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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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DOE OSTI2024

Advanced Laboratory and Field Arrays (ALFA)/Lab Collaboration Project (LCP) for Marine Energy (Final Scientific/Technical Report)

The objective of the Advanced Laboratory and Field Arrays (ALFA) project was to reduce the Levelized Cost of Energy (LCOE) of Marine and Hydrokinetic (MHK) energy by leveraging research, development, and testing capabilities at Oregon State University, University of Washington, and the University of Alaska, Fairbanks. ALFA is a project within the Pacific Marine Energy Center (PMEC; formerly NNMREC), a multi-institution entity with a diverse funding base that focuses on research and development for marine renewables. The ALFA project aimed to accelerate the development of next-generation arrays of wave energy conversion (WEC) and tidal energy conversion (TEC) devices through a suite of field-focused R&D activities spanning a broad range of strategic opportunity areas identified in the Funding Opportunity Announcement: • Device and/or array operation and maintenance (O&M) logistics development; • High-fidelity resource characterization and/or modeling technique development and validation; • Array-specific component technology development (e.g. moorings and foundations, transmission, and other offshore grid components); • Array performance testing and evaluation; and • Novel cost-effective environmental monitoring techniques and instrumentation testing and evaluation. The objective of the Lab Collaboration Project (LCP) was to accelerate the development of next-generation marine energy conversion systems. The LCP aimed to achieve these project objectives in collaboration with the national laboratories by: • Developing concept generation and assessment tools; • Improving access to existing testing resources; • Validating collision risk models between fish and turbines; and • Advancing analysis and simulation capabilities for wave-WEC interactions and PTO analysis in nonlinear ocean waves. The ALFA portion of the project was comprised of six overarching technical tasks: • Task 1: Debris Modeling, Detection and Mitigation; • Task 2: Autonomous Monitoring & Intervention; • Task 3: Resource Characterization for Extreme Conditions; • Task 4: Robust Models for Design of Offshore Anchoring and Mooring Systems; • Task 5: Performance Enhancement for Marine Energy Converter (MEC) Arrays; and • Task 6: Evaluating Sampling Techniques for MHK Biological Monitoring. The LCP was divided into four overarching technical tasks: • Task 7: Project Management and Reporting • Task 8: Novel Design and Assessment Methodologies for Wave Energy Converter Design (Wave- SPARC) • Task 9: Testing Access for Commercial Marine Renewable Energy Technology Developers • Task 10: Quantifying Collision Risk for Fish and Turbines • Task 11: Nonlinear Ocean Waves and PTO Control Strategy Each ALFA/LCP task listed above functioned as a separate and discreet project. A final Technical Report was written for each individual task and these reports were uploaded to OSTI, after receiving DOE approval. The following document is a compilation of each of these final, approved reports arranged as individual chapters.

13 HYDRO ENERGY↗

DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science [Slides]

Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Bounded extremum seeking for angular velocity actuated control of nonholonomic unicycle

Here, we study control of the angular-velocity actuated nonholonomic unicycle, via a simple, bounded extremum seeking controller which is robust to external disturbances and measurement noise. The vehicle performs source seeking despite not having any position information about itself or the source, able only to sense a noise corrupted scalar value whose extremum coincides with the unknown source location. In order to control the angular velocity, rather than the angular heading directly, a controller is developed such that the closed loop system exhibits multiple time scales and requires an analysis approach expanding the previous work of Kurzweil, Jarnik, Sussmann, and Liu, utilizing weak limits. We provide analytic proof of stability and demonstrate how this simple scheme can be extended to include position-independent source seeking, tracking, and collision avoidance of groups on autonomous vehicles in GPS-denied environments, based only on a measure of distance to an obstacle, which is an especially important feature for an autonomous agent.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed-Autonomy Traffic

Here, this article presents a novel hierarchical speed planning framework for variable speed limits in mixed-autonomy traffic environments, leveraging server-side macroscopic control and vehicle-side microscopic execution. The framework integrates real-time traffic state estimation (TSE) and reinforcement learning (RL)-based control to mitigate congestion and improve traffic flow. A TSE enhancement module combines macroscopic data from sources like INRIX with high-resolution observations from connected autonomous vehicles (CAVs), enabling predictive modeling to address latency and noise. The target speed design module employs kernel smoothing and a buffer zone strategy to optimize traffic density and flow around bottlenecks. The proposed system was validated in the largest open-road test to date with 100 CAVs, demonstrating an overall 8% traffic density decrease, with a specific decrease of 7% upstream, 10% downstream, and a 52% decrease during the congestion formation phase at bottlenecks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1985

Space research and Spartan

For a number of years, space research has been primarily conducted with sounding rockets and orbiting satellites. The use of satellites has disadvantages related to cost and the long lead time from design to launch, while a drawback in the case of sounding rockets is the short flight time of only a few minutes. The development of the Shuttle led to the proposal to employ for space research an autonomous free-flying payload deployed and recovered by the Shuttle. The proposed payload, called SPEAR (Small Payload Ejection and Recovery), looked very much like a rocket payload. Subsequently, the payload developed into a more compact form, and its name became Spartan-1. Attention is given to the demands of space research, the Spartan-1 payload, the payload functional control system, the thermal control system, the Spartan-1 mission, and the future of Spartan.

Cruddace, R. G.↗

NASA NTRS1987

The Flight Telerobotic Servicer (FTS) - A focus for automation and robotics on the Space Station

The concept, fundamental design principles, and capabilities of the FTS, a multipurpose telerobotic system for use on the Space Station and Space Shuttle, are discussed. The FTS is intended to assist the crew in the performance of extravehicular tasks; the telerobot will also be used on the Orbital Maneuvering Vehicle to service free-flyer spacecraft. The FTS will be capable of both teleoperation and autonomous operation; eventually it may also utilize ground control. By careful selection of the functional architecture and a modular approach to the hardware and software design, the FTS can accept developments in artificial intelligence and newer, more advanced sensors, such as machine vision and collision avoidance.

Hinkal, Sanford W.↗

NASA NTRS1987

Traction-drive telerobot for space manipulation

The National Aeronautics and Space Administration (NASA) Space Station Program marks the begining of a new era in space utilization and habitation. Extensive use of remote manipulation and robotics to reduce astronaut extra-vehicular activity is expected. Emphasis on teleoperator technology in early Space Station phases, followed by growth of autonomous robotics capabilities, is planned. A new telerobot concept has been developed at Oak Ridge National Laboratory (ORNL) under NASA Langley Research Center sponsorship, to address the technical needs of both teleoperations and telerobotics for these future NASA programs. The concept is based on traction drives, redundant kinematics, modular construction, and a state-of-the-art distributed, hierarchical control system.

Herndon, J. N.↗

NASA NTRS1989

Ambler - An autonomous rover for planetary exploration

The authors are building a prototype legged rover, called the Ambler (loosely an acronym for autonomous mobile exploration robot) and testing it on full-scale, rugged terrain of the sort that might be encountered on the Martian surface. They present an overview of their research program, focusing on locomotion, perception, planning, and control. They summarize some of the most important goals and requirements of a rover design and describe how locomotion, perception, and planning systems can satisfy these requirements. Since the program is relatively young (one year old at the time of writing) they identify issues and approaches and describe work in progress rather than report results. It is expected that many of the technologies developed will be applicable to other planetary bodies and to terrestrial concerns such as hazardous waste assessment and remediation, ocean floor exploration, and mining.

Bares, John↗

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 NTRS1995

Human-like Compliance for Dexterous Robot Hands

This paper describes the Active Electromechanical Compliance (AEC) system that was developed for the Jau-JPL anthropomorphic robot. The AEC system imitates the functionality of the human muscle's secondary function, which is to control the joint's stiffness: AEC is implemented through servo controlling the joint drive train's stiffness. The control strategy, controlling compliant joints in teleoperation, is described. It enables automatic hybrid position and force control through utilizing sensory feedback from joint and compliance sensors. This compliant control strategy is adaptable for autonomous robot control as well. Active compliance enables dual arm manipulations, human-like soft grasping by the robot hand, and opens the way to many new robotics applications.

Compliant Control↗

NASA NTRS2011

Microencapsulation of Corrosion Indicators for Smart Coatings

A multifunctional smart coating for the autonomous detection, indication, and control of corrosion is been developed based on microencapsulation technology. This paper summarizes the development, optimization, and testing of microcapsules specifically designed for early detection and indication of corrosion when incorporated into a smart coating. Results from experiments designed to test the ability of the microcapsules to detect and indicate corrosion, when blended into several paint systems, show that these experimental coatings generate a color change, indicative of spot specific corrosion events, that can be observed with the naked eye within hours rather than the hundreds of hours or months typical of the standard accelerated corrosion test protocols.. Key words: smart coating, corrosion detection, microencapsulation, microcapsule, pH-sensitive microcapsule, corrosion indicator, corrosion sensing paint

Li, Wenyan↗

NASA NTRS1993

Experimental object-level strategic control with cooperating manipulators

This article presents the high-level control module and user interface of the Dynamic and Strategic Control of Cooperating Manipulators (DASCCOM) project at Stanford University's Aerospace Robotics Laboratory. In addition to cooperative dynamic control, DASCCOM incorporates real-time vision-ased feedback, a novel strategic programming technique, and an iconic 'object-only' graphical user interface. By focusing on the vertical integration problem, we are examining not only these subsystems, but also their interfaces and interactions. The control system implements a multilevel hierarchical structure interconnected via a real-time network. At the highest level, a mouse-driven graphical user interface allows an operator to direct the activities of the system conceptually. Strategic command is provided by an event-driven finite state machine. This methodology provides a powerful yet flexible technique for managing concurrent system interactions. The dynamic controller implements object impedance control - an extension of Neville Hogan's impedance control concept to cooperative multiple-arm manipulation of a single object. This article concentrates on user interfacing techniques and strategic programming capabilities. These modules allow the user to directly specify conceptual object relationships. Experimental results are presented, showing the system locating and identifying a moving object, 'catching' it, and performing a simple cooperative assembly. Each of these operations is executed autonomously, with only object-level task-specification direction from a remote operator.

Schneider, Stanley A.↗

NASA NTRS2019

Astrobee Robot Software: Enabling Mobile Autonomy on the ISS

Astrobee is a new free-flyer robot designed to operate inside the International Space Station (ISS). Astrobee capabilities include markerless navigation, autonomous docking for recharge, perching on handrails to minimize power and modular payloads. Astrobee will operate without crew support, controlled by teleoperation, plan execution, or on-board third parties software. This slides presents the Astrobee Robot Software, a NASA Open-Source project, powering the Astrobee robot.The Astrobee Robot Software relies on a distributed architecture based on the Robot Operating System (ROS). We present the software approach, infrastructure required, and main software components.

Fluckiger, Lorenzo↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) DATASCOPE Technical Interchange Meeting 21st August 2023: Background and Summary of Discussion

An aspiration for EIMO datascope is to realize artificial intelligence-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A vision proposed to the meeting participants was that of a “system of systems,” whereby EIMO will utilize AI-supported natural language processing and machine learning techniques to synthesize embedded reference databases and real-time data streams [input vectors] from multiple data sources to continuously and seamlessly assess crew health & performance. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will ideally have a degree of mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats.

Artificial Intelligence↗

NASA NTRS2012

Ground Simulation of an Autonomous Satellite Rendezvous and Tracking System Using Dual Robotic Systems

A hardware-in-the-loop ground system was developed for simulating a robotic servicer spacecraft tracking a target satellite at short range. A relative navigation sensor package "Argon" is mounted on the end-effector of a Fanuc 430 manipulator, which functions as the base platform of the robotic spacecraft servicer. Machine vision algorithms estimate the pose of the target spacecraft, mounted on a Rotopod R-2000 platform, relay the solution to a simulation of the servicer spacecraft running in "Freespace", which performs guidance, navigation and control functions, integrates dynamics, and issues motion commands to a Fanuc platform controller so that it tracks the simulated servicer spacecraft. Results will be reviewed for several satellite motion scenarios at different ranges. Key words: robotics, satellite, servicing, guidance, navigation, tracking, control, docking.

Trube, Matthew J.↗

DOE OSTI2022

Artificial Intelligence and Machine Learning for Bioenergy Research: Opportunities and Challenges

The integration of artificial intelligence and machine learning (AI/ML) with automated experimentation, genomics, biosystems design, and bioprocessing technologies is poised to revolutionize scientific investigation and, particularly, bioenergy research. To identify the opportunities and challenges in this emerging research area, the U.S. Department of Energy’s (DOE) Biological and Environmental Research program (BER) and Bioenergy Technologies Office (BETO) held a joint virtual workshop on AI/ML for Bioenergy Research (AMBER) on August 23–25, 2022. These interests have since been amplified in a September 2022 Executive Order, “Advancing Biotechnology and Biomanufacturing Innovation for a Sustainable, Safe, and Secure U.S. Bioeconomy,” to promote a whole-of government approach to biotechnology development (White House 2022). Approximately 50 scientists with various backgrounds and expertise from academia, industry, and DOE national laboratories met to discuss the opportunities and challenges of AI/ML for bioenergy research. Workshop participants were tasked with assessing the potential for AI/ML and laboratory automation to advance biological understanding and engineering in general. They particularly examined how integrating AI/ML tools with laboratory automation could accelerate biosystems design and optimize biomanufacturing. Discussions included the data and computational infrastructure needed to augment biosystems design applications and the expertise and workforce development efforts urgently required to shift integrated systems toward bioenergy research more broadly. Participants discussed many existing and future applications of AI/ML for biosystems design ranging from enzymes to plants and microbes, microbiomes, and bioprocess development. They also identified three key categories of scientific and technical opportunities and challenges: high-quality data, AI/ML algorithms, and laboratory automation. Several main takeaways emerged from the workshop: 1. Numerous AI/ML and automated experimentation applications exist for a variety of DOE mission needs in energy and the environment; 2. Exemplary research grand challenges for which AI/ML could provide solutions include: building microbes and microbial communities to specifications, developing closed-loop autonomous design and control for biosystems design, and advancing scale-up and automation; 3. Lack of sufficient high-quality, annotated data hinders the development of AI/ML applications; 4. New and improved AI/ML tools are needed, particularly those meeting the specific needs of the BER and BETO research communities; 5. Trade-offs in performance, cost, and reliability exist between deploying commercially available versus building custom-developed instrumentation and software for automated or autonomous experimentation; translation of manual to automated or autonomous methods is often a nontrivial endeavor; 6. Training a new generation of young scientists who can develop and apply AI/ML tools is needed to solve long-standing scientific challenges in bioenergy research. The integration of AI/ML tools and automated experimentation represents a new data-driven research paradigm complementary to the traditional hypothesis-driven research paradigm. This paradigm accelerates design and optimization of biological systems and processes for a variety of DOE mission needs in energy and the environment. The AMBER workshop broadly explored the potential of this new paradigm for bioenergy research, of particular interest to BER and BETO, and identified key challenges and opportunities that DOE can address in the coming years by leveraging its unique capabilities and resources.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2005

Line of Sight Stabilization of James Webb Space Telescope

The James Webb Space Telescope (JWST) builds upon the successful flight experience of the Chandra Xray Telescope by incorporating an additional LOS pointing servo to meet the more stringent pointing requirements. The LOS pointing servo, referred to in JWST as the Fine Guidance Control System (FGCS), will utilize a Fine Guidance Sensor (FGS) as the sensor, and a Fine Steering Mirror (FSM) as the actuator. The FSM is a part of the Optical Telescope Element (OTE) and is in the optical path between the tertiary mirror and the instrument focal plane, while the FGS is part of the Integrated Science Instrument Module (ISIM). The basic Chandra spacecraft bus attitude control and determination architecture, utilizing gyros, star trackers/aspect camera, and reaction wheels, is retained for JWST. This system has achieved pointing stability of better than 0.5 arcseconds. To reach the JWST requirements of milli-arcsecond pointing stability with this ACS hardware, the local FGCS loop is added to the optical path. The FGCS bandwidth is about 2.0 Hz and will therefore attenuate much of the spacecraft ACS induced low frequency jitter. In order to attenuate the higher frequency (greatet than 2.0 Hz) disturbances associated with reaction wheel static and dynamic imbalances, as well as bearing run-out, JWST will employ a two-stage passive vibration isolation system consisting of (1) 7.0 Hz reaction wheel isolators between each reaction wheel and the spacecraft bus, and (2) a 1.0 Hz tower isolator between the spacecraft bus and the Optical Telescope Element (OTE). In order to sense and measure the LOS, the FGS behaves much like an autonomous star tracker that has a very small field of view and uses the optics of the telescope. It performs the functions of acquisition, identification and tracking of stars in its 2.5 x 2.5 arcminute field of view (FOV), and provides the centroid and magnitude of the selected star for use in LOS control. However, since only a single star is being tracked at any time within the FGS FOV there is only tip and tilt information; rotation about the FGS LOS will not be sensed. The FGCS uses the FSM to move the guide star within the FGS FOV and place the centroid of the guide star at any desired position within the FGS focal plane. Using this architecture allows the FGCS to correct the low frequency LOS jitter that is induced by the spacecraft ACS in pitch and yaw, and achieve the milli-arcsecond pointing stability required by JWST. The less stringent ISIM FOV roll performance will be provided solely by the ACS, using the spacecraft gyros and star trackers. Since the FSM is in the optical path, the pointing stabilrty of a science object in any of the instruments will be similar to that of the guide star LOS.

Meza, Luis↗

NASA NTRS1990

Project WISH: The Emerald City

When Project WISH (Wandering Interplanetary Space Harbor) was initiated as a multi-year project, several design requirements were specified. The space station must have a lifetime of at least 50 years, be autonomous and independent of Earth resources, be capable of traveling throughout the solar system within a maximum flight time of three years, and have a population of 500 to 1000 people. The purpose of the station is to provide a permanent home for space colonists and to serve as a service station for space missions. The orbital mechanics, propulsion system, vehicle dynamics and control, life support system, communication system, power system, and thermal system are discussed.

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