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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Overview of Intelligent Power Controller Development for Human Deep Space Exploration

Intelligent or autonomous control of an entire spacecraft is a major technology that must be developed to enable NASA to meet its human exploration goals. NASA's current long term human space platform, the International Space Station, is in low earth orbit with almost continuous communication with the ground based mission control. This permits the near real-time control by the ground of all of the core systems including power. As NASA moves beyond Low Earth Orbit, the issues of communication time-lag and lack of communication bandwidth beyond geosynchronous orbit does not permit this type of operation. This paper presents the work currently ongoing at NASA to develop an architecture for an autonomous power control system as well as the effort to assemble that controller into the framework of the vehicle mission manager and other subsystem controllers to enable autonomous control of the complete spacecraft. Due to the common problems faced in both space power systems and terrestrial power system, the potential for spin-off applications of this technology for use in micro-grids located at the edge or user end of terrestrial power grids for peak power accommodation and reliability are described.

Space Flight↗
NASA NTRS2014

Overview of Intelligent Power Controller Development for Human Deep Space Exploration

Intelligent or autonomous control of an entire spacecraft is a major technology that must be developed to enable NASA to meet its human exploration goals. NASAs current long term human space platform, the International Space Station, is in low earth orbit with almost continuous communication with the ground based mission control. This permits the near real-time control by the ground of all of the core systems including power. As NASA moves beyond Low Earth Orbit, the issues of communication time-lag and lack of communication bandwidth beyond geosynchronous orbit does not permit this type of operation. This paper presents the work currently ongoing at NASA to develop an architecture for an autonomous power control system as well as the effort to assemble that controller into the framework of the vehicle mission manager and other subsystem controllers to enable autonomous control of the complete spacecraft. Due to the common problems faced in both space power systems and terrestrial power system, the potential for spin-off applications of this technology for use in micro-grids located at the edge or user end of terrestrial power grids for peak power accommodation and reliability are described.

Space Flight↗
NASA NTRS2014

Overview of Intelligent Power Controller Development for Human Deep Space Exploration

Intelligent or autonomous control of an entire spacecraft is a major technology that must be developed to enable NASA to meet its human exploration goals. NASA's current long term human space platform, the International Space Station, is in low Earth orbit with almost continuous communication with the ground based mission control. This permits the near real-time control by the ground of all of the core systems including power. As NASA moves beyond low Earth orbit, the issues of communication time-lag and lack of communication bandwidth beyond geosynchronous orbit does not permit this type of operation. This paper presents the work currently ongoing at NASA to develop an architecture for an autonomous power control system as well as the effort to assemble that controller into the framework of the vehicle mission manager and other subsystem controllers to enable autonomous control of the complete spacecraft. Due to the common problems faced in both space power systems and terrestrial power system, the potential for spin-off applications of this technology for use in micro-grids located at the edge or user end of terrestrial power grids for peak power accommodation and reliability are described.

Space Flight↗
NASA NTRS2010

Use of Business Intelligence Tools in the DSN

JPL has operated the Deep Space Network (DSN) on behalf of NASA since the 1960's. Over the last two decades, the DSN budget has generally declined in real-year dollars while the aging assets required more attention, and the missions became more complex. As a result, the DSN budget has been increasingly consumed by Operations and Maintenance (O&M), significantly reducing the funding wedge available for technology investment and for enhancing the DSN capability and capacity. Responding to this budget squeeze, the DSN launched an effort to improve the cost-efficiency of the O&M. In this paper we: elaborate on the methodology adopted to understand "where the time and money are used"-surprisingly, most of the data required for metrics development was readily available in existing databases-we have used commercial Business Intelligence (BI) tools to mine the databases and automatically extract the metrics (including trends) and distribute them weekly to interested parties; describe the DSN-specific effort to convert the intuitive understanding of "where the time is spent" into meaningful and actionable metrics that quantify use of resources, highlight candidate areas of improvement, and establish trends; and discuss the use of the BI-derived metrics-one of the most fascinating processes was the dramatic improvement in some areas of operations when the metrics were shared with the operators-the visibility of the metrics, and a self-induced competition, caused almost immediate improvement in some areas. While the near-term use of the metrics is to quantify the processes and track the improvement, these techniques will be just as useful in monitoring the process, e.g. as an input to a lean-six-sigma process.

Metrics↗
NASA NTRS2013

Robotic Technology Development at Ames: The Intelligent Robotics Group and Surface Telerobotics

Future human missions to the Moon, Mars, and other destinations offer many new opportunities for exploration. But, astronaut time will always be limited and some work will not be feasible for humans to do manually. Robots, however, can complement human explorers, performing work autonomously or under remote supervision from Earth. Since 2004, the Intelligent Robotics Group has been working to make human-robot interaction efficient and effective for space exploration. A central focus of our research has been to develop and field test robots that benefit human exploration. Our approach is inspired by lessons learned from the Mars Exploration Rovers, as well as human spaceflight programs, including Apollo, the Space Shuttle, and the International Space Station. We conduct applied research in computer vision, geospatial data systems, human-robot interaction, planetary mapping and robot software. In planning for future exploration missions, architecture and study teams have made numerous assumptions about how crew can be telepresent on a planetary surface by remotely operating surface robots from space (i.e. from a flight vehicle or deep space habitat). These assumptions include estimates of technology maturity, existing technology gaps, and likely operational and functional risks. These assumptions, however, are not grounded by actual experimental data. Moreover, no crew-controlled surface telerobotic system has yet been fully tested, or rigorously validated, through flight testing. During Summer 2013, we conducted a series of tests to examine how astronauts in the International Space Station (ISS) can remotely operate a planetary rover across short time delays. The tests simulated portions of a proposed human-robotic Lunar Waypoint mission, in which astronauts in lunar orbit remotely operate a planetary rover on the lunar Farside to deploy a radio telescope array. We used these tests to obtain baseline-engineering data.

robotics↗
NASA NTRS2016

Intelligent Monitoring of Rocket Test Systems

Stephanie Rocha is an undergraduate student pursuing a degree in Mechanical Engineering. Esteban Duran is pursuing a degree in Computer Science. Our mentor is Fernando Figueroa. Our project involved developing Intelligent Health Monitoring at the High Pressure Gas Facility (HPGF) utilizing the software GensymG2.

Duran, Esteban↗
NASA NTRS2016

Reconfigurable, Intelligently-Adaptive, Communication System, an SDR Platform

The Space Telecommunications Radio System (STRS) provides a common, consistent framework to abstract the application software from the radio platform hardware. STRS aims to reduce the cost and risk of using complex, configurable and reprogrammable radio systems across NASA missions. The NASA Glenn Research Center (GRC) team made a software defined radio (SDR) platform STRS compliant by adding an STRS operating environment and a field programmable gate array (FPGA) wrapper, capable of implementing each of the platforms interfaces, as well as a test waveform to exercise those interfaces. This effort serves to provide a framework toward waveform development onto an STRS compliant platform to support future space communication systems for advanced exploration missions. The use of validated STRS compliant applications provides tested code with extensive documentation to potentially reduce risk, cost and e ort in development of space-deployable SDRs. This paper discusses the advantages of STRS, the integration of STRS onto a Reconfigurable, Intelligently-Adaptive, Communication System (RIACS) SDR platform, and the test waveform and wrapper development e orts. The paper emphasizes the infusion of the STRS Architecture onto the RIACS platform for potential use in next generation flight system SDRs for advanced exploration missions.

space communications↗
NASA NTRS2016

Reconfigurable, Intelligently-Adaptive, Communication System, an SDR Platform

The Space Telecommunications Radio System (STRS) provides a common, consistent framework to abstract the application software from the radio platform hardware. STRS aims to reduce the cost and risk of using complex, configurable and reprogrammable radio systems across NASA missions. The Glenn Research Center (GRC) team made a software-defined radio (SDR) platform STRS compliant by adding an STRS operating environment and a field programmable gate array (FPGA) wrapper, capable of implementing each of the platforms interfaces, as well as a test waveform to exercise those interfaces. This effort serves to provide a framework toward waveform development on an STRS compliant platform to support future space communication systems for advanced exploration missions. Validated STRS compliant applications provided tested code with extensive documentation to potentially reduce risk, cost and efforts in development of space-deployable SDRs. This paper discusses the advantages of STRS, the integration of STRS onto a Reconfigurable, Intelligently-Adaptive, Communication System (RIACS) SDR platform, the sample waveform, and wrapper development efforts. The paper emphasizes the infusion of the STRS Architecture onto the RIACS platform for potential use in next generation SDRs for advance exploration missions.

space communications↗
NASA NTRS2017

The Importance of Earth Observations and Data Collaboration within Environmental Intelligence Supporting Arctic Research

Within the IARPC Collaboration Team activities of 2016, Arctic in-situ and remote earth observations advanced topics such as :1) exploring the role for new and innovative autonomous observing technologies in the Arctic; 2) advancing catalytic national and international community based observing efforts in support of the National Strategy for the Arctic Region; and 3) enhancing the use of discovery tools for observing system collaboration such as the U.S. National Oceanic and Atmospheric Administration (NOAA) Arctic Environmental Response Management Application (ERMA) and the U.S. National Aeronautics and Space Administration (NASA) Arctic Collaborative Environment (ACE) project geo reference visualization decision support and exploitation internet based tools. Critical to the success of these earth observations for both in-situ and remote systems is the emerging of new and innovative data collection technologies and comprehensive modeling as well as enhanced communications and cyber infrastructure capabilities which effectively assimilate and dissemination many environmental intelligence products in a timely manner. The Arctic Collaborative Environment (ACE) project is well positioned to greatly enhance user capabilities for accessing, organizing, visualizing, sharing and producing collaborative knowledge for the Arctic.

Casas, Joseph↗
NASA NTRS2017

Enabling Autonomous Space Mission Operations with Artificial Intelligence

For over 50 years, NASA's crewed missions have been confined to the Earth-Moon system, where speed-of-light communications delays between crew and ground are practically nonexistent. This ground-centered mode of operations, with a large, ground-based support team, is not sustainable for NASAs future human exploration missions to Mars. Future astronauts will need smarter tools employing Artificial Intelligence (AI) techniques make decisions without inefficient communication back and forth with ground-based mission control. In this talk we will describe several demonstrations of astronaut decision support tools using AI techniques as a foundation. These demonstrations show that astronauts tasks ranging from living and working to piloting can benefit from AI technology development.

Frank, Jeremy↗
NASA NTRS2017

Overview of Intelligent Power Controller Development for the Deep Space Gateway

Intelligent, or autonomous, control of a spacecraft is an enabling technology that must be developed for deep space human exploration. NASAs current long term human space platform, the International Space Station, which is in Low Earth Orbit, is in almost continuous communication with ground based mission control. This allows near real-time control of all the vehicle core systems, including power, to be controlled by the ground. As focus shifts from Low Earth Orbit, communication time-lag and communication bandwidth limitations beyond geosynchronous orbit does not permit this type of operation. This presentation contains ongoing work at NASA to develop an architecture for autonomous power control and the vehicle manager which monitors, coordinates, and delegates to all the on-board subsystems to enable autonomous control of the complete spacecraft.

power management↗
NASA NTRS2018

Artificial Intelligence - The Future of Space Communications

Presentation focus on the Cognitive Communications Project Overview. The project aims to develop cognitive communications technologies to increase mission science return and improve resource efficiencies. Another key aspect of the project is understanding the fundamental aspects of cognitive technology and developing artificial intelligence to advance the future of space communications. This involves the use of machine learning algorithms in the next generation architecture for space communications in the effort to increase efficiency, autonomy and increased performance of the space communication and navigations next generation architecture.

Briones, Janette C.↗
NASA NTRS2018

Real-Time Data Products and the Intelligent Payload Module

This presentation goes over the last 10 years of effort to prototype various aspects fo the HyspIRI Intelligent Payload Module (IPM). This was a mission requirement and various protoytpe efforts were conducted to bring risk down. The effort goes over various hardware and software protoytpe efforts.

high performance space computing↗
NASA NTRS2019

Predicting Satellite Close Approaches Using Statistical Parameters in the Context of Artificial Intelligence

In order to ensure a sustainable use of low earth orbit in particular and near Earth space in general, reliable and effective close approach prediction be-tween space objects is key. Only this allows for efficient and timely colli-sion avoidance. Space Situational Awareness (SSA) for commercial and government missions will be facing the rapidly growing amount of small and potentially less agile satellites as well as debris in the near earth realm, such as the increase in CubeSat launches and upcoming large constellations. At the same time, space object detection capabilities are expected to increase significantly, allowing for the reliable detection of smaller objects, e.g. when the Air Force Space Fence radar becomes operational. In combination, the space object catalog is expected to increase tremendously in size. In this paper, we introduce an investigative approach based on the latest capabili-ties in artificial intelligence in fostering the potential for fast and accurate close approach predictions. We consider the study of statistical and infor-mation theory parameters in contrast and complementary to the classical probability of collision computation alone, in order to determine the feasi-bility of reliably predicting close approaches.

Mashiku, A.↗
NASA NTRS2019

Predicting Satellite Close Approaches Using Statistical Parameters in the Context of Artificial Intelligence

In order to ensure a sustainable use of low earth orbit in particular and near Earth space in general, reliable and effective close approach prediction be-tween space objects is key. Only this allows for efficient and timely colli-sion avoidance. Space Situational Awareness (SSA) for commercial and government missions will be facing the rapidly growing amount of small and potentially less agile satellites as well as debris in the near earth realm, such as the increase in CubeSat launches and upcoming large constellations. At the same time, space object detection capabilities are expected to increase significantly, allowing for the reliable detection of smaller objects, e.g. when the Air Force Space Fence radar becomes operational. In combination, the space object catalog is expected to increase tremendously in size. In this paper, we introduce an investigative approach based on the latest capabili-ties in artificial intelligence in fostering the potential for fast and accurate close approach predictions. We consider the study of statistical and infor-mation theory parameters in contrast and complementary to the classical probability of collision computation alone, in order to determine the feasi-bility of reliably predicting close approaches.

Mashiku, Alinda↗
NASA NTRS2019

Artificial Intelligence: Powering Human Exploration of the Moon and Mars

Artificial Intelligence (AI) is a growing field of computa- tional science techniques designed to mimic functions per- formed by people. Advancements in autonomy will depend on a portfolio of AI technologies. Automated planning and scheduling is a venerable field of study in AI, and is needed for a variety of mission planning functions. Plan execution technology is less well studied, but important for auton- omy and robotics. Specialized forms of automated reason- ing and machine learning are key technologies to enable fault management. Over the past decade, the NASA Au- tonomous Systems and Operations (ASO) project has devel- oped and demonstrated numerous autonomy enabling tech- nologies employing AI techniques. Our work has employed AI in three distinct ways to enable autonomous mission op- erations capabilities. Crew Autonomy gives astronauts tools to assist in the performance of each of these mission oper-ations functions. Vehicle System Management uses AI tech- niques to turn the astronaut's spacecraft into a robot, allow- ing it to operate when astronauts are not present, or to reduce astronaut workload. AI technology also enables Autonomous Robots as crew assistants or proxies when the crew are not present. When these capabilities are used to enable astro- nauts to operate autonomously, they must be integrated with user interfaces, introducing numerous human factors con- siderations; when these capabilities are used to enable vehi- cle system management, they must be integrated with flight software, and run on embedded processors under the control of real-time operating systems.We first describe human spaceflight mission operations capabilities. The remainder of the paper will describe the ASO project, and the development and demonstration per- formed by ASO since 2011. We will describe the AI tech- niques behind each of these demonstrations, which include a variety of symbolic automated reasoning and machine learn- ing based approaches. Finally, we conclude with an assess- ment of future development needs for AI to enable NASA's future Exploration missions.

Mission Operations↗
NASA NTRS2019

Artificial Intelligence in Aviation Safety Applications - Exploring Myths and Truths of AI and ML

Artificial Intelligence (AI) and machine learning (ML) are gaining increased attention as ways to leverage the world's data to solve problems. Although AI and ML offer much potential, there are often misconceptions about the application of such techniques.Panel speakers will present machine learning approaches they have developed on a variety of aviation data, including digital flight data, safety reporting data, and voice communications data. They will discuss the purpose of the application, the data used, and the lessons learned in the development and deployment of their solutions. The panel will also discuss common pitfalls in developing an AI solution, the dangers of the current hype around AI, tips for gaining value from a ML solution, how to determine whether a ML approach is appropriate for a problem, and more.

Reeves, Scott (Capt.)↗
NASA NTRS2017

The Intelligent Landing System for Safe and Precise Landing on Europa

Europa, the smallest of Jupiter’s Galilean moons, is thought to harbor a vast liquid water ocean beneath its icy crust, making it one of the most scientifically intriguing targets for a robotic surface sampling mission in our Solar System. However, autonomously landing a spacecraft safely and precisely on Europa poses unique challenges, such as very little existing high-resolution reconnaissance imagery, a surface expected to be very rough and hazardous over a wide range of scales, an extremely intense ionizing radiation environment, and very limited lander resources for mass and volume. To address these challenges, we propose a novel Intelligent Landing System (ILS) combining four Guidance, Navigation & Control (GN&C) sensing functions – velocimetry, altimetry, map-relative localization, and hazard detection – that would together enable safe and precise landing on Europa’s surface. The ILS is a smart sensor system, combining an inertial measurement unit (IMU), a monocular, passive-optical camera, and a light detection and ranging (Li-DAR) sensor with dedicated computing resources as well as an onboard 3D terrain map. The ILS leverages more than a decade of technology development from programs such as the Lander Vision System, currently baselined on the Mars 2020 mission. This paper provides a detailed description of the proposed ILS architecture and concept of operations, as well as select preliminary simulation results to assess performance and robustness.

Trawny, Nikolas↗