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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Autonomous Satellite Command and Control through the World Wide Web: Phase 3

NASA's New Millenium Program (NMP) has identified a variety of revolutionary technologies that will support orders of magnitude improvements in the capabilities of spacecraft missions. This program's Autonomy team has focused on science and engineering automation technologies. In doing so, it has established a clear development roadmap specifying the experiments and demonstrations required to mature these technologies. The primary developmental thrusts of this roadmap are in the areas of remote agents, PI/operator interface, planning/scheduling fault management, and smart execution architectures. Phases 1 and 2 of the ASSET Project (previously known as the WebSat project) have focused on establishing World Wide Web-based commanding and telemetry services as an advanced means of interfacing a spacecraft system with the PI and operators. Current automated capabilities include Web-based command submission, limited contact scheduling, command list generation and transfer to the ground station, spacecraft support for demonstrations experiments, data transfer from the ground station back to the ASSET system, data archiving, and Web-based telemetry distribution. Phase 2 was finished in December 1996. During January-December 1997 work was commenced on Phase 3 of the ASSET Project. Phase 3 is the subject of this report. This phase permitted SSDL and its project partners to expand the ASSET system in a variety of ways. These added capabilities included the advancement of ground station capabilities, the adaptation of spacecraft on-board software, and the expansion of capabilities of the ASSET management algorithms. Specific goals of Phase 3 were: (1) Extend Web-based goal-level commanding for both the payload PI and the spacecraft engineer; (2) Support prioritized handling of multiple PIs as well as associated payload experimenters; (3) Expand the number and types of experiments supported by the ASSET system and its associated spacecraft; (4) Implement more advanced resource management, modeling and fault management capabilities that integrate the space and ground segments of the space system hardware; (5) Implement a beacon monitoring test; (6) Implement an experimental blackboard controller for space system management; (7) Further define typical ground station developments required for Internet-based remote control and for full system automation of the PI-to-spacecraft link. Each of those goals is examined in the next section. Significant sections of this report were also published as a conference paper.

Cantwell, Brian↗
NASA NTRS2003

Obtaining the Greatest Scientific Benefit from Observational Platforms by Consideration of the Relative Benefit of Observations

One of the current trends in spacecraft software design is to increase the autonomy of onboard flight and science software. This is especially true when real-time observations may affect the observation schedule of a mission. For many science missions, such as those conducted by the Swift Burst Alert Telescope, the ability of the spacecraft to autonomously respond in real-time to unpredicted science events is crucial for mission success. We apply utility theory within resource management middleware to optimize the real-time performance of application software and achieve maximum system level benefit. We then explore how this methodology can be extended to manage both software and observational resources onboard a spacecraft to achieve the best possible observations.

Chelberg, David↗
NASA NTRS2004

The autonomous sciencecraft experiment onboard the EO-1 spacecraft

The Autonomous Sciencecraft Experiment (ASE), currently flying onboard the Earth Observing-1 (EO-I) spacecraft, integrates several autonomy software technologies enabling autonomous 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.

Earth Observatory (EO)↗
NASA NTRS2006

Controlling Herds of Cooperative Robots

A document poses, and suggests a program of research for answering, questions of how to achieve autonomous operation of herds of cooperative robots to be used in exploration and/or colonization of remote planets. In a typical scenario, a flock of mobile sensory robots would be deployed in a previously unexplored region, one of the robots would be designated the leader, and the leader would issue commands to move the robots to different locations or aim sensors at different targets to maximize scientific return. It would be necessary to provide for this hierarchical, cooperative behavior even in the face of such unpredictable factors as terrain obstacles. A potential-fields approach is proposed as a theoretical basis for developing methods of autonomous command and guidance of a herd. A survival-of-the-fittest approach is suggested as a theoretical basis for selection, mutation, and adaptation of a description of (1) the body, joints, sensors, actuators, and control computer of each robot, and (2) the connectivity of each robot with the rest of the herd, such that the herd could be regarded as consisting of a set of artificial creatures that evolve to adapt to a previously unknown environment. A distributed simulation environment has been developed to test the proposed approaches in the Titan environment. One blimp guides three surface sondes via a potential field approach. The results of the simulation demonstrate that the method used for control is feasible, even if significant uncertainty exists in the dynamics and environmental models, and that the control architecture provides the autonomy needed to enable surface science data collection.

Quadrelli, Marco B.↗
NASA NTRS2004

Safe Agents in Space: Lessons from the Autonomous Sciencecraft Experiment

An Autonomous Science Agent is currently flying onboard the Earth Observing One Spacecraft. This software enables the spacecraft to autonomously detect and respond to science events occurring on the Earth. The package includes software systems that perform science data analysis, deliberative planning, and run-time robust execution. Because of the deployment to a remote spacecraft, this Autonomous Science Agent has stringent constraints of autonomy, reliability, and limited computing resources. We describe these constraints and how they are reflected in our agent architecture.

Eatth Observing One Spacecraft↗
NASA NTRS2004

The EO-1 Autonomous Science Agent Architecture

An Autonomous Science Agent is currently flying onboard the Earth Observing One Spacecraft. This software enables the spacecraft to autonomously detect and respond to science events occurring on the Earth. The package includes software systems that perform science data analysis, deliberative planning, and run-time robust execution. Because of the deployment to a remote spacecraft, this Autonomous Science Agent has stringent constraints of autonomy, reliability, and limited computing resources. We describe these constraints and how they are reflected in our agent architecture.

Earth Observing One (EO-1)↗
NASA NTRS2016

UAV Research at NASA Langley: Towards Safe, Reliable, and Autonomous Operations

Unmanned Aerial Vehicles (UAV) are fundamental components in several aspects of research at NASA Langley, such as flight dynamics, mission-driven airframe design, airspace integration demonstrations, atmospheric science projects, and more. In particular, NASA Langley Research Center (Langley) is using UAVs to develop and demonstrate innovative capabilities that meet the autonomy and robotics challenges that are anticipated in science, space exploration, and aeronautics. These capabilities will enable new NASA missions such as asteroid rendezvous and retrieval (ARRM), Mars exploration, in-situ resource utilization (ISRU), pollution measurements in historically inaccessible areas, and the integration of UAVs into our everyday lives all missions of increasing complexity, distance, pace, and/or accessibility. Building on decades of NASA experience and success in the design, fabrication, and integration of robust and reliable automated systems for space and aeronautics, Langley Autonomy Incubator seeks to bridge the gap between automation and autonomy by enabling safe autonomous operations via onboard sensing and perception systems in both data-rich and data-deprived environments. The Autonomy Incubator is focused on the challenge of mobility and manipulation in dynamic and unstructured environments by integrating technologies such as computer vision, visual odometry, real-time mapping, path planning, object detection and avoidance, object classification, adaptive control, sensor fusion, machine learning, and natural human-machine teaming. These technologies are implemented in an architectural framework developed in-house for easy integration and interoperability of cutting-edge hardware and software.

Davila, Carlos G.↗
NASA NTRS1998

Onboard Science Data Analysis: Opportunities, Benefits, and Effects on Mission Design

Much of the initial focus for spacecraft autonomy has been on developing new software and systems concepts to automate engineering functions of the spacecraft: guidance, navigation and control, fault protection, and resources management. However, the ultimate objectives of NASA missions are science objectives, which implies that we need a new framework for perfoming science data evaluation and observation planning autonomously onboard spacecraft.

spacecraft autonomy observation planning science d↗
NASA NTRS2005

Lunar and Planetary Science XXXVI, Part 4

Contents include the following: High-Resolution Electron Energy-Loss Spectroscopy (HREELS) Using a Monochromated TEM/STEM. Dynamical Evolution of Planets in Open Clusters. Experimental Petrology of the Basaltic Shergottite Yamato 980459: Implications for the Thermal Structure of the Martian Mantle. Cryogenic Reflectance Spectroscopy of Highly Hydrated Sulfur-bearing Salts. Implications for Core Formation of the Earth from High Pressure-Temperature Au Partitioning Experiments. Uranium-Thorium Cosmochronology. Protracted Core Differentiation in Asteroids from 182Hf-182W Systematics in the Eagle Station Pallasite. Maximizing Mission Science Return Through Use of Spacecraft Autonomy: Active Volcanism and the Autonomous Sciencecraft Experiment. Classification of Volcanic Eruptions on Io and Earth Using Low-Resolution Remote Sensing Data. Isotopic Mass Fractionation Laws and the Initial Solar System (sup26)Al/(sup27)Al Ratio. Catastrophic Disruption of Porous and Solid Ice Bodies (sup187)Re-(sup187)Os Isotope Disturbance in LaPaz Mare Basalt Meteorites. Comparative Petrology and Geochemistry of the LaPaz Mare Basalt Meteorites. A Comparison of the Structure and Bonding of Carbon in Apex Chert Kerogenous Material and Fischer-Tropsch-Type Carbons. Broad Spectrum Characterization of Returned Samples: Orientation Constraints of Small Samples on X-Ray and Other Spectroscopies. Apollo 14 High-Ti Picritic Glass: Oxidation/Reduction by Condensation of Alkali Metals. New Lunar Meteorites from Oman: Dhofar 925, 960 and 961. The First Six Months of Iapetus Observations by the Cassini ISS Camera. First Imaging Results from the Iapetus B/C Flyby of the Cassini Spacecraft. Radiative Transfer Calculations for the Atmosphere of Mars in the 200-900 nm Range. Geomorphologic Map of the Atlantis Basin, Terra Sirenum, Mars. The Meaning of Iron 60: A Nearby Supernova Injected Short-lived Radionuclides into Our Protoplanetary Disk.

Source record↗
NASA NTRS2008

Constellation Architecture Team-Lunar: Lunar Habitat Concepts

This paper will describe lunar habitat concepts that were defined as part of the Constellation Architecture Team-Lunar (CxAT-Lunar) in support of the Vision for Space Exploration. There are many challenges to designing lunar habitats such as mission objectives, launch packaging, lander capability, and risks. Surface habitats are required in support of sustaining human life to meet the mission objectives of lunar exploration, operations, and sustainability. Lunar surface operations consist of crew operations, mission operations, EVA operations, science operations, and logistics operations. Habitats are crewed pressurized vessels that include surface mission operations, science laboratories, living support capabilities, EVA support, logistics, and maintenance facilities. The challenge is to deliver, unload, and deploy self-contained habitats and laboratories to the lunar surface. The CxAT-Lunar surface campaign analysis focused on three primary trade sets of analysis. Trade set one (TS1) investigated sustaining a crew of four for six months with full outpost capability and the ability to perform long surface mission excursions using large mobility systems. Two basic habitat concepts of a hard metallic horizontal cylinder and a larger inflatable torus concept were investigated as options in response to the surface exploration architecture campaign analysis. Figure 1 and 2 depicts the notional outpost configurations for this trade set. Trade set two (TS2) investigated a mobile architecture approach with the campaign focused on early exploration using two small pressurized rovers and a mobile logistics support capability. This exploration concept will not be described in this paper. Trade set three (TS3) investigated delivery of a "core' habitation capability in support of an early outpost that would mature into the TS1 full outpost capability. Three core habitat concepts were defined for this campaign analysis. One with a four port core habitat, another with a 2 port core habitat, and the third investigated leveraging commonality of the lander ascent module and airlock pressure vessel hard shell. The paper will describe an overview of the various habitat concepts and their functionality. The Crew Operations area includes basic crew accommodations such as sleeping, eating, hygiene and stowage. The EVA Operations area includes additional EVA capability beyond the suit-port airlock function such as redundant airlock(s), suit maintenance, spares stowage, and suit stowage. The Logistics Operations area includes the enhanced accommodations for 180 days such as closed loop life support systems hardware, consumable stowage, spares stowage, interconnection to the other Hab units, and a common interface mechanism for future growth and mating to a pressurized rover. The Mission & Science Operations area includes enhanced outpost autonomy such as an IVA glove box, life support, and medical operations.

Toups, Larry↗
NASA NTRS2010

Constellation Architecture Team-Lunar Scenario 12.0 Habitation Overview

This paper will describe an overview of the Constellation Architecture Team Lunar Scenario 12.0 (LS-12) surface habitation approach and concept performed during the study definition. The Lunar Scenario 12 architecture study focused on two primary habitation approaches: a horizontally-oriented habitation module (LS-12.0) and a vertically-oriented habitation module (LS-12.1). This paper will provide an overview of the 12.0 lunar surface campaign, the associated outpost architecture, habitation functionality, concept description, system integration strategy, mass and power resource estimates. The Scenario 12 architecture resulted from combining three previous scenario attributes from Scenario 4 "Optimized Exploration", Scenario 5 "Fission Surface Power System" and Scenario 8 "Initial Extensive Mobility" into Scenario 12 along with an added emphasis on defining the excursion ConOps while the crew is away from the outpost location. This paper will describe an overview of the CxAT-Lunar Scenario 12.0 habitation concepts and their functionality. The Crew Operations area includes basic crew accommodations such as sleeping, eating, hygiene and stowage. The EVA Operations area includes additional EVA capability beyond the suitlock function such as suit maintenance, spares stowage, and suit stowage. The Logistics Operations area includes the enhanced accommodations for 180 days such as enhanced life support systems hardware, consumable stowage, spares stowage, interconnection to the other habitation elements, a common interface mechanism for future growth, and mating to a pressurized rover or Pressurized Logistics Module (PLM). The Mission & Science Operations area includes enhanced outpost autonomy such as an IVA glove box, life support, medical operations, and exercise equipment.

Kennedy, Kriss J.↗
NASA NTRS2004

Spacecraft Attitude Maneuver Planning Using Genetic Algorithms

A key enabling technology that leads to greater spacecraft autonomy is the capability to autonomously and optimally slew the spacecraft from and to different attitudes while operating under a number of celestial and dynamic constraints. The task of finding an attitude trajectory that meets all the constraints is a formidable one, in particular for orbiting or fly-by spacecraft where the constraints and initial and final conditions are of time-varying nature. This approach for attitude path planning makes full use of a priori constraint knowledge and is computationally tractable enough to be executed onboard a spacecraft. The approach is based on incorporating the constraints into a cost function and using a Genetic Algorithm to iteratively search for and optimize the solution. This results in a directed random search that explores a large part of the solution space while maintaining the knowledge of good solutions from iteration to iteration. A solution obtained this way may be used as is or as an initial solution to initialize additional deterministic optimization algorithms. A number of representative case examples for time-fixed and time-varying conditions yielded search times that are typically on the order of minutes, thus demonstrating the viability of this method. This approach is applicable to all deep space and planet Earth missions requiring greater spacecraft autonomy, and greatly facilitates navigation and science observation planning.

Kornfeld, Richard P.↗
NASA NTRSDate not supplied

Advancing Autonomy in Distributed Space Systems: Insights From on-Orbit Testing with the Starling 1.0 Mission

Autonomous decision-making is crucial for enhancing mission effectiveness in Distributed Space Systems (DSS), particularly in multi-spacecraft operations where communication constraints and mission complexity pose challenges. The Distributed Spacecraft Autonomy (DSA) team at NASA’s Ames Research Center is advancing autonomy in DSS through five key technical areas: distributed resource and task management, reactive operations, system modeling and simulation, human-swarm interaction, and ad hoc network communications. The DSA experiment onboard the Starling 1.0 Mission showcases collaborative resource allocation for multi-point science data collection with four small spacecraft. Autonomy in decision-making is highlighted as a crucial factor for multi-spacecraft missions, enabling spacecraft to operate independently, reducing reliance on ground control. This capability is particularly significant for future deep-space missions, where communication delays and limited data transmission capacity make traditional command and control approaches impractical. This demonstration focuses on a GPS Channel Selection Experiment, leveraging emergent capabilities like "shared sampling" and "simultaneous sampling" to optimize channel selection across the spacecraft swarm. The experiment aims to capture ionospheric phenomena such as the Equatorial Ionization Anomaly and Polar Patches. The DSA system's autonomous reconfiguration ability is showcased, emphasizing its adaptability to natural phenomena without significant integration efforts. The GPS Channel Selection Experiment utilizes a dual-band GPS receiver to estimate plasma density in the ionosphere. Explorative and exploitative channel selections are employed based on the nature of observed phenomena. The performance of DSA algorithms is evaluated in terms of optimal channel allocations and responsiveness to changes in observed features. The DSA Flight Software utilizes the Core Flight System (cFS) framework, ensuring compatibility with the Starling 1.0 flight mission software. DSA showcases results from RTI’s Connext DDS Micro communication middleware, enabling message routing over the Ad-Hoc Network of Starling 1.0. This paper provides a comprehensive overview of the DSA experiment's initial results, emphasizing the advancements in autonomy for Distributed Space Systems and the successful collaboration with the Starling 1.0 mission.

Caleb Ashmore Adams↗
NASA NTRS2008

Supporting Increased Autonomy for a Mars Rover

This paper presents an architecture and a set of technology for performing autonomous science and commanding for a planetary rover. The MER rovers have outperformed all expectations by lasting over 1100 sols (or Martian days), which is an order of magnitude longer than their original mission goal. The longevity of these vehicles will have significant effects on future mission goals, such as objectives for the Mars Science Laboratory rover mission (scheduled to fly in 2009) and the Astrobiology Field Lab rover mission (scheduled to potentially fly in 2016). Common objectives for future rover missions to Mars include the handling of opportunistic science, long-range or multi-sol driving, and onboard fault diagnosis and recovery. To handle these goals, a number of new technologies have been developed and integrated as part of the CLARAty architecture. CLARAty is a unified and reusable robotic architecture that was designed to simplify the integration, testing and maturation of robotic technologies for future missions. This paper focuses on technology comprising the CLARAty Decision Layer, which was designed to support and validate high-level autonomy technologies, such as automated planning and scheduling and onboard data analysis.

autonomous science↗
NASA NTRS2000

ISS Payload Operations: The Need for and Benefit of Responsive Planning

International Space Station (ISS) payload operations are controlled through implementation of a payload operations plan. This plan, which represents the defined approach to payload operations in general, can vary in terms of level of definition. The detailed plan provides the specific sequence and timing of each component of a payload's operations. Such an approach to planning was implemented in the Spacelab program. The responsive plan provides a flexible approach to payload operations through generalization. A responsive approach to planning was implemented in the NASA/Mir Phase 1 program, and was identified as a need during the Skylab program. The current approach to ISS payload operations planning and control tends toward detailed planning, rather than responsive planning. The use of detailed plans provides for the efficient use of limited resources onboard the ISS. It restricts flexibility in payload operations, which is inconsistent with the dynamic nature of the ISS science program, and it restricts crew desires for flexibility and autonomy. Also, detailed planning is manpower intensive. The development and implementation of a responsive plan provides for a more dynamic, more accommodating, and less manpower intensive approach to planning. The science program becomes more dynamic and responsive as the plan provides flexibility to accommodate real-time science accomplishments. Communications limitations and the crew desire for flexibility and autonomy in plan implementation are readily accommodated with responsive planning. Manpower efficiencies are accomplished through a reduction in requirements collection and coordination, plan development, and maintenance. Through examples and assessments, this paper identifies the need to transition from detailed to responsive plans for ISS payload operations. Examples depict specific characteristics of the plans. Assessments identify the following: the means by which responsive plans accommodate the dynamic nature of science programs and the crew desire for flexibility; the means by which responsive plans readily accommodate ISS communications constraints; manpower efficiencies to be achieved through use of responsive plans; and the implications of responsive planning relative to resource utilization efficiency.

Nahay, Ed↗