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  • 1
    Publication Date: 2018-06-08
    Description: Paper maps are an important but unwieldy data format. To increase its utility, copious amounts of map data have been scanned into a digital map knowledge base. The next task in this knowledge base is to reduce this data to its underlying feature form suitable for analysis.
    Keywords: Cybernetics, Artificial Intelligence and Robotics
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  • 2
    Publication Date: 2018-06-08
    Description: Artificial neural networks, derived from their biological counterparts, offer a new and enabling computing paradigm specially suitable for such tasks as image and signal processing with feature classification/object recognition, global optimization, and adaptive control. When implemented in fully parallel electronic hardware, it offers orders of magnitude speed advantage. Basic building blocks of the new architecture are the processing elements called neurons implemented as nonlinear operational amplifiers with sigmoidal transfer function, interconnected through weighted connections called synapses implemented using circuitry for weight storage and multiply functions either in an analog, digital, or hybrid scheme.
    Keywords: Cybernetics, Artificial Intelligence and Robotics
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  • 3
    Publication Date: 2018-06-08
    Description: In mid-2003, we will fly software to detect science events that will drive autonomous scene selectionon board the New Millennium Earth Observing 1 (EO-1) spacecraft. This software will demonstrate the potential for future space missions to use onboard decision-making to detect science events and respond autonomously to capture short-lived science events and to downlink only the highest value science data.
    Keywords: Cybernetics, Artificial Intelligence and Robotics
    Type: International Symposium on Artificial Intelligence, Robotics, and Automation in Space; Nara; Japan
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  • 4
    Publication Date: 2019-07-13
    Description: Object-Relational database management system is an integrated hybrid cooperative approach to combine the best practices of both the relational model utilizing SQL queries and the object-oriented, semantic paradigm for supporting complex data creation. In this paper, a highly scalable, information on demand database framework, called NETMARK, is introduced. NETMARK takes advantages of the Oracle 8i object-relational database using physical addresses data types for very efficient keyword search of records spanning across both context and content. NETMARK was originally developed in early 2000 as a research and development prototype to solve the vast amounts of unstructured and semi-structured documents existing within NASA enterprises. Today, NETMARK is a flexible, high-throughput open database framework for managing, storing, and searching unstructured or semi-structured arbitrary hierarchal models, such as XML and HTML.
    Keywords: Cybernetics, Artificial Intelligence and Robotics
    Type: ISMIS 2003 Conference; Oct 28, 2003 - Oct 31, 2003; Maebashi; Japan
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  • 5
    Publication Date: 2019-07-13
    Description: We present ongoing work in the Autonomy Incubator at NASA Langley Research Center (LaRC) exploring the efficacy of a data set aggregation approach to reinforcement learning for small unmanned aerial vehicle (sUAV) flight in dense and cluttered environments with reactive obstacle avoidance. The goal is to learn an autonomous flight model using training experiences from a human piloting a sUAV around static obstacles. The training approach uses video data from a forward-facing camera that records the human pilot's flight. Various computer vision based features are extracted from the video relating to edge and gradient information. The recorded human-controlled inputs are used to train an autonomous control model that correlates the extracted feature vector to a yaw command. As part of the reinforcement learning approach, the autonomous control model is iteratively updated with feedback from a human agent who corrects undesired model output. This data driven approach to autonomous obstacle avoidance is explored for simulated forest environments furthering autonomous flight under the tree canopy research. This enables flight in previously inaccessible environments which are of interest to NASA researchers in Earth and Atmospheric sciences.
    Keywords: Cybernetics, Artificial Intelligence and Robotics
    Type: NF1676L-21547 , AIAA Aviation Technology, Integration, and Operations Conference; Jun 22, 2015 - Jun 26, 2015; Dallas, TX; United States
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  • 6
    Publication Date: 2019-07-13
    Description: Autonomous decision making in the presence of uncertainly is a deeply studied problem space particularly in the area of autonomous systems operations for land, air, sea, and space vehicles. Various techniques ranging from single algorithm solutions to complex ensemble classifier systems have been utilized in a research context in solving mission critical flight decisions. Realized systems on actual autonomous hardware, however, is a difficult systems integration problem, constituting a majority of applied robotics development timelines. The ability to reliably and repeatedly classify objects during a vehicles mission execution is vital for the vehicle to mitigate both static and dynamic environmental concerns such that the mission may be completed successfully and have the vehicle operate and return safely. In this paper, the Autonomy Incubator proposes and discusses an ensemble learning and recognition system planned for our autonomous framework, AEON, in selected domains, which fuse decision criteria, using prior experience on both the individual classifier layer and the ensemble layer to mitigate environmental uncertainty during operation.
    Keywords: Cybernetics, Artificial Intelligence and Robotics
    Type: NF1676L-20291 , AIAA Aviation Technology, Integration, and Operations Conference; Jun 22, 2015 - Jun 26, 2015; Dallas, TX; United States
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  • 7
    Publication Date: 2019-07-13
    Description: We present a demonstration of onboard hyperspectral image processing with the potential to reduce mission downlink requirements. The system detects spectral endmembers and then uses them to map units of surface material. This summarizes the content of the scene, reveals spectral anomalies warranting fast response, and reduces data volume by two orders of magnitude. We have integrated this system into the Autonomous Science craft Experiment for operational use onboard the Earth Observing One (EO-1) Spacecraft. The system does not require prior knowledge about spectra of interest. We report on a series of trial overflights in which identical spacecraft commands are effective for autonomous spectral discovery and mapping for varied target features, scenes and imaging conditions.
    Keywords: Cybernetics, Artificial Intelligence and Robotics
    Type: International Symposium on AI, Robotics and Automation in Space; Sep 04, 2012 - Sep 06, 2012; Turin; Italy
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  • 8
    Publication Date: 2019-07-13
    Description: With the anticipated increase of small unmanned aircraft systems (sUAS) entering into the National Airspace System, it is highly likely that vehicle operators will be teaming with fleets of small autonomous vehicles. The small vehicles may consist of sUAS, which are 55 pounds or less that typically will y at altitudes 400 feet and below, and small ground vehicles typically operating in buildings or defined small campuses. Typically, the vehicle operators are not concerned with manual control of the vehicle; instead they are concerned with the overall mission. In order for this vision of high-level mission operators working with fleets of vehicles to come to fruition, many human factors related challenges must be investigated and solved. First, the interface between the human operator and the autonomous agent must be at a level that the operator needs and the agents can understand. This paper details the natural language human factors e orts that NASA Langley's Autonomy Incubator is focusing on. In particular these e orts focus on allowing the operator to interact with the system using speech and gestures rather than a mouse and keyboard. With this ability of the system to understand both speech and gestures, operators not familiar with the vehicle dynamics will be able to easily plan, initiate, and change missions using a language familiar to them rather than having to learn and converse in the vehicle's language. This will foster better teaming between the operator and the autonomous agent which will help lower workload, increase situation awareness, and improve performance of the system as a whole.
    Keywords: Cybernetics, Artificial Intelligence and Robotics
    Type: NF1676L-20310 , AIAA Aviation Technology, Integration, and Operations Conference; Jun 22, 2015 - Jun 26, 2015; Dallas, TX; United States
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  • 9
    Publication Date: 2019-07-13
    Description: The Autonomous Sciencececraft Experiment (ASE), currently flying onboard the Earth Observing-1 (EO-1) spacecraft, integrates several autnomoy software technologies enabling autnomous science analysis and mission planning. The experiment demonstrates the potential for future space missions to use onboard decision-making to respond autonomously to capture short-lived science phenomena. The AAAI software demonstration will consist of two sections: a real-time display of an ASE-commanded ground contact from the EO-1 spacecraft, and a simulation of the full ASE autonomous science-response scenario.
    Keywords: Cybernetics, Artificial Intelligence and Robotics
    Type: 19th National Conference on Artificial Intelligence; Jul 23, 2004 - Jul 25, 2004; Menlo Park, CA; United States
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  • 10
    Publication Date: 2019-07-13
    Description: No abstract available
    Keywords: Cybernetics, Artificial Intelligence and Robotics
    Type: International Symposium on Artificial Intelligence, Robotics and Automation in Space i-SAIRAS; Sep 04, 2012 - Sep 06, 2012; Turin; Italy
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