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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Entry, Descent, and Landing GN&C System Evaluation via Cable-Driven Emulation Robotics

An innovative cable-driven parallel robot named the Six Degree-of-Freedom Tendon Actu- ated Robot (STAR), recently developed by researchers at NASA Johnson Space Center, was built to enable entry, descent, and landing (EDL) emulation experiments. Upon functional testing and system verification, STAR employed several simulated EDL trajectories to validate velocimeter LIDAR-based sensor models for cooperative terrain relative navigation applica- tions for its first testing campaign. The STAR lab also hosted researchers from Astrobotic to evaluate the real-time performance of a newly developed sensor payload, UltraNav, through hardware in-the-loop-testing (HWIL). This work introduces the STAR system along with its accompanying testing utilities and capabilities, and deliver the experimental methodology and results of test campaigns performed with the STAR facility to date.

navigation↗
NASA NTRSDate not supplied

Astrobee: Completed, Current, and Future Research Using Free Flying Robots on the International Space Station

After four years on the International Space Station (ISS), the Astrobee Research Facility, has completed over 130 Test Sessions logging over 1000 hours of operations. Managed by the NASA ISS Program OZ office and supported by NASA Ames Research Center (ARC) in California, the Astrobee Team maintains three identical free-flying Astrobee robots for research on the ISS. As a technology demonstration platform, the Astrobee Robots are available for Guest Scientists to use for a spectrum of research capabilities.

Astrobee↗
NASA NTRSDate not supplied

SOLL-E: A Module Transport and Placement Robot for Autonomous Assembly of Discrete Lattice Structures

This paper presents the design and development of a transport and placement robot that demonstrates autonomous assembly of structural building blocks. The robots are intended to serve as a critical component of automated structural assembly and maintenance systems. The Scaling Omni-directional Lattice Locomoting Explorer (SOLL-E) uses a 5-DoF bipedal inchworm locomotion architecture with locking foot and cargo grippers. The locomotion system employs large magnet gap diameter BLDC motors with moderate timing belt gearing for primary joints, and DC planetary gearmotors for turning. Foot and cargo grippers are identical, with servo-actuated locking mechanisms. Three modular controller boards are used to control these actuators in real-time, with command and telemetry data transferred between the server and each controller board via WiFi. Functionality and performance were evaluated in a ground demonstration.

robotics↗
NASA NTRSDate not supplied

Improving Sim-to-Real Transfer in Vision-Based Robot Navigation Via Instance-Level GAN-Based Data Augmentation

Achieving robust vision-based robotic tasks requires large amounts of data, which are often difficult to obtain in real-world scenarios. Simulators and synthetic data offer a cost-effective alternative, but the visual gap between simulation and reality hinders the performance of models when deployed in real-world environments. In this paper, we present a data augmentation pipeline that integrates a foundation model (Segment Anything Model) with an unsupervised image-to-image translation model (CycleGAN) for instance-level domain transfer from simulation to reality. This pipeline enables the generation of realistic labeled data from synthetic images for training supervised machine learning models in vision-based navigation tasks. We evaluate our approach on real-world data for ego-vehicle pose estimation, a critical autonomous navigation task involving the prediction of cross-track position and heading angle relative to road center line markings. The results of our tests show that our GAN-based data augmentation pipeline significantly outperforms models trained solely on simulation data or on data processed with standard image augmentation methods for sim-to-real transfer, enhancing model robustness and generalizability in real-world scenarios. Our method provides a scalable and flexible data augmentation tool for leveraging large synthetic datasets to enhance vision-based robotic navigation tasks.

artificial intelligence↗
DOE OSTI2022

An Autonomous Robot for Shell and Tube Heat Exchanger Inspection

Shell and tube heat exchangers (STHEs) are critical to energy conversion efficiency of power plants. Eddy current examination is a way to evaluate working conditions of these tubes. However, the current testing apparatus requires human to manually insert an eddy current testing (ECT) probe into and extract it out of individual tubes, and meanwhile monitor measurement results for diagnosis. It is a time-consuming and labor-intensive procedure even for an experienced technician. To tackle this challenge, in this work, we developed a robot enabled ECT system for autonomous inspection of STHEs. The robotic platform employs Mecanum wheeled chassis for high mobility, machine vision to locate tube bundle and tube inlets, a rotational Cartesian mechanism to operate at planes with all possible inclinations, and a task-specific mechanism for ECT probe delivery. Machine vision locates tube bundle and tube inlets by an April tag detection algorithm and a Circle Hough Transform (CHT) algorithm, respectively. Assisted by a guidance cone, the ECT probe is continuously fed into the tubes with a fill factor of 0.819. During this process, the eddy current data are automatically collected and real-time analyzed by convolutional neural networks (CNN), showing accuracy of nearly 100% for identifying defective and non-defective tubes and 85% for four types of defective tubes and non-defective tubes.

autonomy, deep learning, eddy current testing, hea↗
DOE OSTI2017

Wearable Robots for Worker Assistance - 17464

Wearable Robots is a fast growing field with exoskeleton devices focused on augmenting, enhancing, and assisting human movement and strength. Devices have been designed for people with spinal cord injuries, stroke survivors, medical rehabilitation, assistance to the elderly, and recreation to aid in sports. For manufacturing and industry, devices are quickly being developed that allow the human and the robot to interact in a synergistic fashion to accomplish a task. (authors)

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DOE OSTI2016

Mobile Robots and Remote Characterisation Systems for Nuclear Decommissioning - 16028

This paper provides an updated summary of the research being conducted at the University of Manchester, UK, in support of the decommissioning plan for the Sellafield nuclear site in Cumbria, UK. The research is being conducted in collaboration with a number of companies including Sellafield Ltd, the National Nuclear Laboratory and Forth Engineering Ltd. The primary focus of the research is in support of the decommissioning of the legacy facilities on the Sellafield site, however the technology can be applied to other nuclear sites, both modern and legacy, as well as in other industries such as oil and gas, petrochemical and the marine industry. The progress on the development of novel characterisation platforms (mobile robots) and sensing systems is presented including the AVEXIS underwater inspection system, a robot spider for remote operations and algorithms for efficient compression of Lidar point cloud data. (authors)

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DOE OSTI2018

3-D Volumetric Gamma-ray Imaging and Source Localization with a Mobile Robot - 18124

Radiation detection has largely been a manual inspection process with point sensors such as Geiger-Muller counters and scintillation spectrometers to date. While their observations of source proximity prove useful, they lack the directional information necessary for efficient source localization and characterization in cluttered environments with multiple radiation sources. The recent commercialization of Compton gamma cameras provides directional information to the broader radiation detection community for the first time. This paper presents the integration of a Compton gamma camera with a self-localizing ground robot for accurate 3D radiation mapping. Using the position and orientation of the robot, radiation images from the gamma camera are accumulated over a traversed path in a shared frame of reference to construct a consistent voxel grid-based radiation map. The peaks of the map at pre-specified energy windows are selected as the source location estimates, which are compared to the ground truth source locations. The proposed approach localizes multiple sources to within an average of 0.2 m in two 5 x 4 m{sup 2} and 14 x 6 m{sup 2} laboratory environments. (authors)

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DOE OSTI2018

Automation and Use of Robotics for Decommissioning Tasks - 18322

Decommissioning across the globe is being executed largely by manual labor. The Organization for Economic Co-operation and Development (OECD) has concluded that the nuclear industry has not fully exploited or implemented current technological capabilities, and relies on sometimes outdated technology to perform decommissioning tasks [1]. To address this issue, EPRI recently published the report Evaluation of System Automation and Robotics for Decommissioning Applications (EPRI 3002010599) [2]. To facilitate wide usage of the guidance by technology developers, the report is publicly available at no cost. The use of more efficient and effective decommissioning technologies and processes could reduce the decommissioning project schedule and therefore reduce the period-dependent costs incurred throughout the decommissioning project. Such systems may also offer a substantial benefit with respect to worker safety and radiation exposure reduction. However, tasks for which use of such systems is beneficial must be carefully considered, being robust and generally trouble free to operate in the demanding environment of commercial power plant decommissioning. A review of the major tasks involved in the decommissioning of a nuclear power plant is presented, with a critical assessment of the benefits for the use of robotics and/or automation. Recommendations for areas of future research and development are identified. The results of this project will be beneficial to global organizations working on development of engineered systems for decommissioning applications by providing a focus on priority tasks. (authors)

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DOE OSTI2018

Robotic Measurement of Holdup Deposit Volume in Gaseous Diffusion Piping to Quantify U-235 Content - 18375

During D and D of old gaseous diffusion uranium enrichment facilities, radiometric assay of U-235 holdup in pipes is a costly, time consuming, and labor-intensive process. Subject to human interpretation using approximate modeling, radiometric assay introduces significant challenges. Taking advantage of routine demolition activities in which D and D cuts pipes open, robotic in-pipe assay is explored. The novel method introduced here generates models of the internal pipe and deposit surface geometries that are used to derive volumetric quantities. The pipe surface is sensed using a non-contact inductive proximity sensor and the deposit surface is sensed using an optical laser triangulation sensor. These sensors are mounted on a spinning disk and driven down the pipe to construct a helical point cloud of discrete measurements. Surfaces are fit to each of the two point clouds to create a watertight volume that represents the holdup. This provides the location and volume per foot of pipe that is used to compare against criticality incredible (CI) thresholds during evaluation. The robotic system collects data autonomously, deploying and returning to the same pipe opening from which it is launched. This provides redundant measurements of the pipe and deposit surfaces as well as odometry used in localization. (authors)

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DOE OSTI2018

Additive Manufacturing Advances to Facilitate Novel Robot and Remote System Construction - 18493

Additive manufacturing (AM), in both polymer and metal, has advanced to the point where it can significantly facilitate robot and remote system design and implementation. Properly used AM creates features and capabilities otherwise not possible in machined or cast products. AM design rules are still being developed and must be applied differently based on the process and purpose of the final component, but progress is being made in practical implementation. Polymer-based AM has been commercially available for quite some time and is regularly used for prototyping and test fitting parts before they are fabricated from metal or produced in low production quantity parts that do not require high structural strength. Metal printing processes are more diverse than polymer print processes. Both polymer and metal printing systems can be used to make parts or complete systems for robotics. Two implementation examples that illustrate the possibilities and identify issues that remain in metal printing are: a dual arm hydraulic manipulator and a distal link of an excavator arm. The dual arm hydraulic manipulator was printed in titanium using an e-beam fusing process. All hydraulics and electrical cabling passageways were printed into the manipulator; there are no external hoses or cables. The distal link of an excavator arm was printed using a wire-fed metal inert gas process. The excavator was then used for digging as part of an equipment exposition validating the structural integrity of the printed part. (authors)

3D PRINTING↗
DOE OSTI2018

Wearable Robotics and Exoskeletons for WM Workers: Potential Benefits and Challenges - 18506

Wearable robotic and exoskeleton systems are emerging that have the potential to augment workers and reduce musculoskeletal injury and fatigue. To fully realize this potential, developers and workers should collaborate to ensure that the devices' benefits are realized without inducing secondary side-effects that outweigh the benefits. This paper describes the development of a prototype system for both injury and fatigue reduction. It also provides a summary of several important considerations when integrating wearable robotics with a workforce, such as comfort, fit, and hygiene. We recommend that an integration plan be developed before providing these systems to workers. (authors)

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DOE OSTI2018

Mine Rescue Robotics: Gemini-Scout - 18507

Gemini Scout is a mine rescue vehicle developed by Sandia National Laboratories to help locate and communicate with trapped miners following an accident. In a mining accident, first responders are working against a myriad of dangers to assess the situation and rescue trapped miners. The robot can enter potentially dangerous situations ahead of rescuers to evaluate hazardous environments and help them determine how operations should proceed. The Gemini Scout mitigates many of the potential dangers and allows exploration and assessment without further endangering individuals. The robotic vehicle can operate in hostile, debris strewn, dark, muddy, high temperature, and explosive environments. It has extreme ground mobility including operation in up to 18 inches of water and ability to traverse large rubble piles and cross railroad tracks. The vehicle provides gas and temperature sensing as well as pan, tilt, zoom color and thermal camera video streaming back to an operator in a safe zone. The extreme mobility of the vehicle and the modularity of the platform allowing easy integration of sensors are directly applicable to many needs within Waste Management. The development of Gemini Scout was funded by National Institute of Occupational Safety and Health (NIOSH) and the primary customer and certification entity was Mine Safety and Health Administration (MSHA). Lessons-learned during development and steps leading to certification are also applicable to the development of systems for unique Waste Management sites. (authors)

42 ENGINEERING↗
DOE OSTI2018

Use of Robotics in Liquid Waste System - {sup 3}H Evaporator, Tank Closure and Defense Waste Processing Facility - 18525

The Savannah River Site (SRS) Liquid Waste System (LWS) safely stores and treats high-level radioactive waste. The LWS consists of 51 waste storage tanks (eight of which are operationally closed and filled with grout), waste evaporators, treatment facilities, and solidification facilities such as the Defense Waste Processing Facility (DWPF) and Salt-stone Production Facility (SPF). At Savannah River Remediation (SRR), the liquid waste contractor, robotic technologies are needed to enter areas and spaces that are otherwise inaccessible or prohibit direct access by workers due to unsafe, unstable, or unknown physical or structural conditions; or configurations that are hard to reach or beyond reach without taking extraordinary mechanical measures. As discussed below, SRR uses robotic technologies at various LWS facilities to reduce dose to the workers as well as provide access to unreachable locations due to the presence of radiological or chemical hazards. (author)

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DOE OSTI2018

Robotic Crane System for the Deconstruction of Chernobyl Unit 4 - 18583

The Chernobyl Nuclear Power Plant (ChNPP) Unit 4 accident occurred on April 26, 1986 leaving a severely damaged reactor building and a highly radioactive area. To minimize the long-term radiological impact to personnel and the environment, Unit 4 was immediately enclosed by a structure named the Object Shelter (OS). To transform the OS into an ecologically safe system, the Shelter Implementation Plan (SIP) was developed in 1997. The most significant SIP tasks was the design and construction of the New Safe Confinement (NSC). The NSC is a 36,000 tonne arch enclosure that includes a suite of process equipment to address the ruins of Unit 4 while assuring the safety of workers, public, and the environment. The NSC has a span of 257 meters, length of 164 meters, and height of 110 meters and is designed for a 100 years life. The New Safe Confinement was slid from its erection area to directly over Unit 4 (a distance of 327 meters) in November 2016. One of the major mechanical handling systems installed in the NSC is the Main Cranes System (MCS), designed and fabricated by PaR Systems (USA). The planned deconstruction activities require the handling of shielded waste disposal casks containing fuel as well as transporting extremely large structural elements -remotely controlled using video surveillance equipment. The lifting activities required the cranes to be tele-robotically controlled to prevent inadvertent collisions, expedite repetitive and high accuracy functions, protect the equipment and structures within the NSC as well as increase the overall effectiveness of the MCS. In addition, due to the elevated radiation levels, remote deconstruction activities will need to be performed. The MCS has design provisions included for a tele-robotically controlled manipulator and a suite of tooling to be used for multiple activities including concrete coring, cutting, debris collection and other heavy demolition activities. (authors)

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DOE OSTI2019

Automated Analysis, Reporting, and Archiving for Robotic Nondestructive Assay of Holdup Deposits - 19508

To decommission deactivated gaseous diffusion enrichment facilities, miles of contaminated pipe must be measured. The current method requires thousands of manual measurements, repeated manual data transcription, and months of manual analysis. The Pipe Crawling Activity Measurement System (PCAMS), developed by Carnegie Mellon University and in commissioning for use at the DOE Portsmouth Gaseous Diffusion Enrichment Facility, uses a robot to measure Uranium-235 from inside pipes and automatically log the data. Radiation measurements, as well as imagery, geometric modeling, and precise measurement positioning data are digitally transferred to the PCAMS server. On the server, data can be automatically processed in minutes and summarized for analyst review. Measurement reports are auto-generated with the push of a button. A database specially-configured to hold heterogeneous data such as spectra, images, and robot trajectories serves as archive. This paper outlines the features and design of the PCAMS Post-Processing Software, currently in commissioning for use at the Portsmouth Gaseous Diffusion Enrichment Facility. The analysis process, the analyst interface to the system, and the content of auto-generated reports are each described. Example pipe-interior geometric surface models, illustration of how key report features apply in operational runs, and user feedback are discussed. (authors)

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DOE OSTI2019

Collaborative Robots vs. Master/Slaves - 19522

This paper will discuss the feasibility, application, and economy of applying collaborative robots (co-bots) in lieu of traditional through-the-wall tele-operated manipulator systems, traditionally used within hazardous (radioactive) environments. Both fixed facility and mobile applications will be discussed. Both single- and dual-armed co-bot systems will be employed. The six-axis, plus gripper, single-armed co-bots will provide for machine vision enhanced pick-n-place applications within shielded rooms and also include force-reflecting (haptic) teleoperations controls for non-routine materials-handling operations. The dual-armed system will be mounted to a motorized stand with motorized adjustment both vertically and rotationally (around its base). To complement these co-bots, both two-dimensional and three-dimensional vision technologies will be implemented. The vision technologies, in these applications, are used in lieu of traditional radiation shielding windows. two-dimensional video systems will provide for large area viewing, incorporating pan, tilt, and zoom capabilities. Three-dimensional video systems will be mounted to the dual-armed robot stand and provide viewing, including depth-of-perception, to the operator, performing material handling and other tasks, from an operator workstation located outside the hazardous environment. The unique workstation used with the dual-armed systems will also be described. (authors)

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DOE OSTI2019

R and DE Robotic Sensor Intern Team: Advanced Sensor Suite Prototype

The Department of Energy (DOE) nuclear complex includes many hazardous and challenging environments that must be inspected remotely and routinely for structural integrity evaluation and long-term planning purposes. The H-Canyon Air Exhaust (HCAEX) Tunnel is an example of such an environment where a biannual inspection is currently performed. Presently inspections are performed by teleoperated robotic crawlers which provide low-res video feedback to the structural engineers. Advanced sensors: including Lidar and panospheric hi-res cameras have the potential to provide additional valuable data in future inspections. The primary goal of the sensor suite is to provide an internal 3D map of the HCAEX tunnel. Possible sensors were a stereo camera, a 3D Lidar, or a 2D Lidar that would need to be spun around. The issues with the stereo camera were that it does not have a large field-of-view and that its depth information was not nearly as accurate as even the baseline Lidars. The Hokuyo UTM-30LX-EW shown below provides a 270 deg. FOV with high accuracy and resolution while the largest FOV on a 3D Lidar is 40 deg. This was the reason a 2D Lidar was chosen over the 3D Lidar. Even though the 2D Lidar requires additional components and software to generate a 3D map, its FOV advantage is too great given that the HCAEX tunnel is a relatively confined space. Along with the Lidar, a camera sensor, IMU sensor, pan-tit unit, and on board computer needed to be identified. The two panospheric cameras allow for full 360 deg. FOV. The IMU will give data on the state of the robot inside the tunnel. The computer is used to gather and process data on board and to control the pan-tilt system. Mounts were designed and 3D printed to build the prototype of the sensor suite. It was designed to have the Lidar on top With its full FOV free while having the camera below and as close to together as possible while still leaving much of their FOV visible. The design also managed the cables such that they were unlikely to be damaged if in tension and so that they did not intrude much on the cameras vision. The sensor tree developed for the H-Canyon tunnel inspection project produces a large quantity of camera and Lidar data. a postprocessing pipeline was created to facilitate the analysis of these data. The pipeline follows the following routine: 1) Registration - Salient local features in subsequent scans are identified and used to align the scans With one another. This allows production of a single cohesive map of the tunnel. 2) Sensor Fusion - RGB camera data is 'painted' onto the depth data created by the Lidar. 3) Segmentation - Individual surfaces from within the scene are segmented out separately for individual analysis. 4) Degradation Analysis - Automatic mapping of local deviation from the plane in position or surface normal is performed, as is detection of exposed rebar using Lidar return intensity. 2) Obstacle Detection - Lidar data is also used in real time to detect obstacles in the environment. This could give feedback to the operator to successfully drive in the tunnel. 6) Graphic User Interface - a custom GUI was created to facilitate customer use of the system. The system provides real-time feedback from both the cameras and Lidar. The Lidar point-cloud data is also used for obstacle detection. As the Lidar spins, various algorithms are run to analyze the current 'slice' and obstacles in the environment are determined and registered on a cost-map. A Graphical User Interface (GUI) was developed to allow the user to easily command the crawler to set the rotational speed of the Lidar, perform a scan, and register and save the 3D generated cloud after a scan is done. For the good of the crawler, a critical aspect of the GUI design is to allow the user to cancel a scan any time after it is commanded and before it is saved. The sensor trees depth accuracy was evaluated in comparison to a high end commercial FARO Terrestrial Laser Scanner (TLS) with specifications for millimeter accuracy at distances above 10 m. For the purposes of these tests, the depth measurements produced by the TLS were taken as ground truth. Example test wall panels were scanned with the TLS and postprocessed with its proprietary software, and subsequently scanned with the sensor tree system. The two scans were manually aligned and for each point in the output cloud from the sensor tree, the distance to the nearest ground truth point in the FARO cloud was determined. 98.7% of all produced points were within the specified target range of 6 mm, with an average deviation of 1.75 mm. The test walls used have an overall in-wall depth variation of around 40 mm.

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