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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Using Model-Based Reasoning for Autonomous Instrument Operation

Multiprobe missions are an important part of NASA's future: Cluster, Magnetospheric Multi Scale, Global Electrodynamics and Magnetospheric Constellation are representatives from the Sun-Earth Connections Theme. To make such missions robust, reliable, and affordable, ideally the many spacecraft of a constellation must be at least as easy to operate as one spacecraft is today. To support this need for scalability, science instrumentation must become increasingly easy to operate, even as this same instrumentation becomes more capable and advanced. Communication and control resources will be at a premium for future instruments. Many missions will be out of contact with ground operators for extended periods either to reduce operations cost or because of orbits that limit communication to weekly perigee transits. Autonomous capability is necessary if such missions are to effectively achieve their operational objectives. An autonomous system is one that acts given its situation in a mission appropriate manner without external direction to achieve mission goals. To achieve this capability autonomy must be built into the system through judicious design or through a built-in intelligence that recognizes system state and manages system response. To recognize desired or undesired system states, the system must have an implicit or explicit understanding of its expected states given its history and self observations. The systems we are concerned with, science instruments, can have stringent requirements for system state knowledge in addition to requirements driven by health and safety concerns. Without accurate knowledge of the system state, the usefulness of the science instrument may be severely limited. At the same time, health and safety concerns often lead to overly conservative instrument operations further reducing the effectiveness of the instrument. These requirements, coupled with overall mission requirements including lack of communication opportunities and tolerance of environmental hazards, frame the problem of constructing autonomous science instruments. we are developing a model of the Low Energy Neutral Atom instrument (LENA) that is currently flying on board the Imager for Magnetosphere-to-Aurora Global Exploration (IMAGE) spacecraft. LENA is a particle detector that uses high voltage electrostatic optics and time-of-flight mass spectrometry to image neutral atom emissions from the denser regions of the Earth's magnetosphere. As with most spacecraft borne science instruments, phenomena in addition to neutral atoms are detected by LENA. Solar radiation and energetic particles from Earth's radiation belts are of particular concern because they may help generate currents that may compromise LENA's long term performance. An explicit model of the instrument response has been constructed and is currently in use on board IMAGE to dynamically adapt LENA to the presence or absence of energetic background radiations. The components of LENA are common in space science instrumentation, and lessons learned by modelling this system may be applied to other instruments. This work demonstrates that a model-based approach can be used to enhance science instrument effectiveness. Our future work involves the extension of these methods to cover more aspects of LENA operation and the generalization to other space science instrumentation.

Johnson, Mike↗
NASA NTRS1990

Demonstrating artificial intelligence for space systems - Integration and project management issues

As part of its Systems Autonomy Demonstration Project (SADP), NASA has recently demonstrated the Thermal Expert System (TEXSYS). Advanced real-time expert system and human interface technology was successfully developed and integrated with conventional controllers of prototype space hardware to provide intelligent fault detection, isolation, and recovery capability. Many specialized skills were required, and responsibility for the various phases of the project therefore spanned multiple NASA centers, internal departments and contractor organizations. The test environment required communication among many types of hardware and software as well as between many people. The integration, testing, and configuration management tools and methodologies which were applied to the TEXSYS project to assure its safe and successful completion are detailed. The project demonstrated that artificial intelligence technology, including model-based reasoning, is capable of the monitoring and control of a large, complex system in real time.

Hack, Edmund C.↗
NASA NTRS1993

A vision system planner for increasing the autonomy of the Extravehicular Activity Helper/Retriever

The Extravehicular Activity Retriever (EVAR) is a robotic device currently being developed by the Automation and Robotics Division at the NASA Johnson Space Center to support activities in the neighborhood of the Space Shuttle or Space Station Freedom. As the name implies, the Retriever's primary function will be to provide the capability to retrieve tools and equipment or other objects which have become detached from the spacecraft, but it will also be able to rescue a crew member who may have become inadvertently de-tethered. Later goals will include cooperative operations between a crew member and the Retriever such as fetching a tool that is required for servicing or maintenance operations. This paper documents a preliminary design for a Vision System Planner (VSP) for the EVAR that is capable of achieving visual objectives provided to it by a high level task planner. Typical commands which the task planner might issue to the VSP relate to object recognition, object location determination, and obstacle detection. Upon receiving a command from the task planner, the VSP then plans a sequence of actions to achieve the specified objective using a model-based reasoning approach. This sequence may involve choosing an appropriate sensor, selecting an algorithm to process the data, reorienting the sensor, adjusting the effective resolution of the image using lens zooming capability, and/or requesting the task planner to reposition the EVAR to obtain a different view of the object. An initial version of the Vision System Planner which realizes the above capabilities using simulated images has been implemented and tested. The remaining sections describe the architecture and capabilities of the VSP and its relationship to the high level task planner. In addition, typical plans that are generated to achieve visual goals for various scenarios are discussed. Specific topics to be addressed will include object search strategies, repositioning of the EVAR to improve the quality of information obtained from the sensors, and complementary usage of the sensors and redundant capabilities.

Magee, Michael↗
NASA NTRS1990

Results of the Systems Autonomy Demonstration Project

The Systems Autonomy Demonstration Project (SADP) produced a knowledge-based real-time control system for real-time control and fault detection, isolation and recovery (FDIR) of a prototype two-phase Space Station Freedom external control system (TCS). The Thermal Expert System (TEXSYS) was demonstrated in recent tests to be capable of reliable fault anticipation and detection, as well as nominal control of the thermal bus. Performance requirements were addressed by adopting a hierarchical symbolic control approach, layering model-based expert system software on a conventional numerical data acquisition and control system. The model-based capabilities of TEXSYS were shown to be advantageous, particularly for detection of unforeseen faults and sensor failures.

Glass, B. J.↗
NASA NTRS1990

TEXSYS

The Systems Autonomy Demonstration Project has recently completed a major test and evaluation of TEXSYS, a knowledge-based system (KBS) which demonstrates real-time control and FDIR for the Space Station Freedom thermal control system test-bed. TEXSYS is the largest KBS ever developed by NASA and offers a unique opportunity for the study of technical issues associated with the use of advanced KBS concepts including: model-based reasoning and diagnosis, quantitative and qualitative reasoning, integrated use of model-based and rule-based representations, temporal reasoning, and scale-up performance issues. TEXSYS represents a major achievement in advanced automation that has the potential to significantly influence Space Station Freedom's design for the thermal control system. An overview of the Systems Autonomy Demonstration Project, the thermal control system test-bed, the TEXSYS architecture, preliminary test results, and thermal domain expert feedback are presented.

Bull, John↗
NASA NTRS1992

Thermal Expert System (TEXSYS): Systems automony demonstration project, volume 1. Overview

The Systems Autonomy Demonstration Project (SADP) produced a knowledge-based real-time control system for control and fault detection, isolation, and recovery (FDIR) of a prototype two-phase Space Station Freedom external active thermal control system (EATCS). The Thermal Expert System (TEXSYS) was demonstrated in recent tests to be capable of reliable fault anticipation and detection, as well as ordinary control of the thermal bus. Performance requirements were addressed by adopting a hierarchical symbolic control approach-layering model-based expert system software on a conventional, numerical data acquisition and control system. The model-based reasoning capabilities of TEXSYS were shown to be advantageous over typical rule-based expert systems, particularly for detection of unforeseen faults and sensor failures. Volume 1 gives a project overview and testing highlights. Volume 2 provides detail on the EATCS test bed, test operations, and online test results. Appendix A is a test archive, while Appendix B is a compendium of design and user manuals for the TEXSYS software.

Glass, B. J.↗
NASA NTRS1992

Thermal Expert System (TEXSYS): Systems autonomy demonstration project, volume 2. Results

The Systems Autonomy Demonstration Project (SADP) produced a knowledge-based real-time control system for control and fault detection, isolation, and recovery (FDIR) of a prototype two-phase Space Station Freedom external active thermal control system (EATCS). The Thermal Expert System (TEXSYS) was demonstrated in recent tests to be capable of reliable fault anticipation and detection, as well as ordinary control of the thermal bus. Performance requirements were addressed by adopting a hierarchical symbolic control approach-layering model-based expert system software on a conventional, numerical data acquisition and control system. The model-based reasoning capabilities of TEXSYS were shown to be advantageous over typical rule-based expert systems, particularly for detection of unforeseen faults and sensor failures. Volume 1 gives a project overview and testing highlights. Volume 2 provides detail on the EATCS testbed, test operations, and online test results. Appendix A is a test archive, while Appendix B is a compendium of design and user manuals for the TEXSYS software.

Glass, B. J.↗
NASA NTRS2000

Controlling Microbial Byproducts using Model-Based Substrate Monitoring and Control Strategies

We have developed a computer-controlled bioreactor system to study various aspects of microbially-mediated nitrogen cycling. The system has been used to investigate methods for controlling microbial denitrification (the dissimilatory reduction of nitrate to N2O and N2) in hydroponic plant growth chambers. Such chambers are key elements of advanced life support systems being designed for use on long duration space missions, but nitrogen use efficiency in them is reduced by denitrification. Control software architecture was designed which permits the heterogeneous control of system hardware using traditional feedback control, and quantitative and qualitative models of various system features. Model-based feed forward control entails prediction of future systems in states and automated regulation of system parameters to achieve desired and avoid undesirable system states. A bacterial growth rate model based on the classic Monod model of saturation kinetics was used to evaluate the response of several individual denitrifying species to varying environmental conditions. The system and models are now being applied to mixed microbial communities harvested from the root zone of a hydroponic growth chamber. The use of a modified Monod organism interaction model was evaluated as a means of achieving more accurate description of the dynamic behavior of the communities. A minimum variance parameter estimation routine was also' used to calibrate the constant parameters in the model by iterative evaluation of substrate (nitrate) uptake and growth kinetics. This representation of processes and interactions aids in the formulation of control laws. The feed forward control strategy being developed will increase system autonomy, reduce crew intervention and limit the accumulation of undesirable waste products (NOx).

Smernoff, David T.↗
NASA NTRS2000

From Livingstone to SMV: Formal Verification for Autonomous Spacecrafts

To fulfill the needs of its deep space exploration program, NASA is actively supporting research and development in autonomy software. However, the reliable and cost-effective development and validation of autonomy systems poses a tough challenge. Traditional scenario-based testing methods fall short because of the combinatorial explosion of possible situations to be analyzed, and formal verification techniques typically require a tedious, manual modelling by formal method experts. This paper presents the application of formal verification techniques in the development of autonomous controllers based on Livingstone, a model-based health-monitoring system that can detect and diagnose anomalies and suggest possible recovery actions. We present a translator that converts the models used by Livingstone into specifications that can be verified with the SMV model checker. The translation frees the Livingstone developer from the tedious conversion of his design to SMV, and isolates him from the technical details of the SMV program. We describe different aspects of the translation and briefly discuss its application to several NASA domains.

Pecheur, Charles↗
NASA NTRS2001

Collaboration Between NASA Centers of Excellence on Autonomous System Software Development

Software for space systems flight operations has its roots in the early days of the space program when computer systems were incapable of supporting highly complex and flexible control logic. Control systems relied on fast data acquisition and supervisory control from a roomful of systems engineers on the ground. Even though computer hardware and software has become many orders of magnitude more capable, space systems have largely adhered to this original paradigm In an effort to break this mold, Kennedy Space Center (KSC) has invested in the development of model-based diagnosis and control applications for ten years having broad experience in both ground and spacecraft systems and software. KSC has now partnered with Ames Research Center (ARC), NASA's Center of Excellence in Information Technology, to create a new paradigm for the control of dynamic space systems. ARC has developed model-based diagnosis and intelligent planning software that enables spacecraft to handle most routine problems automatically and allocate resources in a flexible way to realize mission objectives. ARC demonstrated the utility of onboard diagnosis and planning with an experiment aboard Deep Space I in 1999. This paper highlights the software control system collaboration between KSC and ARC. KSC has developed a Mars In-situ Resource Utilization testbed based on the Reverse Water Gas Shift (RWGS) reaction. This plant, built in KSC's Applied Chemistry Laboratory, is capable of producing the large amount of Oxygen that would be needed to support a Human Mars Mission. KSC and ARC are cooperating to develop an autonomous, fault-tolerant control system for RWGS to meet the need for autonomy on deep space missions. The paper will also describe how the new system software paradigm will be applied to Vehicle Health Monitoring, tested on the new X vehicles and integrated into future launch processing systems.

Goodrich, Charles H.↗
NASA NTRS2002

Virtual Engineering and Science Team - Reusable Autonomy for Spacecraft Subsystems

In this paper we address the design, development, and evaluation of the Virtual Engineering and Science Team (VEST) tool - a revolutionary way to achieve onboard subsystem/instrument autonomy. VEST directly addresses the technology needed for advanced autonomy enablers for spacecraft subsystems. It will significantly support the efficient and cost effective realization of on-board autonomy and contribute directly to realizing the concept of an intelligent autonomous spacecraft. VEST will support the evolution of a subsystem/instrument model that is probably correct and from that model the automatic generation of the code needed to support the autonomous operation of what was modeled. VEST will directly support the integration of the efforts of engineers, scientists, and software technologists. This integration of efforts will be a significant advancement over the way things are currently accomplished. The model, developed through the use of VEST, will be the basis for the physical construction of the subsystem/instrument and the generated code will support its autonomous operation once in space. The close coupling between the model and the code, in the same tool environment, will help ensure that correct and reliable operational control of the subsystem/instrument is achieved.VEST will provide a thoroughly modern interface that will allow users to easily and intuitively input subsystem/instrument requirements and visually get back the system's reaction to the correctness and compatibility of the inputs as the model evolves. User interface/interaction, logic, theorem proving, rule-based and model-based reasoning, and automatic code generation are some of the basic technologies that will be brought into play in realizing VEST.

Bailin, Sidney C.↗
NASA NTRS1997

A Generative Control Capability for a Model-based Executive

This paper describes Burton, a core element of a new generation of goal-directed model-based autonomous executives. This executive makes extensive use of component-based declarative models to analyze novel situations and generate novel control actions both at the goal and hardware levels. It uses an extremely efficient online propositional inference engine to efficiently determine likely states consistent with current observations and optimal target states that achieve high level goals. It incorporates a flexible generative control sequencing algorithm within the reactive loop to bridge the gap between current and target states. The system is able to detect and avoid damaging and irreversible situations, After every control action it uses its model and sensors to detect anomalous situations and immediately take corrective action. Efficiency is achieved through a series of model compilation and online policy construction methods, and by exploiting general conventions of hardware design that permit a divide and conquer approach to planning. The paper presents a formal characterization of Burton's capability, develops efficient algorithms, and reports on experience with the implementation in the domain of spacecraft autonomy. Burton is being incorporated as one of the key elements of the Remote Agent core autonomy architecture for Deep Space One, the first spacecraft for NASA's New Millenium program.

Williams, Brian C.↗
NASA NTRS1998

A Hybrid Procedural/Deductive Executive for Autonomous Spacecraft

The New Millennium Remote Agent (NMRA) will be the first AI system to control an actual spacecraft. The spacecraft domain places a strong premium on autonomy and requires dynamic recoveries and robust concurrent execution, all in the presence of tight real-time deadlines, changing goals, scarce resource constraints, and a wide variety of possible failures. To achieve this level of execution robustness, we have integrated a procedural executive based on generic procedures with a deductive model-based executive. A procedural executive provides sophisticated control constructs such as loops, parallel activity, locks, and synchronization which are used for robust schedule execution, hierarchical task decomposition, and routine configuration management. A deductive executive provides algorithms for sophisticated state inference and optimal failure recover), planning. The integrated executive enables designers to code knowledge via a combination of procedures and declarative models, yielding a rich modeling capability suitable to the challenges of real spacecraft control. The interface between the two executives ensures both that recovery sequences are smoothly merged into high-level schedule execution and that a high degree of reactivity is retained to effectively handle additional failures during recovery.

Pell, Barney↗
NASA NTRS2003

Agent Based Software for the Autonomous Control of Formation Flying Spacecraft

Distributed satellite systems is an enabling technology for many future NASA/DoD earth and space science missions, such as MMS, MAXIM, Leonardo, and LISA [1, 2, 3]. While formation flying offers significant science benefits, to reduce the operating costs for these missions it will be essential that these multiple vehicles effectively act as a single spacecraft by performing coordinated observations. Autonomous guidance, navigation, and control as part of a coordinated fleet-autonomy is a key technology that will help accomplish this complex goal. This is no small task, as most current space missions require significant input from the ground for even relatively simple decisions such as thruster burns. Work for the NMP DS1 mission focused on the development of the New Millennium Remote Agent (NMRA) architecture for autonomous spacecraft control systems. NMRA integrates traditional real-time monitoring and control with components for constraint-based planning, robust multi-threaded execution, and model-based diagnosis and reconfiguration. The complexity of using an autonomous approach for space flight software was evident when most of its capabilities were stripped off prior to launch (although more capability was uplinked subsequently, and the resulting demonstration was very successful).

How, Jonathan P.↗
NASA NTRS2003

Simulation-Based Verification of Livingstone Applications

AI software is viewed as a means to give greater autonomy to automated systems, capable of coping with harsh and unpredictable environments in deep space missions. Autonomous systems pose a serious challenge to traditional test-based verification approaches, because of the enormous space of possible situations that they aim to address. Before these systems are put in control of critical applications, appropriate new verification approaches need to be developed. This article describes Livingstone PathFinder (LPF), a verification tool for autonomous diagnosis applications based on NASA's Livingstone model-based diagnosis system. LPF applies state space exploration algorithms to an instrumented testbed, consisting of the Livingstone diagnosis system embedded in a simulated operating environment. The article describes different facets of LPF and reports some experimental results from applying LPF to a Livingstone model of the main propulsion feed subsystem of the X-34 space vehicle. This paper describes Livingstone PathFinder (LPF), a verification tool for autonomous diagnosis applications based on NASA's Livingstone model-based diagnosis system. LPF applies state space exploration algorithms to an instrumented testbed, consisting of the Livingstone diagnosis system embedded in a simulated operating environment. Section 2 provides an overview of Livingstone; Section 3 describes the LPF architecture; Section 4 discusses its applicability; Section 5 reviews some experimental results; Section 6 compares LPF to related verification approaches; Section 7 draws conclusions and discusses some perspectives.

Lindsey, Anthony E.↗
NASA NTRS2004

Simulation-Based Verification of Autonomous Controllers via Livingstone PathFinder

AI software is often used as a means for providing greater autonomy to automated systems, capable of coping with harsh and unpredictable environments. Due in part to the enormous space of possible situations that they aim to addrs, autonomous systems pose a serious challenge to traditional test-based verification approaches. Efficient verification approaches need to be perfected before these systems can reliably control critical applications. This publication describes Livingstone PathFinder (LPF), a verification tool for autonomous control software. LPF applies state space exploration algorithms to an instrumented testbed, consisting of the controller embedded in a simulated operating environment. Although LPF has focused on NASA s Livingstone model-based diagnosis system applications, the architecture is modular and adaptable to other systems. This article presents different facets of LPF and experimental results from applying the software to a Livingstone model of the main propulsion feed subsystem for a prototype space vehicle.

Lindsey, A. E.↗
NASA NTRS2005

Sensor Webs: Autonomous Rapid Response to Monitor Transient Science Events

To better understand how physical phenomena, such as volcanic eruptions, evolve over time, multiple sensor observations over the duration of the event are required. Using sensor web approaches that integrate original detections by in-situ sensors and global-coverage, lower-resolution, on-orbit assets with automated rapid response observations from high resolution sensors, more observations of significant events can be made with increased temporal, spatial, and spectral resolution. This paper describes experiments using Earth Observing 1 (EO-1) along with other space and ground assets to implement progressive mission autonomy to identify, locate and image with high resolution instruments phenomena such as wildfires, volcanoes, floods and ice breakup. The software that plans, schedules and controls the various satellite assets are used to form ad hoc constellations which enable collaborative autonomous image collections triggered by transient phenomena. This software is both flight and ground based and works in concert to run all of the required assets cohesively and includes software that is model-based, artificial intelligence software.

Mandl, Dan↗
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↗