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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Supervised autonomous rendezvous and docking system technology evaluation

Technology for manned space flight is mature and has an extensive history of the use of man-in-the-loop rendezvous and docking, but there is no history of automated rendezvous and docking. Sensors exist that can operate in the space environment. The Shuttle radar can be used for ranges down to 30 meters, Japan and France are developing laser rangers, and considerable work is going on in the U.S. However, there is a need to validate a flight qualified sensor for the range of 30 meters to contact. The number of targets and illumination patterns should be minimized to reduce operation constraints with one or more sensors integrated into a robust system for autonomous operation. To achieve system redundancy, it is worthwhile to follow a parallel development of qualifying and extending the range of the 0-12 meter MSFC sensor and to simultaneously qualify the 0-30(+) meter JPL laser ranging system as an additional sensor with overlapping capabilities. Such an approach offers a redundant sensor suite for autonomous rendezvous and docking. The development should include the optimization of integrated sensory systems, packaging, mission envelopes, and computer image processing to mimic brain perception and real-time response. The benefits of the Global Positioning System in providing real-time positioning data of high accuracy must be incorporated into the design. The use of GPS-derived attitude data should be investigated further and validated.

Marzwell, Neville I.↗
NASA NTRS1999

Classification of JERS-1 Image Mosaic of Central Africa Using A Supervised Multiscale Classifier of Texture Features

In this paper, a multiscale approach is introduced to classify the Japanese Research Satellite-1 (JERS-1) mosaic image over the Central African rainforest. A series of texture maps are generated from the 100 m mosaic image at various scales. Using a quadtree model and relating classes at each scale by a Markovian relationship, the multiscale images are classified from course to finer scale. The results are verified at various scales and the evolution of classification is monitored by calculating the error at each stage.

Saatchi, Sassan↗
NASA NTRS1997

Supervised Classification Techniques for Hyperspectral Data

The recent development of more sophisticated remote sensing systems enables the measurement of radiation in many mm-e spectral intervals than previous possible. An example of this technology is the AVIRIS system, which collects image data in 220 bands. The increased dimensionality of such hyperspectral data provides a challenge to the current techniques for analyzing such data. Human experience in three dimensional space tends to mislead one's intuition of geometrical and statistical properties in high dimensional space, properties which must guide our choices in the data analysis process. In this paper high dimensional space properties are mentioned with their implication for high dimensional data analysis in order to illuminate the next steps that need to be taken for the next generation of hyperspectral data classifiers.

Jimenez, Luis O.↗
NASA NTRS2005

Semi-Supervised Data Summarization: Using Spectral Libraries to Improve Hyperspectral Clustering

Hyperspectral imagers produce very large images, with each pixel recorded at hundreds or thousands of different wavelengths. The ability to automatically generate summaries of these data sets enables several important applications, such as quickly browsing through a large image repository or determining the best use of a limited bandwidth link (e.g., determining which images are most critical for full transmission). Clustering algorithms can be used to generate these summaries, but traditional clustering methods make decisions based only on the information contained in the data set. In contrast, we present a new method that additionally leverages existing spectral libraries to identify materials that are likely to be present in the image target area. We find that this approach simultaneously reduces runtime and produces summaries that are more relevant to science goals.

Wagstaff, K. L.↗
NASA NTRS2006

Supervising Remote Humanoids Across Intermediate Time Delay

The President's Vision for Space Exploration, laid out in 2004, relies heavily upon robotic exploration of the lunar surface in early phases of the program. Prior to the arrival of astronauts on the lunar surface, these robots will be required to be controlled across space and time, posing a considerable challenge for traditional telepresence techniques. Because time delays will be measured in seconds, not minutes as is the case for Mars Exploration, uploading the plan for a day seems excessive. An approach for controlling humanoids under intermediate time delay is presented. This approach uses software running within a ground control cockpit to predict an immersed robot supervisor's motions which the remote humanoid autonomously executes. Initial results are presented.

Hambuchen, Kimberly↗
NASA NTRS2006

Towards Supervising Remote Dexterous Robots Across Time Delay

The President s Vision for Space Exploration, laid out in 2004, relies heavily upon robotic exploration of the lunar surface in early phases of the program. Prior to the arrival of astronauts on the lunar surface, these robots will be required to be controlled across space and time, posing a considerable challenge for traditional telepresence techniques. Because time delays will be measured in seconds, not minutes as is the case for Mars Exploration, uploading the plan for a day seems excessive. An approach for controlling dexterous robots under intermediate time delay is presented, in which software running within a ground control cockpit predicts the intention of an immersed robot supervisor, then the remote robot autonomously executes the supervisor s intended tasks. Initial results are presented.

Hambuchen, Kimberly↗
NASA NTRS2009

Self-Supervised Learning of Terrain Traversability from Proprioceptive Sensors

Robust and reliable autonomous navigation in unstructured, off-road terrain is a critical element in making unmanned ground vehicles a reality. Existing approaches tend to rely on evaluating the traversability of terrain based on fixed parameters obtained via testing in specific environments. This results in a system that handles the terrain well that it trained in, but is unable to process terrain outside its test parameters. An adaptive system does not take the place of training, but supplements it. Whereas training imprints certain environments, an adaptive system would imprint terrain elements and the interactions amongst them, and allow the vehicle to build a map of local elements using proprioceptive sensors. Such sensors can include velocity, wheel slippage, bumper hits, and accelerometers. Data obtained by the sensors can be compared to observations from ranging sensors such as cameras and LADAR (laser detection and ranging) in order to adapt to any kind of terrain. In this way, it could sample its surroundings not only to create a map of clear space, but also of what kind of space it is and its composition. By having a set of building blocks consisting of terrain features, a vehicle can adapt to terrain that it has never seen before, and thus be robust to a changing environment. New observations could be added to its library, enabling it to infer terrain types that it wasn't trained on. This would be very useful in alien environments, where many of the physical features are known, but some are not. For example, a seemingly flat, hard plain could actually be soft sand, and the vehicle would sense the sand and avoid it automatically.

Bajracharya, Max↗
NASA NTRS2011

Collaborative Supervised Learning for Sensor Networks

Collaboration methods for distributed machine-learning algorithms involve the specification of communication protocols for the learners, which can query other learners and/or broadcast their findings preemptively. Each learner incorporates information from its neighbors into its own training set, and they are thereby able to bootstrap each other to higher performance. Each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. After being seeded with an initial labeled training set, each learner proceeds to learn in an iterative fashion. New data is collected and classified. The learner can then either broadcast its most confident classifications for use by other learners, or can query neighbors for their classifications of its least confident items. As such, collaborative learning combines elements of both passive (broadcast) and active (query) learning. It also uses ideas from ensemble learning to combine the multiple responses to a given query into a single useful label. This approach has been evaluated against current non-collaborative alternatives, including training a single classifier and deploying it at all nodes with no further learning possible, and permitting learners to learn from their own most confident judgments, absent interaction with their neighbors. On several data sets, it has been consistently found that active collaboration is the best strategy for a distributed learner network. The main advantages include the ability for learning to take place autonomously by collaboration rather than by requiring intervention from an oracle (usually human), and also the ability to learn in a distributed environment, permitting decisions to be made in situ and to yield faster response time.

Wagstaff, Kiri L.↗
NASA NTRSDate not supplied

Analyzing Natural Language Context in Human-Machine Teaming using Supervised Machine Learning

Building a foundation for trustworthiness and trust verification in multi-asset teaming is the research challenge of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR). The Design Reference Mission (DRM) for ATTRACTOR is a search and rescue mission objective governed by a multi-member team consisting of human and machine operators. A crucial component to the effort is the communication between humans and autonomous agents throughout both planning and execution stages of the mission. Intuitive communication methods and modalities are posited as critical enablers for certifying trust and trustworthiness. This paper reports on the data collection and analysis conducted in support of the Human Informed Natural-language GANs Evaluation (HINGE)project to attain explainable and trusted communication between human-machine assets. Two identically curated image description datasets were acquired for HINGE, both consisting of two unique input modalities (typed vs. verbal) and retrieved in two distinct contexts (general vs. specific). The gathered datasets were assessed and compared using Parts-of-Speech (POS)features, sentence similarity metrics, and linguistic analysis. Then, the datasets were modeled and tested separately and in combination with one another using machine learning algorithms. The comparison and testing results reveal a superior dataset, by which a preferred context and input is understood, for generating image representations of missing persons using a Generative Adversarial Network (GAN).

Bryan A Barrows↗
NASA NTRS2022

A Generalized Approach to Aircraft Trajectory Prediction via Supervised Deep Learning

As research advances diverse forms and missions of aircraft, the National Airspace System (NAS) will become increasingly crowded, limiting current communications resources to accommodate aviation operations. Ongoing research proposes a paradigm of airspace communications, such that resources are autonomously and dynamically allocated via intelligent agents; this allocation requires accurate representations of the NAS, including the predicted positions of aircraft. State-of-the-art research emphasizes the importance of a hybrid-recurrent framework for trajectory prediction and compares the impact of commonly considered weather data on prediction accuracy. However, current research has been limited in its scope of efforts, frequently utilizing a unique flight route, architecture, set of weather data, and date range. This article considers the challenges of generalizing hybrid-recurrent predictive models for flight trajectories. Results illustrate an increase in error variance when identical models are trained over a generalized set of flights; this may be mitigated with careful tuning of hyperparameters, both in the network structure and optimization algorithms. Even so, an irreducible vertical error was identified, resulting from the complex takeoff and landing procedures which can not be correlated to functions of weather or additional assumptions of aircraft behavior. Finally, the use of a test route indicates that generalized models still do not possess sufficient knowledge for general aircraft predictions, with mean error increases ranging from 70-500%. These results illustrate the need for continued efforts on improving model versatility, as well as potential limitations for spectrum allocation near airports and other centers.

Nathan Schimpf↗
NASA NTRS2022

MLtool: Universal Supervised Machine Learning Tool to Model Tabulated Data

Machine Learning (ML) is a subfield of Artificial Intelligence that gives computers the ability to learn from past data without being explicitly programmed. The predictive capabilities of ML models have already been used to facilitate several scientific breakthroughs. However, the practical application of ML is often limited due to the gaps in technical knowledge of its users. The common issue faced by many scientific researchers is the inability to choose the appropriate ML pipelines that are needed to treat real-world data, which is often sparse and noisy. To solve this problem, we have developed an automated Machine Learning tool (MLtool) that includes a set of ML algorithms and approaches to aid scientific researchers. The current version of MLtool is implemented as an object-oriented Python code that is easily extensible. It includes 44 different regression algorithms used to model data. MLtool helps users select the best model for their data, based on the scoring metrics used. Besides regression algorithms, MLtool also includes a suite of pre- and post-processing techniques such as missing value imputation, categorical variable encoding, input feature normalization, uncertainty quantification, exploratory data analysis (EDA), etc. MLtool was tested on several publicly available multi-dimensional data sets and was found capable of making accurate predictions.

Machine learning↗
NASA NTRSDate not supplied

Deploying a Self-Supervised Learning Based Model to Search Events Across Space and Time

Motivation - Scientific Study of natural events, phenomena, or disasters require examples which span across time and space. - Machine Learning adaptation is on the rise, but there’s a lack of labeled training datasets that could be used to train or validate the models. - Best case scenario: - There’s an event database that tracks events available through time and space. - Provides all data associated with the events. - Real life scenario: - Some events are better tracked than others. - Scientists need to spend significant time identifying and gathering examples of events from different sources.

Iksha Gurung↗