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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 1,099 records · Page 61

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NASA NTRS1989

Real-time edge tracking using a tactile sensor

Object recognition through the use of input from multiple sensors is an important aspect of an autonomous manipulation system. In tactile object recognition, it is necessary to determine the location and orientation of object edges and surfaces. A controller is proposed that utilizes a tactile sensor in the feedback loop of a manipulator to track along edges. In the control system, the data from the tactile sensor is first processed to find edges. The parameters of these edges are then used to generate a control signal to a hybrid controller. Theory is presented for tactile edge detection and an edge tracking controller. In addition, experimental verification of the edge tracking controller is presented.

Berger, Alan D.↗

NASA NTRS1991

Autonomous Mars ascent and orbit rendezvous for earth return missions

The details of tha assessment of autonomous Mars ascent and orbit rendezvous for earth return missions are presented. Analyses addressing navigation system assessments, trajectory planning, targeting approaches, flight control guidance strategies, and performance sensitivities are included. Tradeoffs in the analysis and design process are discussed.

Edwards, H. C.↗

NASA NTRS2000

Autonomous Performance Monitoring System: Monitoring and Self-Tuning (MAST)

Maintaining the long-term performance of software onboard a spacecraft can be a major factor in the cost of operations. In particular, the task of controlling and maintaining a future mission of distributed spacecraft will undoubtedly pose a great challenge, since the complexity of multiple spacecraft flying in formation grows rapidly as the number of spacecraft in the formation increases. Eventually, new approaches will be required in developing viable control systems that can handle the complexity of the data and that are flexible, reliable and efficient. In this paper we propose a methodology that aims to maintain the accuracy of flight software, while reducing the computational complexity of software tuning tasks. The proposed Monitoring and Self-Tuning (MAST) method consists of two parts: a flight software monitoring algorithm and a tuning algorithm. The dependency on the software being monitored is mostly contained in the monitoring process, while the tuning process is a generic algorithm independent of the detailed knowledge on the software. This architecture will enable MAST to be applicable to different onboard software controlling various dynamics of the spacecraft, such as attitude self-calibration, and formation control. An advantage of MAST over conventional techniques such as filter or batch least square is that the tuning algorithm uses machine learning approach to handle uncertainty in the problem domain, resulting in reducing over all computational complexity. The underlying concept of this technique is a reinforcement learning scheme based on cumulative probability generated by the historical performance of the system. The success of MAST will depend heavily on the reinforcement scheme used in the tuning algorithm, which guarantees the tuning solutions exist.

Peterson, Chariya↗

NASA NTRS2004

Autonomous Sciencecraft Experiment (ASE) Operations on EO-1 in 2004

The Autonomous Sciencecraft Experiment (ASE) has been selected for flight demonstration by NASAs New Millennium Program (NMP) as part of the Space Technology 6 (ST6) mission. NASA has identified the development of an autonomously operating spacecraft as a necessity for an expanded program of missions exploring the Solar System. The versatile ASE spacecraft command and control software, image formation software, and science processing software will be uploaded to the Earth Observer 1 (EO-1) spacecraft in early 2004 to detect surface modification related to volcanism, ice formation and retreat, and flooding.

Davies, A. G.↗

NASA NTRSDate not supplied

Mass Economy Evaluation for Integrated ECLSS and Propulsion Architecture

As missions in Low Earth Orbit (LEO) lengthen and extend to deep space, minimizing resupply needs becomes vital for sustaining crewed operations. Traditional life support systems depend on consumables resupplied from Earth, a method that is increasingly impractical for missions beyond LEO, such as lunar outposts or Mars transit. Long-duration missions require more efficient, autonomous systems that can recycle essential resources, particularly water and oxygen, to minimize the frequency and mass of resupply missions. The Environmental Control and Life Support System (ECLSS) is essential to such missions, with the International Space Station (ISS) serving as a testbed for advanced water recovery and partial oxygen recycling via physico-chemical methods. Yet, ECLSS and propulsion subsystems generally operate independently, despite overlapping requirements and potential areas for synergy. For instance, ECLSS byproducts, water, CO₂, and hydrogen, could be repurposed for propulsion, potentially reducing dedicated propellant mass and increasing overall system efficiency. One promising approach is to develop shared-resource architectures that integrate ECLSS with propulsion systems. This study examines the potential of such integration through the Sabatier CO₂ reduction process, focusing on water management as a key factor in system mass trade-offs. The Sabatier reaction produces water and methane from metabolic CO₂ and electrolytic hydrogen, partially closing the life support loop and providing methane, which could serve as a propellant. This integration could minimize waste, reduce resupply requirements, and enhance mission mass efficiency. A dynamic modeling framework will be used to simulate resource flows over long missions, capturing interactions between life support and propulsion. By comparing integrated versus separate system configurations, the study aims to quantify mass benefits and penalties, informing future habitat designs and trade studies for missions prioritizing autonomy and mass efficiency.

ECLSS↗

NASA NTRS2026

Mass Economy Evaluation for Integrated ECLSS and Propulsion Architecture

As missions in low Earth orbit (LEO) lengthen and extend to deep space, minimizing resupply needs becomes vital for sustaining crewed operations. Traditional life support systems depend on consumables resupplied from Earth, a method that is increasingly impractical for missions beyond LEO, such as lunar outposts or Mars transit. Long-duration missions require more efficient, autonomous systems that can recycle essential resources, particularly water and oxygen, to minimize the frequency and mass of resupply missions. The Environmental Control and Life Support System (ECLSS) is essential to such missions, with the International Space Station (ISS) serving as a testbed for advanced water recovery and partial oxygen recycling via physico-chemical methods. Yet, ECLSS and propulsion subsystems generally operate independently, despite overlapping requirements and potential areas for synergy. For instance, ECLSS byproducts, water, CO 2 , and hydrogen, could be repurposed for propulsion, potentially reducing dedicated propellant mass and increasing overall system efficiency. One promising approach is to develop shared-resource architectures that integrate ECLSS with propulsion systems. This study examines the potential of such integration through the Sabatier CO₂ reduction process, focusing on water management as a key factor in system mass trade-offs. The Sabatier reaction produces water and methane from metabolic CO 2 and electrolytic hydrogen, partially closing the life support loop and providing methane, which could serve as a propellant. This integration could minimize waste, reduce resupply requirements, and enhance mission mass efficiency. A dynamic modeling framework will be used to simulate resource flows over long missions, capturing interactions between life support and propulsion. By comparing integrated versus separate system configurations, the study aims to quantify mass benefits and penalties, informing future habitat designs and trade studies for missions prioritizing autonomy and mass efficiency.

ECLSS↗

DOE OSTI2016

Compact mobile-reader system for two-way wireless communication, tracking and status monitoring for transport safety and security

A system for monitoring a plurality radio frequency identification tags is described. The system uses at least one set of radio frequency identification tags. Each tag is attached to a container and includes several sensors for detecting physical conditions of said container. The system includes at least one autonomous intermediate reader in wireless communication with the frequency identification tags. The intermediate reader includes external wireless communication system, intermediate reader logic controller, and a self-contained rechargeable power supply. The system uses a central status reporting system in communication the intermediate reader.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2002

The nature of the autonomic dysfunction in multiple system atrophy

The concept that multiple system atrophy (MSA, Shy-Drager syndrome) is a disorder of the autonomic nervous system is several decades old. While there has been renewed interest in the movement disorder associated with MSA, two recent consensus statements confirm the centrality of the autonomic disorder to the diagnosis. Here, we reexamine the autonomic pathophysiology in MSA. Whereas MSA is often thought of as "autonomic failure", new evidence indicates substantial persistence of functioning sympathetic and parasympathetic nerves even in clinically advanced disease. These findings help explain some of the previously poorly understood features of MSA. Recognition that MSA entails persistent, constitutive autonomic tone requires a significant revision of our concepts of its diagnosis and therapy. We will review recent evidence bearing on autonomic tone in MSA and discuss their therapeutic implications, particularly in terms of the possible development of a bionic baroreflex for better control of blood pressure.

NASA Discipline Regulatory Physiology↗

DOE OSTI2025

Next-Generation Energy Technologies for Connected and Automated On-Road Vehicles (NEXTCAR) - Predictive Data-Driven Vehicle Dynamics and Powertrain Control: from ECU to the Cloud (Final Scientific/Technical Report)

This project developed and demonstrated a predictive, data-driven vehicle control system designed to improve energy efficiency and driving performance. The team created intelligent self-driving car technology that optimizes fuel and electricity use by proactively planning vehicle actions. By combining Level 4 autonomous driving capabilities with vehicle-to-everything (V2X) connectivity, the system enables vehicles to adjust speed and change lanes in response to traffic signals, surrounding vehicles, and road conditions, reducing unnecessary stops and delays. In testing, the system improved vehicle fuel economy by more than 30% and reduced travel time by approximately 10%, compared to a conventional adaptive cruise control baseline. These results demonstrate the technical effectiveness of using predictive, V2X-enabled strategies, such as traffic light timing and surrounding traffic awareness, to inform real-time vehicle powertrain control and driving behavior. Additionally, a supporting cloud platform was developed to provide dispatch and route recommendations as well as to log vehicle data, demonstrating the economic feasibility of this approach at the fleet level. By optimizing dispatching and routing operations, this technology enables electric fleet operators to use their vehicles more efficiently and reduce reliance on diesel backups, lowering both operating costs and energy consumption. Overall, this project’s technology advances the future of clean, energy-efficient transportation, enabling vehicles and fleets to reduce energy waste, cut costs, and lower emissions through intelligent automation and connectivity.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Development of a Discrepancy Checker for the Digital Twin in a Supervisory Control System for a Thermal Energy Delivery System

Defined as a virtual representation of a physical object, process, or service, and used to support real-world decision-making, a digital twin (DT) can be utilized to combine classical and novel frameworks in sensors, state predictions, and multi-input/multi-output systems, and to enable optimal autonomous operations. However, a DT’s usefulness largely depends on its ability to adequately mirror the state of its physical counterpart, and this adequacy should be reflected by the level of uncertainty in the underlying simulation models when estimating and predicting quantities of interest (QOIs). Moreover, simulation models in a DT may involve multiple fidelities of representations—ranging from physics-based models to data-driven ones—but classical uncertainty quantification (UQ) methods struggle to handle numerous uncertainty sources, nor are they designed for real-time applications. This work presents a UQ-based discrepancy checking and diagnosis tool for a DT-based supervisory control system applied to a thermal energy delivery system (TEDS) at Idaho National Laboratory. The discrepancy checker was developed using metadata from an automated DT development process, and these metadata included different combinations of physical model forms and model parameters, training data and hyperparameters for surrogate models, and design parameters for supervisory control systems. Next, correlations between the uncertainty results and the metadata were established and then applied to the DT operations. The discrepancy checker evaluates the discrepancies between model predictions from virtual and sensor measurements and backtraces them to the corresponding major sources of uncertainty. The discrepancy checker showed reasonable performance in detecting discrepancies and diagnosing sources of uncertainty in testing scenarios.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Forecasting of in situ electron energy loss spectroscopy

Abstract Forecasting models are a central part of many control systems, where high-consequence decisions must be made on long latency control variables. These models are particularly relevant for emerging artificial intelligence (AI)-guided instrumentation, in which prescriptive knowledge is needed to guide autonomous decision-making. Here we describe the implementation of a long short-term memory model (LSTM) for forecasting in situ electron energy loss spectroscopy (EELS) data, one of the richest analytical probes of materials and chemical systems. We describe key considerations for data collection, preprocessing, training, validation, and benchmarking, showing how this approach can yield powerful predictive insight into order-disorder phase transitions. Finally, we comment on how such a model may integrate with emerging AI-guided instrumentation for powerful high-speed experimentation.

36 MATERIALS SCIENCE↗

NASA NTRS1976

Automatic control of a mobile Viking lander on the surface of Mars

A mobile lander system is being considered for use in a possible follow-on mission to the Viking '75 landings on Mars. A mobile Viking lander, which could be launched as early as the 1979 opportunity, would be capable of traversing 100 m to 1 km per day on a commanded heading while sensing hazards and performing avoidance maneuvers. The degree of autonomous control, and consequently the daily traverse range, is still under study. The mobility concept requires the addition of: (1) track-laying or wheel units in place of the Viking Lander footpads, (2) a set of hazard and navigation sensors, and (3) a mobility control computer capability. The technology required to develop these three subsystems is available today. The principal objective of current design studies, as described in this paper, is to define a mobile lander system that will demonstrate high reliability and fail-safe hazard avoidance while achieving range- and terrain-handling capabilities which satisfy the Mars exploration science requirements.

Moore, J.↗

NASA NTRS1998

Vestibular influences on autonomic cardiovascular control in humans

There is substantial evidence that anatomical connections exist between vestibular and autonomic nuclei. Animal studies have shown functional interactions between the vestibular and autonomic systems. The nature of these interactions, however, is complex and has not been fully defined. Vestibular stimulation has been consistently found to reduce blood pressure in animals. Given the potential interaction between vestibular and autonomic pathways this finding could be explained by a reduction in sympathetic activity. However, rather than sympathetic inhibition, vestibular stimulation has consistently been shown to increase sympathetic outflow in cardiac and splanchnic vascular beds in most experimental models. Several clinical observations suggest that a link between vestibular and autonomic systems may also exist in humans. However, direct evidence for vestibular/autonomic interactions in humans is sparse. Motion sickness has been found to induce forearm vasodilation and reduce baroreflex gain, and head down neck flexion induces transient forearm and calf vasoconstriction. On the other hand, studies using optokinetic stimulation have found either very small, variable, or inconsistent changes in heart rate and blood pressure, despite substantial symptoms of motion sickness. Furthermore, caloric stimulation severe enough to produce nystagmus, dizziness, and nausea had no effect on sympathetic nerve activity measured directly with microneurography. No effect was observed on heart rate, blood pressure, or plasma norepinephrine. Several factors may explain the apparent discordance of these results, but more research is needed before we can define the potential importance of vestibular input to cardiovascular regulation and orthostatic tolerance in humans.

Review↗

NASA NTRS2001

Integrated Power Source Grant

Traditional spacecraft power systems incorporate a solar array energy source, an energy storage element (battery), and battery charge control and bus voltage regulation electronics to provide continuous electrical power for spacecraft systems and instruments. Dedicated power conditioning components provide limited fault isolation between systems and instruments, while a centralized power-switching unit provides spacecraft load control. Battery undervoltage conditions are detected by the spacecraft processor, which removes fault conditions and non-critical loads before permanent battery damage can occur. Cost effective operation of a micro-sat constellation requires a fault tolerant spacecraft architecture that minimizes on-orbit operational costs by permitting autonomous reconfiguration in response to unexpected fault conditions. A new micro-sat power system architecture that enhances spacecraft fault tolerance and improves power system survivability by continuously managing the battery charge and discharge processes on a cell-by-cell basis has been developed. This architecture is based on the Integrated Power Source (US patent 5644207), which integrates dual junction solar cells, Lithium Ion battery cells, and processor based charge control electronics into a structural panel that can be deployed or used to form a portion of the outer shell of a micro-spacecraft. The first generation Integrated Power Source is configured as a one inch thick panel in which prismatic Lithium Ion battery cells are arranged in a 3x7 matrix (26VDC) and a 3x1 matrix (3.7VDC) to provide the required output voltages and load currents. A multi-layer structure holds the battery cells, as well as the thermal insulators that are necessary to protect the Lithium Ion battery cells from the extreme temperatures of the solar cell layer. Independent thermal radiators, located on the back of the panel, are dedicated to the solar cell array, the electronics, and the battery cell array. In deployed panel applications, these radiators maintain the battery cells in an appropriate operational temperature range.

Source record↗

NASA NTRS1989

A scheme for fault tolerance in earth sensors

A system is presented that uses dual-redundant earth sensors to measure pitch and roll errors of a three-axis stabilized spacecraft, with provision for (1) autonomously detecting and identifying a faulty earth sensor, and (2) automatically selecting the outputs of the fault-free sensor for closed-loop attitude control, before failures cause major problems. A brief description is given of the system, and various failure modes of earth sensors and their effects are discussed. Novel techniques and algorithms for automatic fault detection, identification, and reconfiguration (FDIR) of dual-redundant earth sensors are developed. The algorithms are validated through computer simulations, and the results are presented. The proposed scheme can easily be implemented without much penalty on hardware, power consumption, and processing time.

Murugesan, S.↗

NASA NTRS1997

Managing Satellites

Integral Systems, Inc.'s EPOCH 2000 forms the core of NASA's Near Earth Asteroid Rendezvous (NEAR) mission's command and control center. EPOCH 2000, which allows ground operators to monitor and control satellites over a wide area network, owes part of its heritage from work completed to support Goddard Space Flight Center. The software automates telemetry processing, commanding, anomaly detection, and archiving collected data. The NEAR spacecraft, launched in February 1996, will rendezvous in early 1999 and orbit the Asteroid Eros for a year. Integral Systems also provided Low Earth Orbit Autonomous Ground Terminals (LEO-Ts) to NASA. The LEO-T is designed to make it easier and less expensive for principal investigators to obtain telemetry, tracking and control services for their science missions. The company products have supported well over 70 satellite missions aimed at scientific research, meteorology, or communications applications.

Source record↗

DOE OSTI2024

Data for: A Synoptic System for Capturing Ecosystem Control Points Across Terrestrial-Aquatic Interfaces

The investigation of how climate change and water level fluctuations impact variable and interconnected ecosystems, like the interfaces between terrestrial and aquatic environments, requires the collection and integration of many data types. We describe an integrative and autonomous environmental monitoring approach that uses environmental sensors and data loggers to monitor surface water, groundwater, soil, and vegetation changes and generate essential data for predictive models. We established the network at seven sites along the Chesapeake Bay and Lake Erie coastlines, including a large-scale flood manipulation experiment, collectively generating over three million observations per month. Such sensor networks hold great promise for tracking and comprehending environmental changes where land and water intersect. The sensor system and overall approach to sensor management that we have designed is intended to be widely accessible for research teams spanning in size from an individual investigator to large multi-institution projects. This dataset shows example data generated by the sensor network described above. Data output for data loggers connected to groundwater water quality sondes measuring dissolved oxygen, pH, oxidative redox potential (ORP), groundwater elevation, groundwater salinity and temperature, and groundwater elevation; replicate soil moisture and conductivity probes installed at 10 and 30 cm below the ground surface; rainfall and solar radiation; and mean sap flow from 8 replicate probes. Sensors are installed at upland forest (UP), wetlands (W), and transitional locations between these ecosystems (TR). Example datasets are from Goodwin Islands in the Chesapeake Bay and Portage River along the Lake Erie coastline.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility

Air transportation is undergoing a rapid evolution globally with the introduction of Advanced Air Mobility (AAM) and with it comes novel challenges and opportunities for transforming aviation. As AAM operations introduce increasing heterogeneity in vehicle capabilities and density, increased levels of automation are likely necessary to achieve operational safety and efficiency goals. This paper focuses on one example where increased automation has been suggested. Autonomous operations will need contingency management systems that can monitor evolving risk across a span of interrelated (or interdependent) hazards and, if necessary, execute appropriate control interventions via supervised or automated decision making. Accommodating this complex environment may require automated functions (autonomy) that apply artificial intelligence (AI) techniques that can adapt and respond to a quickly changing environment. This paper explores the use of Deep Reinforcement Learning (DRL) which has shown promising performance in complex and high-dimensional environments where the objective can be constructed as a sequential decision-making problem. An extension of a prior formulation of the contingency management problem as a Markov Decision Process (MDP) is presented and uses a DRL framework to train agents that mitigate hazards present in the simulation environment. A comparison of these learning-based agents and classical techniques is presented in terms of their performance, verification difficulties, and development process.

machine learningautonomous systems; flight simulat↗