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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Toward Agent Programs with Circuit Semantics

New ideas are presented for computing and organizing actions for autonomous agents in dynamic environments-environments in which the agent's current situation cannot always be accurately discerned and in which the effects of actions cannot always be reliably predicted. The notion of 'circuit semantics' for programs based on 'teleo-reactive trees' is introduced. Program execution builds a combinational circuit which receives sensory inputs and controls actions. These formalisms embody a high degree of inherent conditionality and thus yield programs that are suitably reactive to their environments. At the same time, the actions computed by the programs are guided by the overall goals of the agent. The paper also speculates about how programs using these ideas could be automatically generated by artificial intelligence planning systems and adapted by learning methods.

ELECTRICAL CIRCUITS↗
NASA NTRS2012

Acoustical and Intelligibility Test of the Vocera(Copyright) B3000 Communication Badge

To communicate with each other or ground support, crew members on board the International Space Station (ISS) currently use the Audio Terminal Units (ATU), which are located in each ISS module. However, to use the ATU, crew members must stop their current activity, travel to a panel, and speak into a wall-mounted microphone, or use either a handheld microphone or a Crew Communication Headset that is connected to a panel. These actions unnecessarily may increase task times, lower productivity, create cable management issues, and thus increase crew frustration. Therefore, the Habitability and Human Factors and Human Interface Branches at the NASA Johnson Space Center (JSC) are currently investigating a commercial-off-the-shelf (COTS) wireless communication system, Vocera(C), as a near-term solution for ISS communication. The objectives of the acoustics and intelligibility testing of this system were to answer the following questions: 1. How intelligibly can a human hear the transmitted message from a Vocera(c) badge in three different noise environments (Baseline = 20 dB, US Lab Module = 58 dB, Russian Module = 70.6 dB)? 2. How accurate is the Vocera(C) badge at recognizing voice commands in three different noise environments? 3. What body location (chest, upper arm, or shoulder) is optimal for speech intelligibility and voice recognition accuracy of the Vocera(C) badge on a human in three different noise environments?

Archer, Ronald↗
NASA NTRS1981

The enchanted loom

The evolution of intelligence began with the movement of Crossopterygian fish onto land. The eventual appearance of large dinosaurs eliminated all but the smallest of mammalian creatures, with the survivors forced to move only nocturnally, when enhanced olfactory and aural faculties were favored and involved a larger grey matter/body mass ratio than possessed by the dinosaurs. Additionally, the mammals made comparisons between the inputs of various senses, implying the presence of significant memory capacity and an ability to abstract survival information. More complex behavior occurred with the advent of tree dwellers (forward-looking eyes), hands, color vision, and the ability to grip and manipulate objects. An extra pound of brain evolved in the human skull in less than a million years. The neural processes that can lead to an action by a creature with a brain are mimicked by the basic AND and OR gates in computers, which are rapidly approaching the circuit density of the human brain. It is suggested that humans will eventually produce computers of higher intelligence than people possess, and computer spacecraft, alive in an electronic sense, will travel outward to explore the universe.

Jastrow, R.↗
NASA NTRSDate not supplied

Advancing Open Source Science Initiatives Through Public-Private Partnerships

Collaboration is fundamental to advancing open science within the science community. With the recent developments in technology and research, the establishment of formal partnerships between the private sector and government agencies are needed to bridge the knowledge gaps and expedite the time to actionable science. NASA’s Interagency Implementation and Advanced Concepts Team (IMPACT) seeks to address this challenge by establishing non-reimbursable Space Act Agreements with industry leaders in cloud computing, artificial intelligence (AI) and machine learning. The purpose of these agreements is to advance open source science initiatives in the areas of data discovery, access and use of high value NASA science data sets on the cloud. As well as, jointly work on common research problems to accelerate the development and adoption of new AI technologies. Current success stories include co-locating NASA datasets from multiple science disciplines on one platform using Amazon Web Services Open Data Registry, developing AI Foundation Models for Science with IBM and co-hosting training workshops and tutorials for the science community aimed at providing hands-on experience with using NASA data and AI models on the cloud. In summary, we will present an overview of our partnerships supporting open source science initiatives, describe current activities and lessons learned that may be useful to others considering similar partnerships with the private sector.

Elizabeth Fancher↗
NASA NTRSDate not supplied

Space ROS TOFU: Flying Space ROS with Containerized Hybrid Trust

Space ROS is a distribution of Robot Operating System 2 (ROS2) targeting the specific requirements of flight software and spaceborne robotics while maintaining the flexibility that has made ROS indispensible for robotics research and industrial system integration. With Space ROS, a project can leverage the existing ROS2 ecosystem to reduce redundant development while tackling increasingly complex demands for on-device intelligence; however, there is no substitute for flight heritage to combat the risk-aversion common to spaceflight projects, and Space ROS has yet to fly. To break the collective-action standoff and gain valuable flight experience, the Distributed Spacecraft Autonomy (DSA) team at NASA Ames Research Center developed Opportunistic Software Experiments for Spacecraft Autonomy Testbeds (OSE-SAT) architecture, utilizing containerization to execute lower-trust software under traditionally verified heritage flight software for demonstration on-orbit. OSE-SAT leverages a hybrid-trust model where flexible, complex components like Space ROS can run without risk to the host spacecraft while providing validated feedback to a highly scrutinized, trusted core. We leverage this testbed to demonstrate Space ROS in flight, building heritage, and experience for projects with "Trust On First Use" (TOFU) requirements for flight heritage. We present a Space ROS component for OSE-SAT, lessons learned integrating Space ROS into a flight software stack, and the results of the first known use of Space ROS in orbit.

small satellites↗
DOE OSTI2022

Data Architecture and Analytics Requirements for Artificial Intelligence and Machine Learning Applications to Achieve Condition-Based Maintenance

This report identified some of the important requirements that needs to be taken into consideration as part of the data evolution for the CBM application of a CWS in a NPP. In the data evolution process, the information is converted into insight leading into actions using advancements in AI/ML technologies. A notion of RESET AI: design, development, deployment, and operation principals are introduced to lifecycle of AI technologies. Towards the end of the report, we discussed how this CBM can be realized in a SDE. As path forward, this report lays the foundation for developing a more detailed industry guidance supporting data evolution for other plant applications like operations and plant support. These would be developed as part of ongoing research in the fiscal year 2023.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2014

Ground Operations Autonomous Control and Integrated Health Management

An intelligent autonomous control capability has been developed and is currently being validated in ground cryogenic fluid management operations. The capability embodies a physical architecture consistent with typical launch infrastructure and control systems, augmented by a higher level autonomous control (AC) system enabled to make knowledge-based decisions. The AC system is supported by an integrated system health management (ISHM) capability that detects anomalies, diagnoses causes, determines effects, and could predict future anomalies. AC is implemented using the concept of programmed sequences that could be considered to be building blocks of more generic mission plans. A sequence is a series of steps, and each executes actions once conditions for the step are met (e.g. desired temperatures or fluid state are achieved). For autonomous capability, conditions must consider also health management outcomes, as they will determine whether or not an action is executed, or how an action may be executed, or if an alternative action is executed instead. Aside from health, higher level objectives can also drive how a mission is carried out. The capability was developed using the G2 software environment (www.gensym.com) augmented by a NASA Toolkit that significantly shortens time to deployment. G2 is a commercial product to develop intelligent applications. It is fully object oriented. The core of the capability is a Domain Model of the system where all elements of the system are represented as objects (sensors, instruments, components, pipes, etc.). Reasoning and decision making can be done with all elements in the domain model. The toolkit also enables implementation of failure modes and effects analysis (FMEA), which are represented as root cause trees. FMEA's are programmed graphically, they are reusable, as they address generic FMEA referring to classes of subsystems or objects and their functional relationships. User interfaces for integrated awareness by operators have been created.

Figueroa, Fernando↗
NASA NTRS2005

Model Checking Artificial Intelligence Based Planners: Even the Best Laid Plans Must Be Verified

Automated planning systems (APS) are gaining acceptance for use on NASA missions as evidenced by APS flown On missions such as Orbiter and Deep Space 1 both of which were commanded by onboard planning systems. The planning system takes high level goals and expands them onboard into a detailed of action fiat the spacecraft executes. The system must be verified to ensure that the automatically generated plans achieve the goals as expected and do not generate actions that would harm the spacecraft or mission. These systems are typically tested using empirical methods. Formal methods, such as model checking, offer exhaustive or measurable test coverage which leads to much greater confidence in correctness. This paper describes a formal method based on the SPIN model checker. This method guarantees that possible plans meet certain desirable properties. We express the input model in Promela, the language of SPIN and express the properties of desirable plans formally.

model checking↗
DOE OSTI2019

Decentralized Signal Processing and Distributed Control of Collaborative Autonomous Networks (17-ERD-101 LDRD Final Report)

Intelligent autonomous sensor networks, often comprised of large numbers of sensors, must be capable of jointly exploiting data collected at each agent in the network, and using that data to optimize their future actions towards multiple mission objectives. Centralized signal processing and optimization solutions process all data and determine all future actions at a single agent, and the resulting information and commands are disseminated back to the network. The communications bandwidth this requires and the single point of failure the central agent represents often make these solutions untenable for national security applications. In this project, several fundamental algorithms for solving both the decentralized signal processing and network optimization were developed, as well as simulation software to validate the results of these algorithms at scale. Specifically, novel algorithms for Bayesian decentralized estimation and decentralized detection and optimization based on the alternating direction method of multipliers (ADMM) were developed for autonomous sensor networks and published in the literature. The first large scale simulation of autonomous sensor networks (1000 agents) was conducted on this project, validating the performance of the developed algorithms. These algorithms and simulation tools are critical components of any decentralized autonomous network and have current and future national security applications, including distributed sensor networks for detection, estimation, and tracking problems, and large decentralized cyber-physical infrastructure such as the power grid.

42 ENGINEERING↗
DOE OSTI2024

Artificial Intelligence for Climate Change Mitigation Roadmap (Second Edition)

The first ICEF Artificial Intelligence for Climate Change Mitigation Roadmap was released in December 2023. Since that time, attention to artificial intelligence (AI) has continued to grow at a rapid pace. Tens of billions of dollars have poured into AI projects, policymakers around the world have considered new AI policies, and OpenAI reports that each month more than 200 million people now use ChatGPT. Signs of a changing climate continue to grow as well. Based on global average temperatures, July 22, 2024 was the warmest day ever recorded; 2023 was the warmest year ever recorded; and the 10 warmest years on record are the past 10 years. Yet global emissions of greenhouse gases continue to climb. Can AI help cut emissions of greenhouse gases? This Roadmap explores that question. In this second edition of the Artificial Intelligence for Climate Change Mitigation Roadmap, a team of 25 co-authors builds on last year’s roadmap—comprehensively updating all old chapters, adding six new chapters and offering 5–10 specific, actionable recommendations in each chapter. Our goal is to provide a useful resource for experts and non-experts alike. In Part I of this Roadmap, we provide brief introductions to both AI and climate change. In Part II, we explore eight sectors in which AI is helping respond to climate change and could do much more. In Part III, we explore crosscutting issues. We close with findings and recommendations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

Intent inferencing with a model-based operator's associate

A portion of the Operator Function Model Expert System (OFMspert) research project is described. OFMspert is an architecture for an intelligent operator's associate or assistant that can aid the human operator of a complex, dynamic system. Intelligent aiding requires both understanding and control. The understanding (i.e., intent inferencing) ability of the operator's associate is discussed. Understanding or intent inferencing requires a model of the human operator; the usefulness of an intelligent aid depends directly on the fidelity and completeness of its underlying model. The model chosen for this research is the operator function model (OFM). The OFM represents operator functions, subfunctions, tasks, and actions as a heterarchic-hierarchic network of finite state automata, where the arcs in the network are system triggering events. The OFM provides the structure for intent inferencing in that operator functions and subfunctions correspond to likely operator goals and plans. A blackboard system similar to that of Human Associative Processor (HASP) is proposed as the implementation of intent inferencing function. This system postulates operator intentions based on current system state and attempts to interpret observed operator actions in light of these hypothesized intentions.

Jones, Patricia M.↗
NASA NTRS1999

An Evolvable Multi-Agent Approach to Space Operations Engineering

A complex system of spacecraft and ground tracking stations, as well as a constellation of satellites or spacecraft, has to be able to reliably withstand sudden environment changes, resource fluctuations, dynamic resource configuration, limited communication bandwidth, etc., while maintaining the consistency of the system as a whole. It is not known in advance when a change in the environment might occur or when a particular exchange will happen. A higher degree of sophistication for the communication mechanisms between different parts of the system is required. The actual behavior has to be determined while the system is performing and the course of action can be decided at the individual level. Under such circumstances, the solution will highly benefit from increased on-board and on the ground adaptability and autonomy. An evolvable architecture based on intelligent agents that communicate and cooperate with each other can offer advantages in this direction. This paper presents an architecture of an evolvable agent-based system (software and software/hardware hybrids) as well as some plans for further implementation.

Mandutianu, Sanda↗
NASA NTRS1994

Deciding When and How to Learn

Learning is an important aspect of intelligent behavior. Unfortunately, learning rarely comes for free.

machine learning powerful learning approaches comp↗
NASA NTRSDate not supplied

Earth System Digital Twins (ESDT) Technology for NASA Earth Science

For NASA's Advanced Information Systems Technology (AIST) Program, an Earth System Digital Twin (ESDT) is defined as an interactive and integrated multidomain, multiscale, digital replica of the state and temporal evolution of Earth systems. It dynamically integrates: relevant Earth system models and simulations; other relevant models (e.g., related to the world's infrastructure); continuous and timely (including near real time and direct readout) observations (e.g., space, air, ground, over/underwater, Internet of Things (IoT), socioeconomic); long-time records; as well as analytics and artificial intelligence tools. Effective ESDTs enable users to run hypothetical scenarios to improve the understanding, prediction of and mitigation/response to Earth system processes, natural phenomena and human activities as well as their many interactions. An ESDT is a type of integrated information system that, for example, enables continuous assessment of impact from naturally occurring and/or human activities on physical and natural environments. AIST ESDT strategic goals are to: 1. Develop information system frameworks to provide continuous and accurate representations of systems as they change over time; 2. Mirror various Earth Science systems and utilize the combination of Data Analytics, Artificial Intelligence, Digital Thread, and state-of-the-art models to help predict the Earth’s response to various phenomena; 3. Provide the tools to conduct "what if" investigations that can result in actionable predictions. The AIST ESDT thrust is developing capabilities toward the development of future digital twins of the Earth or of subcomponents of the Earth. This will enable the development of an overarching framework that will integrate New Observing Strategies (NOS) to enable new observation measurements, i.e., multi-source, coordinated, dynamic and responsive to needs and requests defined by Analytic Collaborative Frameworks (ACF) that enable agile science investigations fusing and analyzing very large amounts of diverse data. NOS and ACF capabilities along with open access to various science, infrastructure and human data, interconnected modeling, data assimilation, simulations, surrogate modeling, high-performance computing and advanced visualization, will define a powerful framework that could be utilized for local, regional or global and/or thematic digital twins. This presentation will describe a general overview of the AIST ESDT vision including prior work done in the areas of NOS and ACF as well as current and upcoming ESDT projects.

Jacqueline Le Moigne↗
NASA NTRS1988

Integration of task level planning and diagnosis for an intelligent robot

The use of robots in the future must go beyond present applications and will depend on the ability of a robot to adapt to a changing environment and to deal with unexpected scenarios (i.e., picking up parts that are not exactly where they were expected to be). The objective of this research is to demonstrate the feasibility of incorporating high level planning into a robot enabling it to deal with anomalous situations in order to minimize the need for constant human instruction. The heuristics can be used by a robot to apply information about previous actions towards accomplishing future objectives more efficiently. The system uses a decision network that represents the plan for accomplishing a task. This enables the robot to modify its plan based on results of previous actions. The system serves as a method for minimizing the need for constant human instruction in telerobotics. This paper describes the integration of expert systems and simulation as a valuable tool that goes far beyond this project. Simulation can be expected to be used increasingly as both hardware and software improve. Similarly, the ability to merge an expert system with simulation means that we can add intelligence to the system. A malfunctioning space satellite is described. The expert system uses a series of heuristics in order to guide the robot to the proper location. This is part of task level planning. The final part of the paper suggests directions for future research. Having shown the feasibility of an expert system embedded in a simulation, the paper then discusses how the system can be integrated with the MSFC graphics system.

Gerstenfeld, Arthur↗
DOE OSTI2020

Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systems

This paper develops a model-free volt-VAR optimization (VVO) algorithm via multi-agent deep reinforcement learning (DRL) in unbalanced distribution systems. This method is novel since we cast the VVO problem in distribution networks to an intelligent deep Q-network (DQN) framework, which avoids solving a specific optimization model directly when facing time-varying operating conditions in the systems. We consider statuses/ratios of switchable capacitors, voltage regulators, and smart inverters installed at distributed generators as the action variables of the agents. A delicately designed reward function guides these agents to interact with the distribution system, in the direction of reinforcing voltage regulation and power loss reduction simultaneously. The forward-backward sweep method for radial three-phase distribution systems provides accurate power flow results within a few iterations to the DRL environment. The proposed method realizes the dual goals for VVO. We test this algorithm on the unbalanced IEEE 13-bus and 123-bus systems. Numerical simulations validate the excellent performance of this method in voltage regulation and power loss reduction.

41 EE - Solar Energy Technologies Office (EE-4S)↗
NASA NTRS1985

Deviser - An AI planner for spacecraft operations

The 'Deviser III' automated spacecraft mission planner prototype can plan complete Voyager spacecraft operational sequences consisting of over 100 data capture goals, working 10-50 times faster than a human analyst. The planner generates a loop-free network of actions and events resembling a program evaluation and review technique chart. Actions, events, and inferences are the building blocks from which plans are assembled. It is noted that, for a very large spacecraft mission plan, the backtracking through remembered decision trees entailed by this artificial intelligence system can consume megabite quantities of computer memory.

Vere, S. A.↗