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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

General Temporal Knowledge for Planning and Data Mining

We consider the architecture of systems that combine temporal planning and plan execution and introduce a layer of temporal reasoning that potential1y improves both the communication between humans and such systems, and the performance of the temporal planner itself. In particular, this additional layer simultaneously supports more flexibility in specifying and maintaining temporal constraints on plans within an uncertain and changing execution environment, and the ability to understand and trace the progress of plan execution. It is shown how a representation based on single set of abstractions of temporal information can be used to characterize the reasoning underlying plan generation and execution interpretation. The complexity of such reasoning is discussed.

Morris, Robert↗
NASA NTRS1987

An integrated approach to space station power system autonomous control

Space Station electrical power management must be accomplished autonomously in order to decrease both airborne and ground support costs. Attention is presently given to the augmentation of terrestrial utility algorithmic decision aids for power dispatching for space station use, using expert systems to direct power demand analyses and the integration of results into operational decisions. Functions to be thus managed encompass power scheduling, energy allocation, failure cause diagnoses, goal proposal and plan preparation, consequence evaluation, and execution plan selection. The operating states of the system are normal, preventive, emergency, and restorative.

Dolce, James L.↗
NASA NTRS2015

Geologic Investigations Spurred by Analog Testing at the 7504 Cone-SP Mountain Area of the San Francisco Volcanic Field

The SP Mountain area of the San Francisco Volcanic Field, AZ, has been used as an analog mission development site for NASA since 1998. This area consists of basaltic cinder cones, lava flows and maar craters that have been active since mid-Miocene, with the youngest events occurring within the last 10,000 years. The area has been used because its geologic and topographic resemblance to lunar and Martian terrains provides an ideal venue for testing hardware and science operations practices that might be employed on planetary surfaces, as well as training astronauts in field geology. Analog operations have often led to insights that spurred new scientific investigations. Most recently, an investigation of the 7504 cone was initiated due to perceptions that Apollo-style traverse plans executed during the Desert RATS 2010 mission had characterized the area incorrectly, leading to concerns that the Apollo traverse planning process was scientifically flawed. This investigation revealed a complex history of fissure eruptions of lava and cinders, cinder cone development, a cone-fill-and-spill episode, extensive rheomorphic lava flow initiation and emplacement, and cone sector collapse that led to a final lava flow. This history was not discernible on pre-RATS mission photogeology, although independent analysis of RATS 2010 data and samples develped a "75% complete solution" that validated the pre-RATS mission planning and Apollo traverse planning and execution. The study also pointed out that the development of scientific knowledge with time in a given field area is not linear, but may follow a functional form that rises steeply in the early period of an investigation but flattens out in the later period, asymptotically approaching a theoretical "complete knowledge" point that probably cannot be achieved. This implies that future human missions must be prepared to shift geographic areas of investigation regularly if significant science returns are to be forthcoming.

Eppler, Dean B.↗
NASA NTRS1997

Replanning Using Hierarchical Task Network and Operator-Based Planning

In order to scale-up to real-world problems, planning systems must be able to replan in order to deal with changes in problem context. In this paper we describe hierarchical task network and operatorbased re-planning techniques which allow adaptation of a previous plan to account for problems associated with executing plans in real-world domains with uncertainty, concurrency, changing objectives.

HTN/operator-based planning↗
NASA NTRS2004

From hybrid systems to universal plans via domain compilation

While universal plans tell a system how to reach a goal regardless of what state it is in, such plans are typically too large to represent. Hybrid systems execute plans where each action is implemented to robustly produce effects if the world does not stray outside the action's control envelopes.

embedded real-time planning↗
DOE OSTI2025

Osprey Framework v0.2.2

The Alpha Berkeley Framework is a software architecture for building agentic AI systems that coordinate multi-step workflows in scientific and industrial environments. It is based on a plan-first orchestration model, where natural language requests are translated into execution plans with explicit dependencies and optional human approval. The framework includes capability classification, which selects relevant tools on a per-task basis to keep orchestration efficient as the number of available tools grows. It incorporates task extraction methods that compress conversational context and integrate external resources such as databases, APIs, and knowledge bases into structured, machine-readable tasks. Execution is supported by modular services with checkpointing, artifact management, and error handling, allowing workflows to be paused, inspected, and resumed. The system is designed for deployment in production environments, supporting both local and containerized execution as well as integration with HPC clusters. Interfaces include command-line tools, browser-based workflows, and containerized services. The framework has been demonstrated in tutorial examples and deployed at the Advanced Light Source, where it coordinates accelerator control and analysis workflows.

Hellert, Thorsten [Lawrence Berkeley National Labo↗
NASA NTRS2004

Coordinating Shared Activities

Shared Activity Coordination (ShAC) is a computer program for planning and scheduling the activities of an autonomous team of interacting spacecraft and exploratory robots. ShAC could also be adapted to such terrestrial uses as helping multiple factory managers work toward competing goals while sharing such common resources as floor space, raw materials, and transports. ShAC iteratively invokes the Continuous Activity Scheduling Planning Execution and Replanning (CASPER) program to replan and propagate changes to other planning programs in an effort to resolve conflicts. A domain-expert specifies which activities and parameters thereof are shared and reports the expected conditions and effects of these activities on the environment. By specifying these conditions and effects differently for each planning program, the domain-expert subprogram defines roles that each spacecraft plays in a coordinated activity. The domain-expert subprogram also specifies which planning program has scheduling control over each shared activity. ShAC enables sharing of information, consensus over the scheduling of collaborative activities, and distributed conflict resolution. As the other planning programs incorporate new goals and alter their schedules in the changing environment, ShAC continually coordinates to respond to unexpected events.

Clement, Bradley↗
NASA NTRS2010

Evolving from Planning and Scheduling to Real-Time Operations Support: Design Challenges

Versions of Scheduling and Planning Interface for Exploration (SPIFe) have supported a variety of mission operations across NASA. This software tool has evolved and matured over several years, assisting planners who develop intricate schedules. While initially conceived for surface Mars missions, SPIFe has been deployed in other domains, where people rather than robotic explorers, execute plans. As a result, a diverse set of end-users has compelled growth in a new direction: supporting real-time operations. This paper describes the new needs and challenges that accompany this development. Among the key features that have been built for SPIFe are current time indicators integrated into the interface and timeline, as well as other plan attributes that enable execution of scheduled activities. Field tests include mission support for the Lunar CRater Observation and Sensing Satellite (LCROSS), NASA Extreme Environment Mission Operations (NEEMO) and Desert Research and Technology Studies (DRATS) campaigns.

Marquez, Jessica J.↗
DOE OSTI2026

Agentic artificial intelligence for multistage physics experiments at a large-scale user facility particle accelerator

We present a language-model-driven agentic artificial intelligence (AI) system to autonomously execute multistage physics experiments on a production synchrotron light source. Implemented at the Advanced Light Source particle accelerator, the system translates natural language user prompts into structured execution plans that combine archive data retrieval, control-system channel resolution, automated script generation, controlled machine interaction, and analysis. In a representative machine physics task, we show that preparation time was reduced by 2 orders of magnitude relative to manual scripting even for a system expert, while operator-standard safety constraints were strictly upheld. Core architectural features, plan-first orchestration, bounded tool access, and dynamic capability selection, enable transparent, auditable execution with fully reproducible artifacts. These results establish a blueprint for the safe integration of agentic AI into accelerator experiments and demanding machine physics studies, as well as routine operations, with direct portability across accelerators worldwide and, more broadly, to other large-scale scientific infrastructures.

Accelerator/storage ring control systems↗
NASA NTRS1993

Impasse-Driven Tutoring for Reactive Skill Acquisition

We introduce a new approach to intelligent tutoring in performance-oriented training environments based on a method called situated plan attribution. The aim of this method is to provide contextualized tutoring for procedural tasks requiring reactive, goal-oriented skills. We use the term plan attribution instead of plan recognition because it does not assume that the problem solver is consciously executing plan. We avoid some of the pitfalls of other popularly used methods, i.e., model tracing and procedure net grammars, by selectively using an expert cognitive model to generate advice after detecting a problem solving impasse. The tutor attributes a set of plans to the student based on a task description. Each action is evaluated with respect to: the student's attributed plans, its actual effects on the training device, and the contextualized goals associated with the plans.

Johnson, W. Lewis↗
NASA NTRS1985

Software verification and testing

General procedures for software verification and validation are provided as a guide for managers, programmers, and analysts involved in software development. The verification and validation procedures described are based primarily on testing techniques. Testing refers to the execution of all or part of a software system for the purpose of detecting errors. Planning, execution, and analysis of tests are outlined in this document. Code reading and static analysis techniques for software verification are also described.

Source record↗
NASA NTRS2012

Real-Time Projection to Verify Plan Success During Execution

The Mission Data System provides a framework for modeling complex systems in terms of system behaviors and goals that express intent. Complex activity plans can be represented as goal networks that express the coordination of goals on different state variables of the system. Real-time projection extends the ability of this system to verify plan achievability (all goals can be satisfied over the entire plan) into the execution domain so that the system is able to continuously re-verify a plan as it is executed, and as the states of the system change in response to goals and the environment. Previous versions were able to detect and respond to goal violations when they actually occur during execution. This new capability enables the prediction of future goal failures; specifically, goals that were previously found to be achievable but are no longer achievable due to unanticipated faults or environmental conditions. Early detection of such situations enables operators or an autonomous fault response capability to deal with the problem at a point that maximizes the available options. For example, this system has been applied to the problem of managing battery energy on a lunar rover as it is used to explore the Moon. Astronauts drive the rover to waypoints and conduct science observations according to a plan that is scheduled and verified to be achievable with the energy resources available. As the astronauts execute this plan, the system uses this new capability to continuously re-verify the plan as energy is consumed to ensure that the battery will never be depleted below safe levels across the entire plan.

Wagner, David A.↗
NASA NTRS1987

Far-field mission planning for nap-of-the-earth flight

In the face of numerically superior hostile forces, deployment of individual vehicles to the right place, at the right time, and the ability to plan missions with less conservatism, will become significant force multipliers. Far-field mission planning is one of the enabling technologies that will facilitate force coordination through management of mission timeline, vehicle survivability and fuel constraints. On-board replanning is required to deal responsively with departures from nominal plan execution that result from imperfect knowledge of and temporal variability in the mission environment. The far-field planning problem is posed as a constrained optimization problem and algorithms and structural organization are proposed for the solution.

Deutsch, Owen L.↗
NASA NTRS1997

On-Board Planning for New Millenium Deep Space One Autonomy

The Deep Space One (DS1) mission, scheduled to fly in 1998, will be the first NASA spacecraft to feature an on-board planner. The planner is part of an artificial intelligence based control architecture that comprises the planner/scheduler, a plan execution engine, and a model-based fault diagnosis and reconfiguration engine...This paper describes the on-board planning and scheduling component of the DS1 autonomy architecture.

Deep↗
NASA NTRS2004

The TechSat 21 Autonomous Sciencecraft Experiment

Software has been developed to perform a number of functions essential to autonomous operation in the Autonomous Sciencecraft Experiment (ASE), which is scheduled to be demonstrated aboard a constellation of three spacecraft, denoted TechSat 21, to be launched by the Air Force into orbit around the Earth in January 2006. A prior version of this software was reported in Software for an Autonomous Constellation of Satellites (NPO-30355), NASA Tech Briefs, Vol. 26, No. 11 (November 2002), page 44. The software includes the following components: Algorithms to analyze image data, generate scientific data products, and detect conditions, features, and events of potential scientific interest; A program that uses component-based computational models of hardware to analyze anomalous situations and to generate novel command sequences, including (when possible) commands to repair components diagnosed as faulty; A robust-execution-management component that uses the Spacecraft Command Language (SCL) software to enable event-driven processing and low-level autonomy; and The Continuous Activity Scheduling, Planning, Execution, and Replanning (CASPER) program for replanning activities, including downlink sessions, on the basis of scientific observations performed during previous orbit cycles.

Sherwood, Robert↗
NASA NTRS1992

Multi-agent planning and scheduling, execution monitoring and incremental rescheduling: Application to motorway traffic

This article describes a planning method applicable to agents with great perception and decision-making capabilities and the ability to communicate with other agents. Each agent has a task to fulfill allowing for the actions of other agents in its vicinity. Certain simultaneous actions may cause conflicts because they require the same resource. The agent plans each of its actions and simultaneously transmits these to its neighbors. In a similar way, it receives plans from the other agents and must take account of these plans. The planning method allows us to build a distributed scheduling system. Here, these agents are robot vehicles on a highway communicating by radio. In this environment, conflicts between agents concern the allocation of space in time and are connected with the inertia of the vehicles. Each vehicle made a temporal, spatial, and situated reasoning in order to drive without collision. The flexibility and reactivity of the method presented here allows the agent to generate its plan based on assumptions concerning the other agents and then check these assumptions progressively as plans are received from the other agents. A multi-agent execution monitoring of these plans can be done, using data generated during planning and the multi-agent decision-making algorithm described here. A selective backtrack allows us to perform incremental rescheduling.

Mourou, Pascal↗
DOE OSTI2026

NREL Validation of S3.2 and S3.3 (CRADA Final Report)

The scope of work for S3.2 and S3.3 validation includes cooperative planning, execution, and analysis of all recommended and agreed-upon test protocols to determine the suitability of the product for long-term deployment of the photovoltaic modules to a broad range of climates. The test plan will be established within two months of project initiation, and NREL will write monthly reports for the duration of the work.

14 SOLAR ENERGY↗