bayesian inference for physical models as an extension of physics-based functional fits to more complex deep kernel learning methods, structured Gaussian processes, and hypothesis learning. These frameworks allow for the use of prior data, the discovery of specific functionalities as encoded in spectral data, and exploration of physical laws manifesting during the experiment. Here, the discussed framework can be universally applied to all techniques combining imaging and spectroscopy, SPM methods, nanoindentation, electron microscopy and spectroscopy, and chemical imaging methods and can be particularly impactful for destructive or irreversible measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Programmable topotaxis of magnetic rollers in time-varying fields

We discuss how spatially uniform, time-periodic magnetic fields can be designed to power and direct the migration of ferromagnetic spheres up (or down) local gradients in the topography of a solid substrate. Our results are based on a dynamical model that considers the time-varying magnetic torques on the particle and its motion through the fluid at low Reynolds number. We use both analytical theory and numerical simulation to design magnetic fields that maximize the migration velocity up (or down) an inclined plane. We show how “topotaxis” of spherical particles relies on differences in the hydrodynamic resistance to rotation about axes parallel and perpendicular to the plane. Importantly, the designed fields can drive multiple independent particles to move simultaneously in different directions as determined by gradients in their respective environments. Experiments on ferromagnetic spheres provide evidence for topotactic motions up inclined substrates. The ability to program the autonomous navigation of driven particles within anisotropic environments is relevant to the design of colloidal robots.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1987

Monitoring robot actions for error detection and recovery

Reliability is a serious problem in computer controlled robot systems. Although robots serve successfully in relatively simple applications such as painting and spot welding, their potential in areas such as automated assembly is hampered by programming problems. A program for assembling parts may be logically correct, execute correctly on a simulator, and even execute correctly on a robot most of the time, yet still fail unexpectedly in the face of real world uncertainties. Recovery from such errors is far more complicated than recovery from simple controller errors, since even expected errors can often manifest themselves in unexpected ways. Here, a novel approach is presented for improving robot reliability. Instead of anticipating errors, researchers use knowledge-based programming techniques so that the robot can autonomously exploit knowledge about its task and environment to detect and recover from failures. They describe preliminary experiment of a system that they designed and constructed.

Gini, M.↗

NASA NTRS1994

Lunar rover technology demonstrations with Dante and Ratler

Carnegie Mellon University has undertaken a research, development, and demonstration program to enable a robotic lunar mission. The two-year mission scenario is to traverse 1,000 kilometers, revisiting the historic sites of Apollo 11, Surveyor 5, Ranger 8, Apollo 17, and Lunokhod 2, and to return continuous live video amounting to more than 11 terabytes of data. Our vision blends autonomously safeguarded user driving with autonomous operation augmented with rich visual feedback, in order to enable facile interaction and exploration. The resulting experience is intended to attract mass participation and evoke strong public interest in lunar exploration. The encompassing program that forwards this work is the Lunar Rover Initiative (LRI). Two concrete technology demonstration projects currently advancing the Lunar Rover Initiative are: (1) The Dante/Mt. Spurr project, which, at the time of this writing, is sending the walking robot Dante to explore the Mt. Spurr volcano, in rough terrain that is a realistic planetary analogue. This project will generate insights into robot system robustness in harsh environments, and into remote operation by novices; and (2) The Lunar Rover Demonstration project, which is developing and evaluating key technologies for navigation, teleoperation, and user interfaces in terrestrial demonstrations. The project timetable calls for a number of terrestrial traverses incorporating teleoperation and autonomy including natural terrain this year, 10 km in 1995. and 100 km in 1996. This paper will discuss the goals of the Lunar Rover Initiative and then focus on the present state of the Dante/Mt. Spurr and Lunar Rover Demonstration projects.

Krotkov, Eric↗

NASA NTRS1996

South Atlantic Anomaly Entry and Exit as Measured by the X-Ray Timing Explorer

The Rossi X-ray Timing Explorer (RXTE) carries instruments that must switch off high voltages (HV) when passing through the South Atlantic Anomaly (SAA). The High Energy X-ray Timing Experiment (HEXTE) contains a particle monitor that detects the increased particle flux associated with the SAA and autonomously reduces its voltage. The Proportional Counter Array (PCA) relies on uplinked predictions of SAA entry/exit times based on ephemeris data provided by the Flight Dynamics Facility. A third instrument, the All-Sky Monitor (ASM) also uses a predicted SAA model to reduce voltage when passing through the SAA. Data collected from the HEXTE particle monitor, as well as other instrument readings near the times of SAA entry/exit offer the potential for refining models of the boundaries of the SAA. The SAA has an increased particle flux which causes high rates of detection in the RXTE instruments designed to observe x-rays. The high counting rates could degrade the PCA if HV is not reduced during SAA passages. On the other hand, PCA downtime can be minimized and the science return can be optimized by having the best possible model of the SAA boundary. Thus, the PCA team planned an extensive effort during in-orbit checkout to utilize both the HEXTE particle monitor data and instrument counting rates to refine the model of the SAA boundary. The times of SAA entry and exit are compared with the definitive epemeris to determine the precise location (latitude and longitude) of the SAA boundary. Over time, the SAA and its perimeter were mapped. The RXTE Science Operations Center is continuously working to feed back the results of this effort into the science scheduling process, improving the SAA model as it affects the RXTE instruments, thus obtaining more accurate estimates of the SAA entry/exit times.

Smith, Evan↗

NASA NTRS1997

Autonomous Navigation of the SSTI/Lewis Spacecraft Using the Global Positioning System (GPS)

The National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) Flight Dynamics Division (FDD) is pursuing the application of Global Positioning System (GPS) technology to improve the accuracy and economy of spacecraft navigation. High-accuracy autonomous navigation algorithms are being flight qualified in conjunction with GSFC's GPS Attitude Determination Flyer (GADFLY) experiment on the Small Satellite Technology Initiative (SSTI) Lewis spacecraft, which is scheduled for launch in 1997. Preflight performance assessments indicate that these algorithms can provide a real-time total position accuracy of better than 10 meters (1 sigma) and velocity accuracy of better than 0.01 meter per second (1 sigma), with selective availability at typical levels. This accuracy is projected to improve to the 2-meter level if corrections to be provided by the GPS Wide Area Augmentation System (WAAS) are included.

Hart, R. C.↗

NASA NTRS2001

Automating Trend Analysis for Spacecraft Constellations

Spacecraft trend analysis is a vital mission operations function performed by satellite controllers and engineers, who perform detailed analyses of engineering telemetry data to diagnose subsystem faults and to detect trends that may potentially lead to degraded subsystem performance or failure in the future. It is this latter function that is of greatest importance, for careful trending can often predict or detect events that may lead to a spacecraft's entry into safe-hold. Early prediction and detection of such events could result in the avoidance of, or rapid return to service from, spacecraft safing, which not only results in reduced recovery costs but also in a higher overall level of service for the satellite system. Contemporary spacecraft trending activities are manually intensive and are primarily performed diagnostically after a fault occurs, rather than proactively to predict its occurrence. They also tend to rely on information systems and software that are oudated when compared to current technologies. When coupled with the fact that flight operations teams often have limited resources, proactive trending opportunities are limited, and detailed trend analysis is often reserved for critical responses to safe holds or other on-orbit events such as maneuvers. While the contemporary trend analysis approach has sufficed for current single-spacecraft operations, it will be unfeasible for NASA's planned and proposed space science constellations. Missions such as the Dynamics, Reconnection and Configuration Observatory (DRACO), for example, are planning to launch as many as 100 'nanospacecraft' to form a homogenous constellation. A simple extrapolation of resources and manpower based on single-spacecraft operations suggests that trending for such a large spacecraft fleet will be unmanageable, unwieldy, and cost-prohibitive. It is therefore imperative that an approach to automating the spacecraft trend analysis function be studied, developed, and applied to missions such as DRACO with the intent that mission operations costs be significantly reduced. The goal of the Constellation Spacecraft Trend Analysis Toolkit (CSTAT) project is to serve as the pathfinder for a fully automated trending system to support spacecraft constellations. The development approach to be taken is evolutionary. In the first year of the project, the intent is to significantly advance the state of the art in current trending systems through improved functionality and increased automation. In the second year, the intent is to add an expert system shell, likely through the adaptation of an existing commercial-off-the-shelf (COTS) or government-off-the-shelf (GOTS) tool to implement some level of the trending intelligence that humans currently provide in manual operations. In the third year, the intent is to infuse the resulting technology into a near-term constellation or formation-flying mission to test it and gain experience in automated trending. The lessons learned from the real missions operations experience will then be used to improve the system, and to ultimately incorporate it into a fully autonomous, closed-loop mission operations system that is truly capable of supporting large constellations. In this paper, the process of automating trend analysis for spacecraft constellations will be addressed. First, the results of a survey on automation in spacecraft mission operations in general, and in trending systems in particular will be presented to provide an overview of the current state of the art. Next, a rule-based model for implementing intelligent spacecraft subsystem trending will be then presented, followed by a survey of existing COTS/GOTS tools that could be adapted for implementing such a model. The baseline design and architecture of the CSTAT system will be presented. Finally, some results obtained from initial software tests and demonstrations will be presented.

Davis, George↗

NASA NTRS2003

An Automated, Low Mass, Low Power Drill for Acquiring Subsurface Samples of Ground Ice for Astrobiology Studies on Earth and on Mars

As a project that is part of NASA s Astrobiology Technology & Instrument Development Program (ASTID), we are developing a low mass (approx.20kg) drill that will be operated without drilling fluids and at very low power levels (approx.60 watts electrical) to access and retrieve samples from permafrost regions of Earth and Mars. The drill, designed and built as a joint effort by NASA JSC and Baker-Hughes International, takes the form of a down-hole unit attached to a cable so that it can, in principle, be scaled easily to reach significant depths. A parallel laboratory effort is being carried out at UC Berkeley to characterize the physics of dry drilling under martian conditions of pressure, temperature and atmospheric composition. Data from the UCB and JSC laboratory experiments are being used as input to a drill simulation program which is under development to provide autonomous control of the drill. The first Arctic field test of the unit is planned for May 2004. A field expedition to Eureka on Ellesmere Island in Spring 2003 provided an introduction for several team members to the practical aspects of drilling under Arctic conditions. The field effort was organized by Wayne Pollard of McGill University and Christopher McKay of NASA ARC. A conventional science drill provided by New Zealand colleagues was used to recover ground ice cores for analysis of their microbial content and also to develop techniques using tracers to track the depth of penetration of contamination from the core surface into the interior of the samples.

Briggs, G. A.↗

NASA NTRS2018

Use of Multi-Spectral High Repetition Rate LED Systems for High Bandwidth Underwater Optical Communications, and Communications to Surface and Aerial Systems

A variety of both existing and developing sensors would benefit from near real time communication of high bandwidth data. To cite just one example, sensors that could more accurately report real-time positions of marine mammals would be useful in reducing whale-ship collisions. Similar considerations are relevant for maritime port and harbor security, including detection and alerts for divers or autonomous underwater vehicles (AUVs) that could pose a risk to ships. Especially in ports and harbors, field experiments have confirmed that acoustic communication in these cluttered and noisy shallow water environments, compounded with vertical reflecting surfaces formed by piers and pilings, can limit the reliability and utility of underwater acoustic communications. Moreover, many sensors have greater bandwidth requirements than acoustic communications are able to provide. We here discuss the development of high repetition rate multispectral LED optical systems initially developed for imaging, but also capable of simultaneous data transmission at rates of approximately100 kilobits per second. Results are discussed for the multispectral images from coral reefs in Guam, and data transmission experiments from underwater to surface vessels. Subsequent field efforts will extend data transmission from AUVs to unmanned aircraft systems (UAS).

Underwater↗

NASA NTRS2003

Sensory Motor Coordination in Robonaut

As a participant of the year 2000 NASA Summer Faculty Fellowship Program, I worked with the engineers of the Dexterous Robotics Laboratory at NASA Johnson Space Center on the Robonaut project. The Robonaut is an articulated torso with two dexterous arms, left and right five-fingered hands, and a head with cameras mounted on an articulated neck. This advanced space robot, now driven only teleoperatively using VR gloves, sensors and helmets, is to be upgraded to a thinking system that can find, interact with and assist humans autonomously, allowing the Crew to work with Robonaut as a (junior) member of their team. Thus, the work performed this summer was toward the goal of enabling Robonaut to operate autonomously as an intelligent assistant to astronauts. Our underlying hypothesis is that a robot can develop intelligence if it learns a set of basic behaviors (i.e., reflexes - actions tightly coupled to sensing) and through experience learns how to sequence these to solve problems or to accomplish higher-level tasks. We describe our approach to the automatic acquisition of basic behaviors as learning sensory-motor coordination (SMC). Although research in the ontogenesis of animals development from the time of conception) supports the approach of learning SMC as the foundation for intelligent, autonomous behavior, we do not know whether it will prove viable for the development of autonomy in robots. The first step in testing the hypothesis is to determine if SMC can be learned by the robot. To do this, we have taken advantage of Robonaut's teleoperated control system. When a person teleoperates Robonaut, the person's own SMC causes the robot to act purposefully. If the sensory signals that the robot detects during teleoperation are recorded over several repetitions of the same task, it should be possible through signal analysis to identify the sensory-motor couplings that accompany purposeful motion. In this report, reasons for suspecting SMC as the basis for intelligent behavior will be reviewed. A robot control system for autonomous behavior that uses learned SMC will be proposed. Techniques for the extraction of salient parameters from sensory and motor data will be discussed. Experiments with Robonaut will be discussed and preliminary data presented.

Peters, Richard Alan, II↗

NASA NTRS1994

P-MASS and P-GBA: Two new hardware developments for growing plants in space

Plant growth, and especially plant performance experiments in microgravity are limited by the currently available plant growth facilities (low light levels, inadequate nutrient delivery and atmosphere conditioning systems, insufficient science instrumentation, infrequent flight opportunities). In addition, mission durations of 10 to 14 days aboard the NSTS Space Shuttle allow for only brief periods of microgravity exposure with respect to the life cycle of a plant. Based on seed germination experiments, using the Generic BioProcessing Apparatus hardware (GBA), two new payloads have been designed specifically for plant growth. These payloads provide new opportunities for plant gravitational and space biology research and emphasize the investigation of plant performance (photosynthesis, biomass accumulations) in microgravity. The Plant-Module for Autonomous Space Support (P-MASS) was designed to utilize microgravity exposure times in excess of 30 days on the first flight of the recoverable COMET satellite (Commercial Experiment Transporter). The Plant-Generic Bioprocessing Apparatus (P-GBA), is designed for the National Space Transportation System (NSTS) Space Shuttle middeck and the SPACEHAB environment. The P-GBA is an evolution from the GBA hardware and P-MASS (plant chamber and instrumentation). The available light levels of both payloads more than double currently available capabilities.

Hoehn, Alexander↗

NASA NTRSDate not supplied

ATTRACTOR: Toward Trustworthy and Trusted Autonomous Systems

The question of what it means and what it takes for an autonomous system to consider another autonomous system justifiably trustworthy must be addressed by all who seek to integrate intelligent machine agents into real-world operations. A satisfactory answer to this question is an essential component in accepting autonomous machine decision-making in safety-critical and time-critical environments, such as aviation. Historically, simulation platforms for test and evaluation of complex systems have proven to be effective in assessing performance and contributing to decisions on the fitness of systems to operate in current general and commercial aviation airspace. Moreover, simulations have informed the definition of safety-critical constraints. However, as machine systems progressively take on responsibilities for decision-making traditionally supplied by humans, simulations require enhancement. Mixed reality simulation that integrates real-world platforms and data or high-fidelity simulation data in a sim-to-flight paradigm provides insight into agent interaction and the rationale behind autonomous agent decision-making as well as the capacity for seamless integrated implementation, testing, and operation of systems. Strong simulation capabilities are especially important in the presence of algorithms that hold great promise in decision-making yet increase the uncertainty in the system. Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) is a subproject of NASA’s Convergent Aeronautics Solutions (CAS) Project. ATTRACTOR’s objective is to build a basis for understanding trust and trustworthiness in multi-agent autonomous teams, and thus to inform future certification of safety-critical and time-critical autonomous systems in aviation. Because the concepts of trust and trustworthiness must be addressed in a context, ATTRACTOR has chosen Search and Rescue (SAR) in dynamic and unstructured environments, with emphasis on search, as its design reference mission (DRM). During dynamic planning and execution of trajectory-based operations, autonomous agents determine their trajectories given an assigned mission or missions and call for assistance from an appropriate teammate when needed. This experience along with the attendant human-machine and machine-machine interactions, serve as a platform for developing approaches to identifying and measuring trustworthiness and increasing trust. In this paper, we give an overview of some of ATTRACTOR’s research and development activities, findings, and ongoing work.

ATTRACTOR↗

DOE OSTI2022

Integrated Monitoring of Macroalgae Farms Using Acoustics and UUV Sensing

The vision of this project was to develop an integrated system for autonomous underwater vehicle (AUV) monitoring of offshore kelp farms using acoustic, environmental, and optical sensors. This project supports the overall MARINER goals of developing an offshore kelp aquaculture industry to produce low-carbon or carbon-neutral biofuels. The project commenced in the spring of 2018 and used laboratory experiments to test the efficacy of acoustic sensors for monitoring kelp farm lines and growing kelp biomass. Sensors were then integrated onto two AUVs as well as an autonomous surface vehicle in order to establish the optimal type, price point, and vehicle to most efficiently monitor kelp farm structures, kelp biomass, and the surrounding environment. Multiple field deployments of these vehicles and sensors in Massachusetts, New Hampshire, and Maine confirmed that farm lines and kelp could be visualized and that monitoring the spatial patterns of environmental variables was possible. The COVID-19 pandemic severely limited fieldwork activities and laboratory testing, with deployments around kelp farms not occurring again until January 2021. Time spent away from the field focused on data visualization and the development of a low-cost, vessel-based sensor system. Unfortunately, the low-cost system experienced a disk failure during its first deployment and due to multiple resignations from the project team further engineering and development was not possible. Additionally, final results from acoustic sensor testing in 2021 were also not able to be completed due to the resignation of the postdoc leading the analysis. Despite these setbacks, multiple avenues for further development of optical imagery processing from the 360-degree Kelpcam camera and testing of the low-cost sensor system may be possible.

09 BIOMASS FUELS↗

DOE OSTI2026

A large-scale benchmarking of deterministic and stochastic derivative-free optimization algorithms

This presentation summarizes our work in the PrOMMiS project on benchmarking of data-driven optimization algorithms and their applications in self-driving laboratories. This work supports the broader project goal of accelerating the identification of promising separation methods and operating conditions for critical minerals separation processes. We present a systematic benchmarking study of 42 data-driven optimization algorithms on a broad collection of 502 test problems. The results identify BAM, GLCCLUSTER, and MULTIMIN as the most effective optimization solvers, with BAM showing the highest overall performance and solving more than 80% of the benchmark problems. The study also shows that no single solver consistently outperforms the others across all problem types, indicating that our future laboratory applications may benefit from using a small set of strong solvers rather than relying on a single method. The presentation also illustrates an in-silico chemical reactor case study showing that data-driven optimization methods can guide autonomous experimentation in a self-driving laboratory and identify optimal operating conditions within a small number of experiments. Overall, the results provide a basis for selecting efficient optimization methods and demonstrate the practical use of data-driven optimization in self-driving laboratory workflows.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Algorithm-guided experimentation for autonomous AI systems in self-driving laboratories

36 MATERIALS SCIENCE↗

NASA NTRS1982

HEAO project - Revisited

A stellar inertial attitude reference with strapdown rate integrating gyros, together with star trackers and a general purpose computer, constitute the attitude control subsystem shared by NASA's HEAO observatories. HEAO-2, which is equipped with the largest and most precise grazing incidence X-ray mirror deployed aboard a satellite to date, has achieved accurate telescope pointing in autonomous maneuvering to selected targets, the acquisition of guide stars for attitude updating, and the positioning of unique experiments at the telescope focal point. Performance data have confirmed maneuvering accuracies of 1 arcmin, and pointing accuracies of 2-5 arcsec. Additional performance results presented support the extrapolation of the present HEAO attitude reference design to future applications.

Wojtalik, F. S.↗