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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
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NASA NTRS2016

Using Multimodal Input for Autonomous Decision Making for Unmanned Systems

Autonomous decision making in the presence of uncertainly is a deeply studied problem space particularly in the area of autonomous systems operations for land, air, sea, and space vehicles. Various techniques ranging from single algorithm solutions to complex ensemble classifier systems have been utilized in a research context in solving mission critical flight decisions. Realized systems on actual autonomous hardware, however, is a difficult systems integration problem, constituting a majority of applied robotics development timelines. The ability to reliably and repeatedly classify objects during a vehicles mission execution is vital for the vehicle to mitigate both static and dynamic environmental concerns such that the mission may be completed successfully and have the vehicle operate and return safely. In this paper, the Autonomy Incubator proposes and discusses an ensemble learning and recognition system planned for our autonomous framework, AEON, in selected domains, which fuse decision criteria, using prior experience on both the individual classifier layer and the ensemble layer to mitigate environmental uncertainty during operation.

Neilan, James H.↗
NASA NTRSDate not supplied

A Distributed Simulation-to-Flight Framework to Support Investigating Trust/Trustworthiness in Multi-Agent Systems

As autonomous systems continue to grow both in use and complexity, the necessity for robust and extensible simulation-to-flight frameworks is paramount for establishing an effective architecture for autonomous systems. Hardware test flights are time-consuming and cost prohibitive during early system design and development. Simulation environments can be useful tools to accelerate algorithm development and testing. However, transitions from simulation to flight (sim-to-flight) can be challenging, unless systems are designed with this transition in mind and with the necessary capabilities built into the architecture and framework. One of the objectives of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) was to design and develop a distributed mixed-reality simulation environment to begin establishing a basis for certification of autonomous systems via research into trust and trustworthiness. ATTRACTOR’s objective was to construct computational concepts of trustworthiness and justifiable trust in multi-agent autonomous teams, to inform future certification of safety-critical and time-critical autonomous systems in aviation. In this paper, we present an autonomous systems architecture and development framework paired with a persistent distributed modeling and simulation (ModSim) environment for test and evaluation of autonomous systems. They were designed under ATTRACTOR in order to measure and establish trustworthiness and trust in single-and multi-agent human-machine systems whether these machines are fixed-wing general aviation, rotary-wing Unmanned Aerial Vehicles (UAVs), ground rovers, or even spacecraft. The Autonomous Entity Operational Network (AEON) framework enables autonomous system development with an easily extensible collection of libraries and plug-n-play nodes facilitated by the Data Distribution Service (DDS) communication protocol standard. The Baseline Environment for Autonomous Modeling (BEAM) simulation environment is a distributed mixed-reality Unity™-based environment built around the same DDS communication paradigm allowing for easy integration with AEON-based autonomous applications, enabling sim-to-flight with minimal configuration changes. Using AEON and BEAM, source code that runs in simulation ports directly to hardware and has successfully flown in the National Airspace System (NAS) at NASA LaRC many times over the lifetime of ATTRACTOR.

Benjamin N Kelley↗
NASA NTRSDate not supplied

A Distributed Simulation-to-Flight Framework to Support Investigating Trust/Trustworthiness in Multi-Agent Systems

As autonomous systems continue to grow both in use and complexity, the necessity for robust and extensible simulation-to-flight methods is paramount for establishing an effective architecture for autonomous systems. A fundamental objective of the ATTRACTOR (Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability) project was to design and develop a distributed mixed-reality simulation environment to begin establishing a basis for certification of autonomous systems via research into trust and trustworthiness. In this paper, we present an autonomous systems architecture and development framework paired with a persistent distributed modeling and simulation environment for test and evaluation of autonomous systems. The Autonomous Entity Operations Network (AEON) framework enables autonomous system development with an easily extensible collection of libraries and plug-n-play nodes facilitated by the Data Distribution Service (DDS) communication protocol standard. The Baseline Environment for Autonomous Modeling (BEAM) simulation environment is a distributed mixed-reality Unity™-based environment built around the same DDS communication paradigm allowing for easy integration with AEON-based autonomous applications. They were designed under ATTRACTOR in order to measure and establish trustworthiness and trust in single- and multi-agent human-machine systems whether these machines are fixed-wing general aviation, rotary-wing Unmanned Aerial Vehicles (UAVs), ground rovers, or even spacecraft. Together AEON and BEAM enable sim-to-flight with minimal configuration changes. By using AEON and BEAM, source code that runs in simulation ports directly to hardware and has successfully flown in the lab and in the National Airspace System (NAS) at NASA LaRC many times over the lifetime of ATTRACTOR.

Benjamin N Kelley↗
NASA NTRSDate not supplied

A Distributed Simulation-to-Flight Framework to Support Investigating Trust/Trustworthiness in Multi-Agent Systems

As autonomous systems continue to grow both in use and complexity, the necessity for robust and extensible simulation-to-flight methods is paramount for establishing an effective architecture for autonomous systems. A fundamental objective of the ATTRACTOR (Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability) project was to design and develop a distributed mixed-reality simulation environment to begin establishing a basis for certification of autonomous systems via research into trust and trustworthiness. In this paper, we present an autonomous systems architecture and development framework paired with a persistent distributed modeling and simulation environment for test and evaluation of autonomous systems. The Autonomous Entity Operations Network (AEON) framework enables autonomous system development with an easily extensible collection of libraries and plug-n-play nodes facilitated by the Data Distribution Service (DDS) communication protocol standard. The Baseline Environment for Autonomous Modeling (BEAM) simulation environment is a distributed mixed-reality Unity™-based environment built around the same DDS communication paradigm allowing for easy integration with AEON-based autonomous applications. They were designed under ATTRACTOR in order to measure and establish trustworthiness and trust in single- and multi-agent human-machine systems whether these machines are fixed-wing general aviation, rotary-wing Unmanned Aerial Vehicles (UAVs), ground rovers, or even spacecraft. Together AEON and BEAM enable sim-to-flight with minimal configuration changes. By using AEON and BEAM, source code that runs in simulation ports directly to hardware and has successfully flown in the lab and in the National Airspace System (NAS) at NASA LaRC many times over the lifetime of ATTRACTOR.

Benjamin N Kelley↗
NASA NTRS1992

Scheduling lessons learned from the Autonomous Power System

The Autonomous Power System (APS) project at NASA LeRC is designed to demonstrate the applications of integrated intelligent diagnosis, control, and scheduling techniques to space power distribution systems. The project consists of three elements: the Autonomous Power Expert System (APEX) for Fault Diagnosis, Isolation, and Recovery (FDIR); the Autonomous Intelligent Power Scheduler (AIPS) to efficiently assign activities start times and resources; and power hardware (Brassboard) to emulate a space-based power system. The AIPS scheduler was tested within the APS system. This scheduler is able to efficiently assign available power to the requesting activities and share this information with other software agents within the APS system in order to implement the generated schedule. The AIPS scheduler is also able to cooperatively recover from fault situations by rescheduling the affected loads on the Brassboard in conjunction with the APEX FDIR system. AIPS served as a learning tool and an initial scheduling testbed for the integration of FDIR and automated scheduling systems. Many lessons were learned from the AIPS scheduler and are now being integrated into a new scheduler called SCRAP (Scheduler for Continuous Resource Allocation and Planning). This paper will service three purposes: an overview of the AIPS implementation, lessons learned from the AIPS scheduler, and a brief section on how these lessons are being applied to the new SCRAP scheduler.

Ringer, Mark J.↗
NASA NTRS1992

Lessons learned from the Autonomous Power System

The Autonomous Power System (APS) project at the NASA Lewis Research Center is designed to demonstrate the applications of integrated intelligent diagnosis, control and scheduling techniques to space power distribution systems. The project consists of three elements: the Autonomous Power Expert System (APEX) for Fault Diagnosis, Isolation, and Recovery (FDIR); the Autonomous Intelligent Power Scheduler (AIPS) to efficiently assign activities start times and resources; and power hardware (Brassboard) to emulate a space-based power system. The APS project had been through one design iteration. Each of the three elements of the APS project has been designed, tested, and integrated into a complete working system. After these three portions were completed, an evaluation period was initiated. Each piece of the system was critiqued based on individual performance as well as the ability to interact with the other portions of the APS project. These critiques were then used to determine guidelines for new and improved components of the APS system.

Ringer, Mark J.↗
NASA NTRS1996

Immobile Robots: AI in the New Millennium

A new generation of sensor rich, massively distributed, autonomous systems are being developed that have the potential for profound social, environmental, and economic change. These include networked building energy systems, autonomous space probes, chemical plant control systems, satellite constellations for remote ecosystem monitoring, power grids, biosphere-like life support systems, and reconfigurable traffic systems, to highlight but a few. To achieve high performance, these immobile robots (or immobots) will need to develop sophisticated regulatory and immune systems that accurately and robustly control their complex internal functions. To accomplish this, immobots will exploit a vast nervous system of sensors to model themselves and their environment on a grand scale. They will use these models to dramatically reconfigure themselves in order to survive decades of autonomous operations. Achieving these large scale modeling and configuration tasks will require a tight coupling between the higher level coordination function provided by symbolic reasoning, and the lower level autonomic processes of adaptive estimation and control. To be economically viable they will need to be programmable purely through high level compositional models. Self modeling and self configuration, coordinating autonomic functions through symbolic reasoning, and compositional, model-based programming are the three key elements of a model-based autonomous systems architecture that is taking us into the New Millennium.

Williams, Brian C.↗
NASA NTRSDate not supplied

The Anatomy of Software Changes and Bugs in Autonomous Operating System

Cyberphysical systems with autonomous functions are complex pieces of software, consisting of many components, some of which implement autonomous functionality and some may use AI or machine learning algorithms. Software bugs in an autonomous system are of particular concern, as they can have catastrophic consequences. However, detailed studies based on empirical data are rare and therefore these bugs are not well understood. This paper aims to contribute towards filling that gap by investigating the software changes and bugs in Autonomy Operating System (AOS) for Unmanned Aircraft Systems (UAS), which consist of 26 components containing about 103,000 lines of code and having a total of 772 bugfixes. Based on the data extracted from the code repository and semi-structured interviews with the developers of AOS, we explore the differences among autonomous software components, components developed using Model-based Software Engineering, and reuse with respect to change proneness, fault proneness, distribution of bugfixes among AOS components and files of these components, and characteristics of bugs of different AOS components. Our results show that the autonomous components were significantly more change prone (measured in number of commits and code churn) and fault prone (measured in bugfixes per KLoC) than non-autonomous components. The distribution of the locations of bugfixes was skewed, both at component and file level (i.e., a small number of components / files contained the majority of bugs). These evidence-based findings provide important insights to researchers and practitioners alike and can be used to efficiently improve the quality and reliability of autonomous systems.

Katerina Goseva-Popstojanova↗
NASA NTRS1992

Closed-loop autonomous docking system

An autonomous docking system is provided which produces commands for the steering and propulsion system of a chase vehicle used in the docking of that chase vehicle with a target vehicle. The docking system comprises a passive optical target affixed to the target vehicle and comprising three reflective areas including a central area mounted on a short post, and tracking sensor and process controller apparatus carried by the chase vehicle. The latter apparatus comprises a laser diode array for illuminating the target so as to cause light to be reflected from the reflective areas of the target; a sensor for detecting the light reflected from the target and for producing an electrical output signal in accordance with an image of the reflected light; a signal processor for processing the electrical output signal in accordance with an image of the reflected light; a signal processor for processing the electrical output signal and for producing, based thereon, output signals relating to the relative range, roll, pitch, yaw, azimuth, and elevation of the chase and target vehicles; and a docking process controller, responsive to the output signals produced by the signal processor, for producing command signals for controlling the steering and propulsion system of the chase vehicle.

Dabney, Richard W.↗
NASA NTRS2014

Reducing Size, Weight, and Power (SWaP) of Perception Systems in Small Autonomous Aerial Systems

The objectives are to examine recent trends in the reduction of size, weight, and power (SWaP) requirements of sensor systems for environmental perception and to explore new technology that may overcome limitations in current systems. Improving perception systems to facilitate situation awareness is critical in the move to introduce increasing autonomy in aerial systems. Whether the autonomy is in the current state-of-the-art of increasing automation or is enabling cognitive decisions that facilitate adaptive behavior, collection of environmental information and fusion of that information into knowledge that can direct actuation is imperative to decisions resulting in appropriate behavior. Artificial sensory systems such as cameras, radar, LIDAR, and acoustic sensors have been in use on aircraft for many years but, due to the large size and weight of the airplane and electrical power made available through powerful engines, the SWaP requirements of these sensors was inconsequential. With the proliferation of Remote Piloted Vehicles (RPV), the trend is in significant reduction in SWaP of the vehicles. This requires at least an equivalent reduction in SWaP for the sensory systems. A survey of some currently available sensor systems and changing technology will reveal the trend toward reduction of SWaP of these systems and will predict future reductions. A new technology will be introduced that provides an example of a desirable new trend. A new device replaces multiple conventional sensory devices facilitating synchronization, localization, altimetry, collision avoidance, terrain mapping, and data communication in a single integrated, small form-factor, extremely lightweight, and low power device that it is practical for integration into small autonomous vehicles and can facilitate cooperative behavior. The technology is based on Ultra WideBand (UWB) radio using short pulses of energy rather than continuous sine waves. The characteristics of UWB yield several desirable characteristics to facilitate integration of perception for autonomous activities. The capabilities of this device and its limitations will be assessed.

Jones, Kennie H.↗
NASA NTRS1994

Global Positioning System Synchronized Active Light Autonomous Docking System

A Global Positioning System Synchronized Active Light Autonomous Docking System (GPSSALADS) for automatically docking a chase vehicle with a target vehicle comprises at least one active light emitting target which is operatively attached to the target vehicle. The target includes a three-dimensional array of concomitantly flashing lights which flash at a controlled common frequency. The GPSSALADS further comprises a visual tracking sensor operatively attached to the chase vehicle for detecting and tracking the target vehicle. Its performance is synchronized with the flash frequency of the lights by a synchronization means which is comprised of first and second internal clocks operatively connected to the active light target and visual tracking sensor, respectively, for providing timing control signals thereto, respectively. The synchronization means further includes first and second Global Positioning System receivers operatively connected to the first and second internal clocks, respectively, for repeatedly providing simultaneous synchronization pulses to the internal clocks, respectively. In addition, the GPSSALADS includes a docking process controller means which is operatively attached to the chase vehicle and is responsive to the visual tracking sensor for producing commands for the guidance and propulsion system of the chase vehicle.

Howard, Richard↗
NASA NTRS1996

Global Positioning System Synchronized Active Light Autonomous Docking System

A Global Positioning System Synchronized Active Light Autonomous Docking System (GPSSALADS) for automatically docking a chase vehicle with a target vehicle comprising at least one active light emitting target which is operatively attached to the target vehicle. The target includes a three-dimensional array of concomitantly flashing lights which flash at a controlled common frequency. The GPSSALADS further comprises a visual tracking sensor operatively attached to the chase vehicle for detecting and tracking the target vehicle. Its performance is synchronized with the flash frequency of the lights by a synchronization means which is comprised of first and second internal clocks operatively connected to the active light target and visual tracking sensor, respectively, for providing timing control signals thereto, respectively. The synchronization means further includes first and second Global Positioning System receivers operatively connected to the first and second internal clocks, respectively, for repeatedly providing simultaneous synchronization pulses to the internal clocks, respectively. In addition, the GPSSALADS includes a docking process controller means which is operatively attached to the chase vehicle and is responsive to the visual tracking sensor for producing commands for the guidance and propulsion system of the chase vehicle.

Howard, Richard T.↗
NASA NTRSDate not supplied

Implementing Artificial Thinking Autonomy with Model-Based System Engineering

Complex autonomous systems capable of successfully operating independently under ‘known unknowns’ and harsh conditions require paradigm innovation in modern development strategies. In the field of autonomy, developing a system-of-systems which can ostensibly think for itself in the face of ‘unknown unknowns’ is still a field of ongoing research. Maturing the systems architecting and modeling methodologies for developing henceforth named Thinking Autonomous Systems, which are verified with digital mission simulation, can potentially usher in the next generation of artificial intelligence for space exploration. The concept presented in this paper incorporates multiple Model-Based Systems Engineering and simulation methodologies combined as a new paradigm to design a novel, biomimetic thinking autonomy strategy. Anachronistic concepts from classical Kantian philosophy will be leveraged to inspire architectural designs that could be used for complex distributed systems in deep space. To accomplish this, digital transformation of a document-based implementation plan for Thinking Autonomous Systems, generated by experienced NASA software engineers, is implemented for NASA’s Platform for Autonomous Systems by creating descriptive and executable software models in SysML to prototype real-time operating capabilities. This conceptual implementation has been developed by incorporating model-based digital simulations to theorize how a cyberphysical thinking system would achieve specific strategies without crew reliance, while simultaneously being resilient to all operating conditions and remaining functional when devoid of ground communication. Additionally, ensuring that an autonomous system framework is an ethical Artificial Intelligence requires careful consideration of system behavior and accountability, human factors for teaming with a thinking autonomous system, and comparison to other modern approaches used for implementing true autonomy. This paper presents the first steps in formalizing the metacognition required for instantiating a truly Thinking Autonomous System; the approach described symphonizes autonomy characteristics from classical philosophical into a unified software architecture describing human thought. In the future, the foundational models described in this paper can be further leveraged to help advance research into thinking autonomy requirements for future deep space missions as well as for current near-term applications, i.e., living aboard crewed spacecraft like a NASA Gateway cislunar habitat.

Artificial Thought↗
NASA NTRS2007

Autonomic Computing for Spacecraft Ground Systems

Autonomic computing for spacecraft ground systems increases the system reliability and reduces the cost of spacecraft operations and software maintenance. In this paper, we present an autonomic computing solution for spacecraft ground systems at NASA Goddard Space Flight Center (GSFC), which consists of an open standard for a message oriented architecture referred to as the GMSEC architecture (Goddard Mission Services Evolution Center), and an autonomic computing tool, the Criteria Action Table (CAT). This solution has been used in many upgraded ground systems for NASA 's missions, and provides a framework for developing solutions with higher autonomic maturity.

Li, Zhenping↗
NASA NTRS2004

Low Gravity Materials Science Research for Space Exploration

On January 14, 2004, the President of the United States announced a new vision for the United States civil space program. The Administrator of the National Aeronautics and Space Administration (NASA) has the responsibility to implement this new vision. The President also created a Presidential Commission 'to obtain recommendations concerning implementation of the new vision for space exploration.' The President's Commission recognized that achieving the exploration objectives would require significant technical innovation, research, and development in focal areas defined as 'enabling technologies.' Among the 17 enabling technologies identified for initial focus were advanced structures; advanced power and propulsion; closed-loop life support and habitability; extravehicular activity system; autonomous systems and robotics; scientific data collection and analysis; biomedical risk mitigation; and planetary in situ resource utilization. The Commission also recommended realignment of NASA Headquarters organizations to support the vision for space exploration. NASA has aggressively responded in its planning to support the vision for space exploration and with the current considerations of the findings and recommendations from the Presidential Commission. This presentation will examine the transformation and realignment activities to support the vision for space exploration that are underway in the microgravity materials science program. The heritage of the microgravity materials science program, in the context of residence within the organizational structure of the Office of Biological and Physical Research, and thematic and sub-discipline based research content areas, will be briefly examined as the starting point for the ongoing transformation. Overviews of future research directions will be presented and the status of organizational restructuring at NASA Headquarters, with respect to influences on the microgravity materials science program, will be discussed. Additional information is included in the original extended abstract.

Clinton, R. G., Jr.↗
NASA NTRS2005

Research Opportunities Supporting the Vision for Space Exploration from the Transformation of the Former Microgravity Materials Science Program

The Microgravity Materials Science Program established a strong research capability through partnerships between NASA and the scientific research community. With the announcement of the vision for space exploration, additional emphasis in strategic materials science areas was necessary. The President's Commission recognized that achieving its exploration objectives would require significant technical innovation, research, and development in focal areas defined as "enabling technologies." Among the 17 enabling technologies identified for initial focus were: advanced structures, advanced power and propulsion; closed-loop life support and habitability; extravehicular activity systems; autonomous systems and robotics; scientific data collection and analysis, biomedical risk mitigation; and planetary in situ resource utilization. Mission success may depend upon use of local resources to fabricate a replacement part to repair a critical system. Future propulsion systems will require materials with a wide range of mechanical, thermophysical, and thermochemical properties, many of them well beyond capabilities of today's materials systems. Materials challenges have also been identified by experts working to develop advanced life support systems. In responding to the vision for space exploration, the Microgravity Materials Science Program aggressively transformed its research portfolio and focused materials science areas of emphasis to include space radiation shielding; in situ fabrication and repair for life support systems; in situ resource utilization for life support consumables; and advanced materials for exploration, including materials science for space propulsion systems and for life support systems. The purpose of this paper is to inform the scientific community of these new research directions and opportunities to utilize their materials science expertise and capabilities to support the vision for space exploration.

Clinton, R. G., Jr.↗
NASA NTRS2010

NASA Tech Briefs, September 2010

Topics covered include: Instrument for Measuring Thermal Conductivity of Materials at Low Temperatures; Multi-Axis Accelerometer Calibration System; Pupil Alignment Measuring Technique and Alignment Reference for Instruments or Optical Systems; Autonomous System for Monitoring the Integrity of Composite Fan Housings; A Safe, Self-Calibrating, Wireless System for Measuring Volume of Any Fuel at Non-Horizontal Orientation; Adaptation of the Camera Link Interface for Flight-Instrument Applications; High-Performance CCSDS Encapsulation Service Implementation in FPGA; High-Performance CCSDS AOS Protocol Implementation in FPGA; Advanced Flip Chips in Extreme Temperature Environments; Diffuse-Illumination Systems for Growing Plants; Microwave Plasma Hydrogen Recovery System; Producing Hydrogen by Plasma Pyrolysis of Methane; Self-Deployable Membrane Structures; Reactivation of a Tin-Oxide-Containing Catalys; Functionalization of Single-Wall Carbon Nanotubes by Photo-Oxidation; Miniature Piezoelectric Macro-Mass Balance; Acoustic Liner for Turbomachinery Applications; Metering Gas Strut for Separating Rocket Stages; Large-Flow-Area Flow-Selective Liquid/Gas Separator; Counterflowing Jet Subsystem Design; Water Tank with Capillary Air/Liquid Separation; True Shear Parallel Plate Viscometer; Focusing Diffraction Grating Element with Aberration Control; Universal Millimeter-Wave Radar Front End; Mode Selection for a Single-Frequency Fiber Laser; Qualification and Selection of Flight Diode Lasers for Space Applications; Plenoptic Imager for Automated Surface Navigation; Maglev Facility for Simulating Variable Gravity; Hybrid AlGaN-SiC Avalanche Photodiode for Deep-UV Photon Detection; High-Speed Operation of Interband Cascade Lasers; 3D GeoWall Analysis System for Shuttle External Tank Foreign Object Debris Events; Charge-Spot Model for Electrostatic Forces in Simulation of Fine Particulates; Hidden Statistics Approach to Quantum Simulations; Reconstituted Three-Dimensional Interactive Imaging; Determining Atmospheric-Density Profile of Titan; Digital Microfluidics Sample Analyzer; Radiation Protection Using Carbon Nanotube Derivatives; Process to Selectively Distinguish Viable from Non-Viable Bacterial Cells; and TEAMS Model Analyzer.

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