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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Li-ion cells for terrestrial robots

SAFT prismatic wound 5 Ahr MP series cells were evaluated for potential application in a lithium ion battery designed for Tactical Mobile Robots (TMR). In order to satisfy battery design requirements, a 10 Ahr battery containing two parallel 8-cell strings was proposed. The proposed battery has a weight and volume of approximately 3.2kg and 1.6 liters, respectively. Cell qualification procedures include initial characterization, followed by charge/discharge cycling at 100% DOD with intermittent EIS measurements at various state of charge. Certain cells were also subjected to extreme operational temperatures for worst-case analysis. Excellent specific energy (>130 Whr/kg) was obtained with initial characterization cycles. Even at abusive thermal conditions, the cell capacity fade was less than Ahr after 300 cycles. Rate characterization showed good cell discharge behavior with minimal decrease in capacity. At various state of charge, impedance measurements suggest that the cathode play a more significant role in capacity. At various state of charge impedance measurements suggest that the cathode play a more significant role in capacity fade than the anode.

robots↗
DOE OSTI2024

Multi‐Material Gradient Printing Using Meniscus‐enabled Projection Stereolithography (MAPS)

Light‐based additive manufacturing methods are widely used to print high‐resolution 3D structures for applications in tissue engineering, soft robotics, photonics, and microfluidics, among others. Despite this progress, multi‐material printing with these methods remains challenging due to constraints associated with hardware modifications, control systems, cross‐contamination, waste, and resin properties. Here, a new printing platform coined Meniscus‐enabled Projection Stereolithography (MAPS) is reported, a vat‐free method that relies on generating and maintaining a resin meniscus between a crosslinked structure and bottom window to print lateral, vertical, discrete, or gradient multi‐material 3D structures with no waste and user‐defined mixing between layers. MAPS is compatible with a wide range of resins shown and can print complex multi‐material 3D structures without requiring specialized hardware, software, or complex washing protocols. MAPS's ability to print structures with microscale variations in mechanical stiffness, opacity, surface energy, cell densities, and magnetic properties provides a generic method to make advanced materials for a broad range of applications.

bioprinting↗
NASA NTRS2005

Learning for Autonomous Navigation

Robotic ground vehicles for outdoor applications have achieved some remarkable successes, notably in autonomous highway following (Dickmanns, 1987), planetary exploration (1), and off-road navigation on Earth (1). Nevertheless, major challenges remain to enable reliable, high-speed, autonomous navigation in a wide variety of complex, off-road terrain. 3-D perception of terrain geometry with imaging range sensors is the mainstay of off-road driving systems. However, the stopping distance at high speed exceeds the effective lookahead distance of existing range sensors. Prospects for extending the range of 3-D sensors is strongly limited by sensor physics, eye safety of lasers, and related issues. Range sensor limitations also allow vehicles to enter large cul-de-sacs even at low speed, leading to long detours. Moreover, sensing only terrain geometry fails to reveal mechanical properties of terrain that are critical to assessing its traversability, such as potential for slippage, sinkage, and the degree of compliance of potential obstacles. Rovers in the Mars Exploration Rover (MER) mission have got stuck in sand dunes and experienced significant downhill slippage in the vicinity of large rock hazards. Earth-based off-road robots today have very limited ability to discriminate traversable vegetation from non-traversable vegetation or rough ground. It is impossible today to preprogram a system with knowledge of these properties for all types of terrain and weather conditions that might be encountered.

mixture of Gaussians↗
DOE OSTI2022

Evolutionary vs imitation learning for neuromorphic control at the edge*

Abstract Neuromorphic computing offers the opportunity to implement extremely low power artificial intelligence at the edge. Control applications, such as autonomous vehicles and robotics, are also of great interest for neuromorphic systems at the edge. It is not clear, however, what the best neuromorphic training approaches are for control applications at the edge. In this work, we implement and compare the performance of evolutionary optimization and imitation learning approaches on an autonomous race car control task using an edge neuromorphic implementation. We show that the evolutionary approaches tend to achieve better performing smaller network sizes that are well-suited to edge deployment, but they also take significantly longer to train. We also describe a workflow to allow for future algorithmic comparisons for neuromorphic hardware on control applications at the edge.

Schuman, Catherine↗
NASA NTRS2022

NASA System-Wide Safety Wildland Firefighting Operations Workshop Report

On March 9-11, 2022, NASA’s System-Wide Safety Wildland Firefighting Operations Workshop engaged the broader wildland firefighting management ecosystem in a safety-oriented discussion via a virtual platform. This enabled a better understanding of how NASA and community expertise can be leveraged in the safe development of current and future firefighting systems and operations. The goals of the workshop were to: (1) identify and prioritize the top safety-oriented risks, gaps in capabilities, and emerging technologies to enhance wildland firefighting for both near-term and far-term concepts, with a specific focus on aviation operations and (2) engage the stakeholder community in defining emergent safety-oriented scope, roles, responsibilities, and procedures for agents undergoing increasingly complex wildland firefighting operations in information-rich, but uncertain environments. Workshop participants were solicited from wildland firefighting stakeholders across government, industry, and academia. All levels of government were engaged, as NASA sought attendees from federal, state, local, and tribal government agencies. Industry participants from traditional wildland firefighting domains such as data visualization and equipment manufacturers were invited, and corporate attendees from novel application domains such as aerial robotics and autonomous systems were present as well. The top three findings were as follows: (1) Enhancing situation awareness is a safety priority, especially in the use of aerial assets; (2) Timely access to information along with data fusion and integrated displays will enhance safety-critical decision-making both inside and outside aviation contexts; and (3) Tailorable standards and common operating pictures in the field will enhance inter-agency cooperation in the wildland firefighting lifecycle and enable the optimal use of limited resources such as aerial assets. The workshop helped inform NASA of the relevant safety-related wildland firefighting concerns and aided the broader ecosystem in understanding the potential safety-oriented role NASA might play in this community. Increased engagement with crucial governmental stakeholders (e.g., U.S. Forest Service, CAL FIRE, etc.) along with industry partners in cutting- edge information -centric domains is a fundamental next step. Additionally, the workshop findings will help define the first of a series of operationally challenging demonstrations, held in concert with strategic ecosystem partners, known as the Safety Demonstrator Series for NASA’s System-Wide Safety project. The first demonstration is set in the wildland firefighting application domain and will: (1) examine high risk operational scenarios to reduce their overall risk via services, functions or capabilities that act as risk mitigators (or transfer that risk to automated systems better able to tolerate it) and (2) explore novel tools and technologies that will enhance safety margins by enabling non-traditional or neoteric operational paradigms.

wildland firefighting↗
NASA NTRS2022

A High Voltage Tethered Power System for Planetary Surface Applications

In the pursuit of human and robotic expansion of theLunar surface and other planetary bodies, power conversionand transmission over long distances has emerged as a distinctneed. The ability to transmit power over kilometers at highefficiency enables human settlement, in-situ resource utilization,and extreme terrain robotic exploration, through the ability toreach previously unreachable places. The critical buildingblocks of such a system are being developed by the TetheredPower System for Lunar Mobility and Power Transmission(TYMPO) project, a NASA project aimed at reaching flightreadiness by 2024. This paper presents an architectural andrequirements overview of the TYMPO project, along withtechnology discussions on the GaN-based multilevel DCXconverters, dual fiber optics and power-line communicationssystem, and high-voltage tether. Finally, a set of proposed futuremissions will be presented, mapping the capabilities of theTYMPO system to missions that may soon explore planetarysurfaces throughout the solar system.

Yang, Summer↗
DOE OSTI2026

Knowledge gaps for neuromorphic ionic computing

BACKGROUND Neuromorphic computing, inspired by the human brain’s ability to process information efficiently, represents a transformative approach to computation. In this Review, we explore the emerging field of neuromorphic ionic computing, which leverages ionic conduction and coupling to mimic neural processes, and identify critical knowledge gaps that must be addressed to realize its full potential. A central theme of the discussion is energy efficiency, a challenge that is both a limitation and an opportunity for this technology. Although complementary metal-oxide semiconductor (CMOS)–based neuromorphic technologies have made strides in scaling to billions of neurons and are increasingly applied in artificial intelligence and numerical computing, they remain orders of magnitude behind the human brain in terms of connectivity and energy efficiency. Neuromorphic ionic computing promises to overcome these limitations by leveraging the distinct architectural and operational principles of the brain. Our brains achieve this energy efficiency by combining several key features: using the same network elements to store and process information; using an incredibly complex and massively interconnected three-dimensional (3D) network of locally active elements that enables sparsity, robustness in the presence of noise, adaptation, and life-long learning; computing at comparatively low voltage and frequency; and last, taking advantage of a plethora of ions and small molecules as information carriers. Here, we propose that ionic computing systems can take advantage of similar features to achieve substantial gains in energy efficiency. ADVANCES Since the first reports of neuromorphic ionic behavior in nanofluidic channels, we have witnessed an explosion of reports that used ionic devices to produce synaptomimetic behaviors. However, achieving the goals of ionic computing requires not only implementation of much more sophisticated device functionality but also overcoming fundamental barriers in materials science, device architecture, and system integration. Current ionic devices, even those incorporating state-of-the-art materials, still suffer from limited functionality and stability, which restrict their performance and increase energy demands. Developing new materials with enhanced ionic properties is essential to overcome these limitations. Similarly, the design of neuromorphic devices must evolve to leverage the particular advantages of ionic processes. Existing architectures often follow a single-information-carrier logic of conventional electronics or are constructed of mesoscale fluidics, failing to capitalize on the energy-efficient mechanisms inherent to ionic systems or implement the multiple-information-carrier paradigm. Current neuromorphic chips focus on large-scale networks of analog memory elements based on mechanisms such as charge trap (flash), filamentary, phase change, or spin, which are built on top of a network of artificial CMOS neurons. Although such prototype networks have achieved impressive performance, it is difficult to envision how they can implement the key features such as massive connectivity, sophisticated plasticity, adaptability, sparsity, and “multichromatic” computing. Although small-scale devices have demonstrated promising results, integrating them, maintaining energy efficiency, and implementing temperature control as systems grow in complexity and size to computationally relevant scale remain major hurdles. Furthermore, interfacing neuromorphic ionic devices with existing computing technologies presents technical and conceptual challenges that will require innovative approaches that combine insights from neuroscience, materials science, and engineering. OUTLOOK Despite these challenges, the potential impact of neuromorphic ionic computing is profound with potential applications ranging from artificial intelligence to robotics and beyond. We also argue that neuromorphic ionic computing systems should not, at least in the beginning, compete with CMOS technologies but rather should focus on applications that require extreme energy efficiency with chemical and/or biological compatibility, such as biomedical applications (for example, brain-computer interfaces), environmental monitoring, and agricultural and food applications. Ultimately, this Review highlights the crucial role of interdisciplinary collaboration in advancing the field. Neuromorphic ionic computing is not merely a technological innovation; it represents a substantial step toward sustainable computation, aligning with the growing demand for energy-conscious solutions in a world that is increasingly reliant on data and computation.

Neuromorphic↗
NASA NTRS2007

Recent Advances in Nuclear Powered Electric Propulsion for Space Exploration

Nuclear and radioisotope powered electric thrusters are being developed as primary in-space propulsion systems for potential future robotic and piloted space missions. Possible applications for high power nuclear electric propulsion include orbit raising and maneuvering of large space platforms, lunar and Mars cargo transport, asteroid rendezvous and sample return, and robotic and piloted planetary missions, while lower power radioisotope electric propulsion could significantly enhance or enable some future robotic deep space science missions. This paper provides an overview of recent U.S. high power electric thruster research programs, describing the operating principles, challenges, and status of each technology. Mission analysis is presented that compares the benefits and performance of each thruster type for high priority NASA missions. The status of space nuclear power systems for high power electric propulsion is presented. The paper concludes with a discussion of power and thruster development strategies for future radioisotope electric propulsion systems,

Cassady, R. Joseph↗
NASA NTRS2001

Electroactive polymers as artificial muscles - reality and challenges

For many years, electroactive polymers (EAP) received reIatively little attention due to the small number of available materials and their limited actuation capability. The recent emergence of EAP materials with large displacement response changed the potential capability and paradigms of these materials. The main attractive characteristic of EAP is their operational similarity to biological muscles, particularly their resilience and ability to induce large actuation strains. Even though the actuation force of existing EAP materials and their robustness require further improvement, there has already been a series of reported successes. The successful applications that were demonstrated include catheter steering element, miniature manipulator, dust-wiper, miniature robotic arm, and grippers. Some of the currently considered applications may be difficult to accomplish and it is important to scope the requirements to the level that current materials can address. Using EAP to replace existing actuators may be a difficult challenge and therefore it is highly desirable to identify a niche application where it would not need to compete with existing capabilities. The application of these materials as actuators to drive various manipulation, mobility and robotic devices involves multidiscipline including materials, chemistry, electro-mechanics, computers, electronics, etc. This paper reviews the current efforts and the expectations for the future.

Bar-Cohen, Yoseph↗
NASA NTRS2005

Insect-Inspired Optical-Flow Navigation Sensors

Integrated circuits that exploit optical flow to sense motions of computer mice on or near surfaces ( optical mouse chips ) are used as navigation sensors in a class of small flying robots now undergoing development for potential use in such applications as exploration, search, and surveillance. The basic principles of these robots were described briefly in Insect-Inspired Flight Control for Small Flying Robots (NPO-30545), NASA Tech Briefs, Vol. 29, No. 1 (January 2005), page 61. To recapitulate from the cited prior article: The concept of optical flow can be defined, loosely, as the use of texture in images as a source of motion cues. The flight-control and navigation systems of these robots are inspired largely by the designs and functions of the vision systems and brains of insects, which have been demonstrated to utilize optical flow (as detected by their eyes and brains) resulting from their own motions in the environment. Optical flow has been shown to be very effective as a means of avoiding obstacles and controlling speeds and altitudes in robotic navigation. Prior systems used in experiments on navigating by means of optical flow have involved the use of panoramic optics, high-resolution image sensors, and programmable imagedata- processing computers.

Thakoor, Sarita↗
NASA NTRS1988

Experiences with Ada in an embedded system

Recent experiences with using Ada in a real time environment are described. The application was the control system for an experimental robotic arm. The objectives of the effort were to experiment with developing embedded applications in Ada, evaluating the suitability of the language for the application, and determining the performance of the system. Additional objectives were to develop a control system based on the NASA/NBS Standard Reference Model for Telerobot Control System Architecture (NASREM) in Ada, and to experiment with the control laws and how to incorporate them into the NASREM architecture.

Labaugh, Robert J.↗
NASA NTRS1999

Challenges to the Transition of IPMC Artificial Muscle Actuators to Practical Application

Ion-exchange membrane metallic composites (IPMC), which were first reported in 1992, are one of the electroactive materials (EAP) with potential applications as artificial muscle actuators. The recent introduction of perfluorocarboxylate-gold composite with tetra-n-butylammonium and Lithium cations instead of sodium made the most significant improvement of the material electroactivity. Under less than 3 volts, IPMC with the new constituents is capable of bending beyond a complete loop. Taking into account the fact that IMPC materials do not induce a significant force, the authors are extensively seeking applications for these bending EAP. Some of the applications that were demonstrated include dust-wiper, catheter guide, miniature motor, robotic-gripper, micro-manipulator, etc. Generally, space applications are the most demanding in terms of operating conditions, robustness and durability, and the co-authors of this paper are jointly addressing the associated challenges. Specifically, a dust-wiper is being developed for the Nanorover's infrared camera window of the MUSES-CN mission. This joint NASA and the Japanese space agency mission, is scheduled to be launch from Kagoshima, Japan, in January 2002, to explore the surface of a small near-Earth asteroid. Several issues that are critical to the operation of IPMC are addressed including the operation in vacuum, low temperatures, and the effect of the electromechanical characteristic of the IPMC on its actuation capability. Highly efficient IPMC materials, mechanical modeling, unique elements and protective coating were introduced by the authors and are making a high probability the success of the IPMC actuated dust-wiper.

Bar-Cohen, Yoseph↗
NASA NTRS2009

Instruments for Imaging from Far to Near

The acronym CHAMP (signifying camera, hand lens, and microscope ) denotes any of several proposed optoelectronic instruments that would be capable of color imaging at working distances that could be varied continuously through a range from infinity down to several millimeters. As in any optical instrument, the magnification, depth of field, and spatial resolution would vary with the working distance. For example, in one CHAMP version, at a working distance of 2.5 m, the instrument would function as an electronic camera with a magnification of 1/100, whereas at a working distance of 7 mm, the instrument would function as a microscope/electronic camera with a magnification of 4.4. Moreover, as described below, when operating at or near the shortest-working-distance/highest-magnification combination, a CHAMP could be made to perform one or more spectral imaging functions. CHAMPs were originally intended to be used in robotic geological exploration of the Moon and Mars. The CHAMP concept also has potential for diverse terrestrial applications that could include remotely controlled or robotic geological exploration, prospecting, field microbiology, environmental surveying, and assembly- line inspection. A CHAMP (see figure) would include two lens cells: (1) a distal cell corresponding to the objective lens assembly of a conventional telescope or microscope and (2) a proximal cell that would contain the focusing camera lens assembly and the camera electronic image-detector chip, which would be of the active-pixel-sensor (APS) type. The distal lens cell would face outward from a housing, while the proximal lens cell would lie in a clean environment inside the housing. The proximal lens cell would contain a beam splitter that would enable simultaneous use of the imaging optics (that is, proximal and distal lens assemblies) for imaging and illumination of the field of view. The APS chip would be mounted on a focal plane on a side face of the beam splitter, while light for illuminating the field of view would enter the imaging optics via the end face of the beam splitter. The proximal lens cell would be mounted on a sled that could be translated along the optical axis for focus adjustment. The position of the CHAMP would initially be chosen at the desired working distance of the distal lens from (corresponding to an approximate desired magnification of) an object to be examined. During subsequent operation, the working distance would ordinarily remain fixed at the chosen value and the position of the proximal lens cell within the instrument would be adjusted for focus as needed.

Mungas, Greg↗
DOE OSTI2020

Toward Large-Scale Image Segmentation on Summit

Semantic segmentation of images is an important computer vision task that emerges in a variety of application domains such as medical imaging, robotic vision and autonomous vehicles to name a few. While these domain-specific image analysis tasks involve relatively small image sizes (~ 10 2 × 10 2 ), there are many applications that need to train machine learning models on image data with extents that are orders of magnitude larger (~10 4 × 10 4 ). Training deep neural network (DNN) models on large extent images is extremely memory-intensive and often exceeds the memory limitations of a single graphical processing unit, a hardware accelerator of choice for computer vision workloads. Here, an efficient, sample parallel approach to train U-Net models on large extent image data sets is presented. Its advantages and limitations are analyzed and near-linear strong-scaling speedup demonstrated on 256 nodes (1536 GPUs) of the Summit supercomputer. Using a single node of the Summit supercomputer, an early evaluation of a recently released model parallel framework called GPipe is demonstrated to deliver ~ 2X speedup in executing a U-Net model with an order of magnitude larger number of trainable parameters than reported before. Performance bottlenecks for pipelined training of U-Net models are identified and mitigation strategies to improve the speedups are discussed. Together, these results open up the possibility of combining both approaches into a unified scalable pipelined and data parallel algorithm to efficiently train U-Net models with very large receptive fields on data sets of ultra-large extent images.

Seal, Sudip↗
DOE OSTI2026

Toward Fully Soft and Multifunctional Shape Sensing via Optical Waveguide Arrays

For soft bodies, surface deformation and pressure provide proprioceptive and exteroceptive information, including body configuration, body compliance, and external forces. We develop a sheet sensor with a fully soft sensing surface that provides surface shape reconstruction using optical waveguide arrays. The waveguides are fabricated to achieve a tunable linear response to bi‐directional bending curvature, and the waveguide arrays are configured to differentiate between ambiguous shapes. We characterize the waveguide performance, relating curvature sensitivity to the core's surface roughness. Synergy of waveguide responses reduces the number of sensing elements required and achieves damage resilience. Using waveguide sensitivity to pressure, we also demonstrate feasibility for exteroception. We demonstrate the multifunctional sensing capability by wrapping the sheet around an upper arm, showcasing joint motion and external force sensing. Integrated into or applied onto surfaces of robotic or living systems, this design can be implemented in applications such as virtual reality, teleoperation, physical therapy, and soft robotics.

Yu, Qifan [Department of Mechanical Engineering Ma↗
DOE OSTI2020

Advancing Fusion with Machine Learning Research Needs Workshop Report

Abstract Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf ). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Exchangeable Liquid Crystalline Elastomers: Enabling Rapid Processing and Enhanced Actuation Stability through On-Demand Deactivation

Exchangeable liquid crystalline elastomers (xLCEs) bearing dynamic covalent bonds are promising candidates for soft actuators due to their unique capability to adjust both network structure and liquid crystalline (LC) alignment after polymerization. While current xLCEs with low exchange temperatures are convenient for processing, they suffer from issues such as creep and loss of LC alignment during repeated thermal actuation. Herein, we present an effective solution using dynamic anhydride chemistry within a thiol-ene-based xLCE. This approach enables a catalyst-free, low-temperature bond exchange of the xLCE after polymerization, allowing for the adjustment of LC orientation under mild conditions. More importantly, it enables on-demand deactivation of the bond exchange via anhydride hydrolysis, effectively eliminating creep and enhancing actuation stability for over 100 cycles. Furthermore, the hydrolysis process results in the formation of carboxylic acid groups, which can be converted into carboxylates via alkali treatment, thereby providing the xLCE with humidity responsiveness. Finally, these findings highlight the use of dynamic anhydride bonds in the fabrication and optimization of xLCEs with enhanced durability and functionality, which is expected to facilitate significant advancements in their applications in soft actuators and robotics.

36 MATERIALS SCIENCE↗