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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 NTRS2004

Robust local search for spacecraft operations using adaptive noise

Randomization is a standard technique for improving the performance of local search algorithms for constraint satisfaction. However, it is well-known that local search algorithms are constraints satisfaction. However, it is well-known that local search algorithms are to the noise values selected. We investigate the use of an adaptive noise mechanism in an iterative repair-based planner/scheduler for spacecraft operations. Preliminary results indicate that adaptive noise makes the use of randomized repair moves safe and robust; that is, using adaptive noise makes it possible to consistently achieve, performance comparable with the best tuned noise setting without the need for manually tuning the noise parameter.

planning↗
NASA NTRSDate not supplied

Advancing Autonomy in Distributed Space Systems: Insights From on-Orbit Testing with the Starling 1.0 Mission

Autonomous decision-making is crucial for enhancing mission effectiveness in Distributed Space Systems (DSS), particularly in multi-spacecraft operations where communication constraints and mission complexity pose challenges. The Distributed Spacecraft Autonomy (DSA) team at NASA’s Ames Research Center is advancing autonomy in DSS through five key technical areas: distributed resource and task management, reactive operations, system modeling and simulation, human-swarm interaction, and ad hoc network communications. The DSA experiment onboard the Starling 1.0 Mission showcases collaborative resource allocation for multi-point science data collection with four small spacecraft. Autonomy in decision-making is highlighted as a crucial factor for multi-spacecraft missions, enabling spacecraft to operate independently, reducing reliance on ground control. This capability is particularly significant for future deep-space missions, where communication delays and limited data transmission capacity make traditional command and control approaches impractical. This demonstration focuses on a GPS Channel Selection Experiment, leveraging emergent capabilities like "shared sampling" and "simultaneous sampling" to optimize channel selection across the spacecraft swarm. The experiment aims to capture ionospheric phenomena such as the Equatorial Ionization Anomaly and Polar Patches. The DSA system's autonomous reconfiguration ability is showcased, emphasizing its adaptability to natural phenomena without significant integration efforts. The GPS Channel Selection Experiment utilizes a dual-band GPS receiver to estimate plasma density in the ionosphere. Explorative and exploitative channel selections are employed based on the nature of observed phenomena. The performance of DSA algorithms is evaluated in terms of optimal channel allocations and responsiveness to changes in observed features. The DSA Flight Software utilizes the Core Flight System (cFS) framework, ensuring compatibility with the Starling 1.0 flight mission software. DSA showcases results from RTI’s Connext DDS Micro communication middleware, enabling message routing over the Ad-Hoc Network of Starling 1.0. This paper provides a comprehensive overview of the DSA experiment's initial results, emphasizing the advancements in autonomy for Distributed Space Systems and the successful collaboration with the Starling 1.0 mission.

Caleb Ashmore Adams↗
NASA NTRS2008

The Telesupervised Adaptive Ocean Sensor Fleet (TAOSF) Architecture: Coordination of Multiple Oceanic Robot Boats

Earth science research must bridge the gap between the atmosphere and the ocean to foster understanding of Earth s climate and ecology. Ocean sensing is typically done with satellites, buoys, and crewed research ships. The limitations of these systems include the fact that satellites are often blocked by cloud cover, and buoys and ships have spatial coverage limitations. This paper describes a multi-robot science exploration software architecture and system called the Telesupervised Adaptive Ocean Sensor Fleet (TAOSF). TAOSF supervises and coordinates a group of robotic boats, the OASIS platforms, to enable in-situ study of phenomena in the ocean/atmosphere interface, as well as on the ocean surface and sub-surface. The OASIS platforms are extended deployment autonomous ocean surface vehicles, whose development is funded separately by the National Oceanic and Atmospheric Administration (NOAA). TAOSF allows a human operator to effectively supervise and coordinate multiple robotic assets using a sliding autonomy control architecture, where the operating mode of the vessels ranges from autonomous control to teleoperated human control. TAOSF increases data-gathering effectiveness and science return while reducing demands on scientists for robotic asset tasking, control, and monitoring. The first field application chosen for TAOSF is the characterization of Harmful Algal Blooms (HABs). We discuss the overall TAOSF architecture, describe field tests conducted under controlled conditions using rhodamine dye as a HAB simulant, present initial results from these tests, and outline the next steps in the development of TAOSF.

sensor web↗
NASA NTRS2017

Advanced Software V&V for Civil Aviation and Autonomy

With the advances in high-computing platform (e.g., advanced graphical processing units or multi-core processors), computationally-intensive software techniques such as the ones used in artificial intelligence or formal methods have provided us with an opportunity to further increase safety in the aviation industry. Some of these techniques have facilitated building safety at design time, like in aircraft engines or software verification and validation, and others can introduce safety benefits during operations as long as we adapt our processes. In this talk, I will present how NASA is taking advantage of these new software techniques to build in safety at design time through advanced software verification and validation, which can be applied earlier and earlier in the design life cycle and thus help also reduce the cost of aviation assurance. I will then show how run-time techniques (such as runtime assurance or data analytics) offer us a chance to catch even more complex problems, even in the face of changing and unpredictable environments. These new techniques will be extremely useful as our aviation systems become more complex and more autonomous.

software assurance↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge and support human space missions. Through artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in space biosciences and engineered astronaut health systems, to enable Earth-independence and mission operations autonomy. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated mission biomonitoring, and 8) a Precision Space Health system. AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the space biology field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics to phenotypic data using an ensemble model to infer causality of rodent liver health disruption, 2) usage of explainable ML to interrogate muscular underpinnings of muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interactions, and 5) a suite of benchmarked open science datasets enabling programmers to identify best algorithms to answer space biology questions.

space biology↗
NASA NTRS1987

Dual arm robotic system with sensory input

The need for dual arm robots in space station assembly and satellite maintainance is of increasing significance. Such robots will be in greater demand in the future when numerous tasks will be assigned to them to relieve the direct intervention of humans in space. Technological demands from these robots will be high. They will be expected to perform high speed tasks with a certain degree of autonomy. Various levels of sensing will have to be used in a sophisticated control scheme. Ongoing research in control, sensing and real-time software to produce a two-arm robotic system than can accomplish generic assembly tasks is discussed. The control hierarchy and the specific control approach are discussed. A decentralized implementation of model-reference adaptive control using Variable Structure controllers and the incorporation of tactile feedback is considered.

Ozguner, U.↗
NASA NTRS2004

Human-Interaction Challenges in UAV-Based Autonomous Surveillance

Autonomous UAVs provide a platform for intelligent surveillance in application domains ranging from security and military operations to scientific information gathering and land management. Surveillance tasks are often long duration, requiring that any approach be adaptive to changes in the environment or user needs. We describe a decision- theoretic model of surveillance, appropriate for use on our autonomous helicopter, that provides a basis for optimizing the value of information returned by the UAV. From this approach arise a range of challenges in making this framework practical for use by human operators lacking specialized knowledge of autonomy and mathematics. This paper describes our platform and approach, then describes human-interaction challenges arising from this approach that we have identified and begun to address.

Freed, Michael↗
NASA NTRS2019

Quantum Technologies for UAS (QTech)

Recent advances in small Unmanned Aerial System (sUAS) technologies lower the barriers for use by both private and commercial entities. However, these advances are also likely to lead to greater vehicle densities, a more heterogenous mix of vehicles and equipment and greater levels of vehicle autonomy, which can increase the chance for communications disruptions. For the safe and secure operation of these vehicles, it is essential to have a robust communications network. This work is focused on harnessing the power of quantum technologies to enable this robust communications network by: (1) utilizing quantum optimization algorithms to design robust network with routing redundancy that can respond adaptively to dynamically changing real-time environment and disruptions, (2) utilize quantum optimization algorithms resource allocation for detection, localization, and tracking of mobile communication disruption agents and (3) utilize quantum key distribution (QKD) to execute secure key sharing in anti-jamming protocols for secure radio frequency (RF) communication. Efforts to map these quantum optimization algorithms to commercially available quantum annealers and soon to be available general-purpose gate-model quantum hardware architectures will be reviewed, and plans for testing the solutions to these algorithms through indoor sUAS flight tests will be discussed. Lastly, efforts to miniaturize and practically deploy Quantum Key Distribution (QKD) hardware, which could ultimately be used to securely exchange encryption keys, in sUAS networks will be reviewed.

Quantum Computing↗
NASA NTRS2006

Controlling Herds of Cooperative Robots

A document poses, and suggests a program of research for answering, questions of how to achieve autonomous operation of herds of cooperative robots to be used in exploration and/or colonization of remote planets. In a typical scenario, a flock of mobile sensory robots would be deployed in a previously unexplored region, one of the robots would be designated the leader, and the leader would issue commands to move the robots to different locations or aim sensors at different targets to maximize scientific return. It would be necessary to provide for this hierarchical, cooperative behavior even in the face of such unpredictable factors as terrain obstacles. A potential-fields approach is proposed as a theoretical basis for developing methods of autonomous command and guidance of a herd. A survival-of-the-fittest approach is suggested as a theoretical basis for selection, mutation, and adaptation of a description of (1) the body, joints, sensors, actuators, and control computer of each robot, and (2) the connectivity of each robot with the rest of the herd, such that the herd could be regarded as consisting of a set of artificial creatures that evolve to adapt to a previously unknown environment. A distributed simulation environment has been developed to test the proposed approaches in the Titan environment. One blimp guides three surface sondes via a potential field approach. The results of the simulation demonstrate that the method used for control is feasible, even if significant uncertainty exists in the dynamics and environmental models, and that the control architecture provides the autonomy needed to enable surface science data collection.

Quadrelli, Marco B.↗
NASA NTRS1984

Psychological Issues in Online Adaptive Task Allocation

Adaptive aiding is an idea that offers potential for improvement over many current approaches to aiding in human-computer systems. The expected return of tailoring the system to fit the user could be in the form of improved system performance and/or increased user satisfaction. Issues such as the manner in which information is shared between human and computer, the appropriate division of labor between them, and the level of autonomy of the aid are explored. A simulated visual search task was developed. Subjects are required to identify targets in a moving display while performing a compensatory sub-critical tracking task. By manipulating characteristics of the situation such as imposed task-related workload and effort required to communicate with the computer, it is possible to create conditions in which interaction with the computer would be more or less desirable. The results of preliminary research using this experimental scenario are presented, and future directions for this research effort are discussed.

Morris, N. M.↗
NASA NTRS2017

Measuring the Effectiveness of Human Autonomy Teaming

Human-Automation Teaming (HAT), is now recognized as a promising solution to the problems of humans managing increasingly complex work systems. A human-automation team can be defined as the interdependent coupling between one or more human operators and one or more autonomous systems requiring collaboration and coordination to accomplish system and task goals (e.g., Langan-Fox et al., 2009). In this conception, automated agents are considered team members that can operate at various levels of automation, be focused on one or more human-information-processing stages, and the interactions with human operators may be adaptable, adjustable or mixed initiative. We investigated some metrics for assessing HAT effectiveness in a demonstration of a HAT tool used by ground station operators in a Reduced Crew Operations project that was conducted at NASA Ames Human Automation Teaming Laboratory. In this paper, we focus on operator metrics of HAT effectiveness, specifically workload and operator behaviors.

measurements↗
NASA NTRS2017

Autonomous, Context-Sensitive, Task Management Systems and Decision Support Tools I: Human-Autonomy Teaming Fundamentals and State of the Art

Recent advances in artificial intelligence, machine learning, data mining and extraction, and especially in sensor technology have resulted in the availability of a vast amount of digital data and information and the development of advanced automated reasoners. This creates the opportunity for the development of a robust dynamic task manager and decision support tool that is context sensitive and integrates information from a wide array of on-board and off aircraft sourcesa tool that monitors systems and the overall flight situation, anticipates information needs, prioritizes tasks appropriately, keeps pilots well informed, and is nimble and able to adapt to changing circumstances. This is the first of two companion reports exploring issues associated with autonomous, context-sensitive, task management and decision support tools. In the first report, we explore fundamental issues associated with the development of an integrated, dynamic, flight information and automation management system. We discuss human factors issues pertaining to information automation and review the current state of the art of pilot information management and decision support tools. We also explore how effective human-human team behavior and expectations could be extended to teams involving humans and automation or autonomous systems.

context-sensitive↗
NASA NTRSDate not supplied

Supervised Autonomous Assembly to Create and Evolve Persistent Assets

Supervised autonomous assembly (SAA) will create a paradigm shift in the planning and design of future persistent assets (PAs), both in near zero-g environments and on planetary surfaces. SAA refers to an autonomy approach that has the benefits of autonomous assembly as well as the benefits provided by a supervisor (operator) who is available to resolve unexpected situations. SAA provides both increased design freedom as well as reduced programmatic risk. SAA enables evolution of future PAs over decades as in-space operations transition from single purpose missions to creation of PAs, such as laboratories and experimental stations which more closely resembling terrestrial laboratories that can easily adapt and evolve to new missions leveraging repeated visits to the PA. The ability to evolve enables PAs to rapidly respond to changing objectives resulting from new questions as our understanding improves. A recently initiated National Aeronautics and Space Administration (NASA) project in the Space Technology Mission Directorate (STMD) Game Changing Development (GCD) Program called the Precision Assembled Space Structure (PASS), leverages the advantages of SAA to develop technologies that enable efficient creation and evolution of hexagonal topologies; both planar (example: fuel depots) and curved (examples: telescopes and shelters). PASS will be used to provide context for the philosophy and concepts discussed as well as the decision and selections made. PASS objectives are: a) Develop confidence in SAA and on-orbit servicing, assembly and manufacturing (OSAM) technologies by executing a test campaign that uses a path-to-flight autonomous precision assembly process directly applicable to future space telescopes. b) Test autonomous technologies including automated path planning and error recovery, to emphasize a robust approach that relies on generic robots and special purpose tools. c) Validate critical component models using a digital twin that includes the assembled primary mirror support structure and assembly process. A digital twin is a high-fidelity simulation of the asset capable of predicting the on-orbit performance. The paper concludes after identifying the critical need for a modest assembly flight experiment to validate and develop confidence in the SAA paradigm, thus accelerating adoption of the benefits described. SAA is a game changing paradigm that enhances the ability of an organization to infuse new technology through rapid evolution of PAs while leveraging OSAM technologies.

Structural Modeling↗
NASA NTRS2018

Serious Gaming for Building a Basis of Certification via Trust and Trustworthiness of Autonomous Systems

Autonomous systems governed by a variety of adaptive and nondeterministic algorithms are being planned for inclusion into safety-critical environments, such as unmanned aircraft and space systems in both civilian and military applications. However, until autonomous systems are proven and perceived to be capable and resilient in the face of unanticipated conditions, humans will be reluctant or unable to delegate authority, remaining in control aided by machine-based information and decision support. Proving capability, or trustworthiness, is a necessary component of certification. Perceived capability is a component of trust. Trustworthiness is an attribute of a cyber-physical system that requires context-driven metrics to prove and certify. Trust is an attribute of the agents participating in the system and is gained over time and multiple interactions through trustworthy behavior and transparency. Historically, artificial intelligence and machine learning systems provide answers without explanation - without a rationale or insight into the machine “thinking”. In order to function as trusted teammates, machines must be able to explain their decisions and actions. This transparency is a product of both content and communication. NASA’s Autonomy Teaming & TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project seeks to build a basis for certification of autonomous systems via establishing metrics for trustworthiness and trust in multi-agent team interactions, using AI (Artificial Intelligence) explainability and persistent modeling and simulation, in the context of mission planning and execution, with analyzable trajectories. Inspired by Massively Multiplayer Online Role Playing Games (MMORPG) and Serious Gaming, the proposed ATTRACTOR modeling and simulation environment is similar to online gaming environments in which player (aka agent) participants interact with each other, affect their environment, and expect the simulation to persist and change regardless of any individual agent’s active participation. This persistent simulation environment will accommodate individual agents, groups of self-organizing agents, and large-scale infrastructure behavior. The effects of the emerging adaptation and coevolution can be observed and measured to building a basis of measurable trustworthiness and trust, toward certification of safety-critical autonomous systems.

Allen, B. Danette↗
NASA NTRSDate not supplied

Automated Reconfigurable Mission Adaptive Digital Assembly Systems (ARMADAS): Robotically Assembled Sustainable Lunar Infrastructure

Introduction: The Automated Reconfigurable Mission Adaptive Digital Assembly Systems (ARMADAS) project at NASA Ames Research Center is developing autonomous infrastructure, instrumentation, and spacecraft assembly and manufacturing capabilities for next generation exploration and science missions, with a goal to change the cost scaling of these missions relative to mission size and duration. Using a building-block approach with a 'kit of parts' composed of ultra-light, high-performance mechanical metamaterials, simplified robots leverage the period environment to achieve high levels of autonomy and reliability for in-space and surface assembly of large-scale apertures, solar-arrays, towers, habitats, and other infrastructure. Robots and structure break down into a compact form factor for launch. By leveraging economies of scale and achieving high-packing ratios, ARMADAS technology can revolutionize possible space missions by breaking the tyranny of the launch shroud, decreasing development times, decreasing mission costs for transformative science capability, and providing a scalable and versatile space infrastructure strategy. Capabilities: To date, the ARMADAS project has demonstrated autonomous assembly of large numbers of structural modules into a meters-scale structure in an earth gravity environment. The mechanical performance is on par with conventional space structures, at a fraction of the cost. By using highly repeatable manufacturing processes (injection molding carbon fiber reinforced space-rated polymers), the structures are both cost effective and highly precise. The ARAMADAS system carefully designs parts for high-precision assembly–the simple and cost-effective robots build structures much larger and more precise than themselves. While maintaining structural efficiency, the ARMADAS system encompasses many functional module types, including for solar power, comm/power routing, etc., that will enable entire infrastructure systems to be engineered and autonomously assembled with modular parts. With well-defined interfaces, custom instrumentation modules can easily be added to fit many mission objectives. Lunar Infrastructure Vision: At the LSIC spring meeting, ARMADAS will present a vision for a general-purpose lunar construction kit capable of meeting a wide variety of lunar surface infrastructure needs (Figure 1). Investing in an ecosystem of reconfigurable, discretely repairable parts and robots enables systems than can expand their capability, reconfigure to meet emergency or unforeseen needs, self-repair and reduce spare-part needs. With a small set of parts (module types), a wide variety of infrastructure needs can be met. Footing modules will allow infrastructure construction at locations with no surface preparation. Footer, primary structure, and rail modules can create reconfigurable rail systems. These rail systems can be used to reduce cost-of-transport between frequently accessed sites, provide dust mitigation, and convey power and communications. High-performance structure modules combined with power-routing and solar modules can create tall towers for power and communication. Concepts for habitats and garages encompass a structure to support regolith cover for a pre-integrated module to an entirely ARMADAS system-based structure. This system can be leveraged by many in-situ resource utilization (ISRU) technologies to simplify processes and augment capabilities.

C. E. Gregg↗
NASA NTRSDate not supplied

Autonomous Lunar Infrastructure Outfitting

The Automated Reconfigurable Mission Adaptive Digital Assembly Systems (ARMADAS) project at NASA Ames Research Center is developing autonomous infrastructure, instrumentation, and spacecraft assembly and manufacturing capabilities for next generation exploration and science missions, with a goal to change the cost scaling of these missions relative to mission size and duration. Using a building-block approach with a 'kit of parts' composed of ultra-light, high-performance mechanical metamaterials, simplified robots leverage the period environment to achieve high levels of autonomy and reliability for in-space and surface assembly of large-scale apertures, solar-arrays, towers, habitats, and other infrastructure. Robots and structure break down into a compact form factor for launch. By leveraging economies of scale and achieving high-packing ratios, ARMADAS technology can revolutionize space missions by breaking the tyranny of the launch shroud, decreasing development times, decreasing mission costs for transformative science capability, and providing a scalable and versatile space infrastructure strategy. An ecosystem of reconfigurable infrastructure modules can be reused, repaired, expanded reconfigured to meet emergency or unforeseen needs, and reduce spare parts.

Christine Gregg↗
NASA NTRS2009

Benchmarking Diagnostic Algorithms on an Electrical Power System Testbed

Diagnostic algorithms (DAs) are key to enabling automated health management. These algorithms are designed to detect and isolate anomalies of either a component or the whole system based on observations received from sensors. In recent years a wide range of algorithms, both model-based and data-driven, have been developed to increase autonomy and improve system reliability and affordability. However, the lack of support to perform systematic benchmarking of these algorithms continues to create barriers for effective development and deployment of diagnostic technologies. In this paper, we present our efforts to benchmark a set of DAs on a common platform using a framework that was developed to evaluate and compare various performance metrics for diagnostic technologies. The diagnosed system is an electrical power system, namely the Advanced Diagnostics and Prognostics Testbed (ADAPT) developed and located at the NASA Ames Research Center. The paper presents the fundamentals of the benchmarking framework, the ADAPT system, description of faults and data sets, the metrics used for evaluation, and an in-depth analysis of benchmarking results obtained from testing ten diagnostic algorithms on the ADAPT electrical power system testbed.

Kurtoglu, Tolga↗