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Results for “goal-based”

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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Application of State Analysis and Goal-Based Operations to a MER Mission Scenario

State Analysis is a model-based systems engineering methodology employing a rigorous discovery process which articulates operations concepts and operability needs as an integrated part of system design. The process produces requirements on system and software design in the form of explicit models which describe the behavior of states and the relationships among them. By applying State Analysis to an actual MER flight mission scenario, this study addresses the specific real world challenges of complex space operations and explores technologies that can be brought to bear on future missions. The paper describes the tools currently used on a daily basis for MER operations planning and provides an in-depth description of the planning process, in the context of a Martian day's worth of rover engineering activities, resource modeling, flight rules, science observations, and more. It then describes how State Analysis allows for the specification of a corresponding goal-based sequence that accomplishes the same objectives, with several important additional benefits.

mission planning↗
NASA NTRS1998

Automated Planning and Scheduling for Goal-Based Autonomous Spacecraft

Automated planning and scheduling technology - we'll call it automated planning systems, for the sake of brevity-is applicable to a wide spectrum of spaceflight missions, from those with limited onboard computational capabilities, such as Lunar Prospector, to those with highly sophisticated software, such as Cassini.

automated planning systems command mission operati↗
DOE OSTI2025

Adaptive Computing (AC) [SWR-24-106]

The Adaptive Computing (AC) software stack supports goal-based computing, for which a simulation workload is created on the fly adapting to the results of calculations. Application-specific code defines an objective, which may be to solve an optimization problem or to train a surrogate model with minimal uncertainty. Then, the AC driver decides where in the design parameter space to run simulations to best achieve that objective. This process is iterative and online; as new data is returned from simulations, the AC driver chooses new simulations to run. The AC driver can strategically run simulations on distributed hardware resources (including high performance computing machines, cloud resources, and edge devices) to maximize throughput and obey resource constraints.

Griffin, Kevin [National Renewable Energy Laborato↗
DOE OSTI2026

Adaptive Computing (AC) (Open Source) [SWR-24-106]

The Adaptive Computing (AC) software stack supports goal-based computing, for which a simulation workload is created on the fly, adapting to the results of calculations. Application-specific code defines an objective, which may be to solve an optimization problem or to train a surrogate model with minimal uncertainty. Then, the AC driver decides where in the design parameter space to run simulations to best achieve that objective. This process is iterative and online; as new data is returned from simulations, the AC driver chooses new simulations to run. The AC driver can strategically run simulations on distributed hardware resources (including high performance computing machines, cloud resources, and edge devices) to maximize throughput and obey resource constraints.

Griffin, Kevin [National Laboratory of the Rockies↗
NASA NTRS1994

The real-world navigator

The success of every mobile robot application hinges on the ability to navigate robustly in the real world. The problem of robust navigation is separable from the challenges faced by any particular robot application. We offer the Real-World Navigator as a solution architecture that includes a path planner, a map-based localizer, and a motion control loop that combines reactive avoidance modules with deliberate goal-based motion. Our architecture achieves a high degree of reliability by maintaining and reasoning about an explicit description of positional uncertainty. We provide two implementations of real-world robot systems that incorporate the Real-World Navigator. The Vagabond Project culminated in a robot that successfully navigated a portion of the Stanford University campus. The Scimmer project developed successful entries for the AIAA 1993 Robotics Competition, placing first in one of the two contests entered.

Balabanovic, Marko↗
NASA NTRS2000

Remote Agent Experiment

Remote Agent (RA) is a model-based, reusable artificial intelligence (At) software system that enables goal-based spacecraft commanding and robust fault recovery. RA was flight validated during an experiment on board of DS1 between May 17th and May 21th, 1999.

Benard, Doug↗
NASA NTRS2002

Autonomous planning and scheduling on the TechSat 21 mission

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting.

AI architectures applications intelligent agents m↗
NASA NTRS2002

Autonomous operations through onboard artificial intelligence

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.

Autonomous Sciencecraft Experiment ASE TechSat 21↗
NASA NTRS2002

Spacecraft autonomy using onboard processing for a SAR constellation mission

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattem recognition to radically increase science retum by enabling intelligent downlink selection and autonomous retargeting. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.

Autonomous Sciencecraft Experiment ASE spacecraft ↗
NASA NTRS2003

The autonomous sciencecraft constellations

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. In this paper we discuss how these AI technologies are synergistically integrated in a hybrid multi-layer control architecture to enable a virtual spacecraft science agent. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.

Autonomous Sciencecraft Experiment ASE AI technolo↗
NASA NTRS2002

Spacecraft autonomy using onboard processing for a SAR constellation mission

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. In this paper we discuss how these AI technologies are synergistically integrated in a hybrid multi-layer control architecture to enable a virtual spacecruft science agent. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.

Autonomous Sciencecraft Experiment ASE virtual spa↗
NASA NTRS2005

A unifying framework for systems modeling, control systems design, and system operation

Current engineering practice in the analysis and design of large-scale multi-disciplinary control systems is typified by some form of decomposition- whether functional or physical or discipline-based-that enables multiple teams to work in parallel and in relative isolation. Too often, the resulting system after integration is an awkward marriage of different control and data mechanisms with poor end-to-end accountability. System of systems engineering, which faces this problem on a large scale, cries out for a unifying framework to guide analysis, design, and operation. This paper describes such a framework based on a state-, model-, and goal-based architecture for semi-autonomous control systems that guides analysis and modeling, shapes control system software design, and directly specifies operational intent. This paper illustrates the key concepts in the context of a large-scale, concurrent, globally distributed system of systems: NASA's proposed Array-based Deep Space Network.

systems modeling↗
NASA NTRS2006

State Analysis Database Tool

The State Analysis Database Tool software establishes a productive environment for collaboration among software and system engineers engaged in the development of complex interacting systems. The tool embodies State Analysis, a model-based system engineering methodology founded on a state-based control architecture (see figure). A state represents a momentary condition of an evolving system, and a model may describe how a state evolves and is affected by other states. The State Analysis methodology is a process for capturing system and software requirements in the form of explicit models and states, and defining goal-based operational plans consistent with the models. Requirements, models, and operational concerns have traditionally been documented in a variety of system engineering artifacts that address different aspects of a mission s lifecycle. In State Analysis, requirements, models, and operations information are State Analysis artifacts that are consistent and stored in a State Analysis Database. The tool includes a back-end database, a multi-platform front-end client, and Web-based administrative functions. The tool is structured to prompt an engineer to follow the State Analysis methodology, to encourage state discovery and model description, and to make software requirements and operations plans consistent with model descriptions.

Rasmussen, Robert↗
NASA NTRS2004

Preliminary results of the autonomous sciencecraft experiment

The Autonomous Sciencecraft Experiment (ASE) will operate onboard the Earth Orbiter 1 mission 2004. The ASE software uses onboard continuous planning, robust task and goal-based execution, and onboard machine learning and patter recognition to radically increase science return by enabling intelligent downlink selection and autnomous retargeting. In this paper we will discuss how these AI technologies are synergistically integrated in multi-layer control architecture to enable a virtual spacecraft science agent.

Rabideau, Gregg↗
NASA NTRS2004

Preliminary results of the autonomous sciencecraft experiment

The Autonomous Sciencecraft Experiment (ASE) will operate onboard the Earth Orbiter 1 mission in 2003. The ASE software uses onboard continuous planning, robust task and goal-based execution, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. In this paper we discuss how these AI technologies are synergistically integrated in multilayer control architecture to enable a virtual spacecraft science agent.

Rabideau, Gregg↗