Publications

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Concentrating Solar Power (CSP) requires precision mirrors, and these in turn require metrology systems to measure their optical slope. In this project we studied a color-based approach to the correspondence problem, which is the association of points on an optical target with their corresponding points seen in a reflection. This is a core problem in deflectometry-based metrology, and a color solution would enable important new capabilities. We modeled color as a vector in the [R,G,B] space measured by a digital camera, and explored a dual-image approach to compensate for inevitable changes in illumination color. Through a series of experiments including color target design and dual-image setups both indoors and outdoors, we collected reference/measurement image pairs for a variety of configurations and light conditions. We then analyzed the resulting image pairs by selecting example [R,G,B] pixels in the reference image, and seeking matching [R,G,B] pixels in the measurement image. Modulating a tolerance threshold enabled us to assess both match reliability and match ambiguity, and for some configurations, orthorectification enabled us to assess match accuracy. Using direct-direct imaging, we demonstrated color correspondence achieving average match accuracy values of 0.004 h, where h is the height of the color pattern. We found that wide-area two-dimensional and linear one-dimensional color targets outperformed hybrid linear/lateral gradient targets in the cases studied. Introducing a mirror degraded performance under our current techniques, and we did not have time to evaluate whether matches could be reliably achieved despite varying light conditions. Nonetheless, our results thus far are promising.

In the framework of SFERA-III WP10 Task3, ENEA has organized the 3D-shape round-robin (RR); the purpose is to compare the main geometrical parameters of 3D shape measurement of parabolic-trough (PT) reflective panels evaluated with the instruments adopted by each participant among: ENEA, DLR, F-ISE, NREL, and SANDIA. The last two institutions are outside of the EU, but benefited from the Transnational Access institute to visit several European laboratories, including the ENEA Casaccia research center where they accomplished some measurements with a portable experimental set-up. RR is based on the inter-laboratory circulation of 3 inner plus 3 outer PT panels. The start of the RR was delayed by the covid pandemic, then the circulation of the specimen-set and their measurement took more than one year. At the time of drafting this deliverable at the end of SFERA-III project, NREL has not yet completed the analysis of the measurements, making available only the deviations of the slopes. Therefore here will be reported only the preliminary results. The full comparison will be published as soon as possible, maybe in the open access venue Open Research Europe.

This paper summarizes findings from a small, mixed-method research study examining industry perspectives on the potential for new forms of automation to invigorate the concentrating solar power (CSP) industry. In Fall 2021, the Solar Energy Technologies Office (SETO) of the United States Department of Energy (DOE) funded Sandia National Laboratories to elicit industry stakeholder perspectives on the potential role of automated systems in CSP operations. We interviewed eleven CSP professionals from five countries, using a combination of structured and open comment response modes. Respondents indicated a preference for automated systems that support heliostat manufacturing and installation, calibration, and responsiveness to shifting weather conditions. This pilot study demonstrates the importance of engaging industry stakeholders in discussions of technology research and development, to promote adoptable, useful innovation.

This paper summarizes findings from a small, mixed-method research study examining industry perspectives on the potential for new forms of automation to invigorate the concentrating solar power (CSP) industry. In Fall 2021, the Solar Energy Technologies Office (SETO) of the United States Department of Energy (DOE) funded Sandia National Laboratories to elicit industry stakeholder perspectives on the potential role of automated systems in CSP operations. We interviewed eleven CSP professionals from five countries, using a combination of structured and open comment response modes. Respondents indicated a preference for automated systems that support heliostat manufacturing and installation, calibration, and responsiveness to shifting weather conditions. This pilot study demonstrates the importance of engaging industry stakeholders in discussions of technology research and development, to promote adoptable, useful innovation.

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The Sandia Optical Fringe Analysis Slope Tool (SOFAST) is a tool that has been developed at Sandia to measure the surface slope of concentrating solar power optics. This tool has largely remained of research quality over the past few years. Since SOFAST is important to ongoing tests happening at Sandia as well as an interest to others outside Sandia, there is a desire to bring SOFAST up to professional software standards. The goal of this effort was to make progress in several broad areas including: code quality, sample data collection, and validation and testing. During the course of this effort, much progress was made in these areas. SOFAST is now a much more professional grade tool. There are, however, some areas of improvement that could not be addressed in the timeframe of this work and will be addressed in the continuation of this effort.

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We use Bayesian data analysis to predict dengue fever outbreaks and quantify the link between outbreaks and meteorological precursors tied to the breeding conditions of vector mosquitos. We use Hamiltonian Monte Carlo sampling to estimate a seasonal Gaussian process modeling infection rate, and aperiodic basis coefficients for the rate of an “outbreak level” of infection beyond seasonal trends across two separate regions. We use this outbreak level to estimate an autoregressive moving average (ARMA) model from which we extrapolate a forecast. We show that the resulting model has useful forecasting power in the 6–8 week range. The forecasts are not significantly more accurate with the inclusion of meteorological covariates than with infection trends alone.

This report summarizes the work performed under the Sandia LDRD project "Eyes on the Ground: Visual Verification for On-Site Inspection." The goal of the project was to develop methods and tools to assist an IAEA inspector in assessing visual and other information encountered during an inspection. Effective IAEA inspections are key to verifying states' compliance with nuclear non-proliferation treaties. In the course of this work we developed a taxonomy of candidate inspector assistance tasks, selected key tasks to focus on, identified hardware and software solution approaches, and made progress in implementing them. In particular, we demonstrated the use of multiple types of 3-d scanning technology applied to simulated inspection environments, and implemented a preliminary prototype of a novel inspector assistance tool. This report summarizes the project's major accomplishments, and gathers the abstracts and references for the publication and reports that were prepared as part of this work. We then describe work in progress that is not yet ready for publication. Approved for public release; further dissemination unlimited.

This report summarizes the work performed under the Sandia LDRD project "Adverse Event Prediction Using Graph-Augmented Temporal Analysis." The goal of the project was to develop a method for analyzing multiple time-series data streams to identify precursors providing advance warning of the potential occurrence of events of interest. The proposed approach combined temporal analysis of each data stream with reasoning about relationships between data streams using a geospatial-temporal semantic graph. This class of problems is relevant to several important topics of national interest. In the course of this work we developed new temporal analysis techniques, including temporal analysis using Markov Chain Monte Carlo techniques, temporal shift algorithms to refine forecasts, and a version of Ripley's K-function extended to support temporal precursor identification. This report summarizes the project's major accomplishments, and gathers the abstracts and references for the publication sub-missions and reports that were prepared as part of this work. We then describe work in progress that is not yet ready for publication.

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This report summarizes the work performed under the Sandia LDRD project "Eyes on the Ground: Visual Verification for On-Site Inspection." The goal of the project was to develop methods and tools to assist an IAEA inspector in assessing visual and other information encountered during an inspection. Effective IAEA inspections are key to verifying states' compliance with nuclear non-proliferation treaties. In the course of this work we developed a taxonomy of candidate inspector assistance tasks, selected key tasks to focus on, identified hardware and software solution approaches, and made progress in implementing them. In particular, we demonstrated the use of multiple types of 3-d scanning technology applied to simulated inspection environments, and implemented a preliminary prototype of a novel inspector assistance tool. This report summarizes the project's major accomplishments, and gathers the abstracts and references for the publication and reports that were prepared as part of this work. We then describe work in progress that is not yet ready for publication. Approved for public release; further dissemination unlimited.

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The goal of the Eyes On the Ground project is to develop tools to aid IAEA inspectors. Our original vision was to produce a tool that would take three-dimensional measurements of an unknown piece of equipment, construct a semantic representation of the measured object, and then use the resulting data to infer possible explanations of equipment function. We report our tests of a 3-d laser scanner to obtain 3-d point cloud data, and subsequent tests of software to convert the resulting point clouds into primitive geometric objects such as planes and cylinders. These tests successfully identified pipes of moderate diameter and planar surfaces, but also incurred significant noise. We also investigated the IAEA inspector task context, and learned that task constraints may present significant obstacles to using 3-d laser scanners. We further learned that equipment scale and enclosing cases may confound our original goal of equipment diagnosis. Meanwhile, we also surveyed the rapidly evolving field of 3-d measurement technology, and identified alternative sensor modalities that may prove more suitable for inspector use in a safeguards context. We conclude with a detailed discussion of lessons learned and the resulting implications for project goals. Approved for public release; further dissemination unlimited.

A previous report assesses our progress to date on the Eyes On the Ground project, and reviews lessons learned. In this report, we address the implications of those lessons in defining the most productive path forward for the remainder of the project. We propose two main concepts: Interactive Diagnosis and Model-Driven Assistance. Among these, the Model-Driven Assistance concept appears the most promising. The Model-Driven Assistance concept is based on an approximate but useful model of a facility, which provides a unified representation for storing, viewing, and analyzing data that is known about the facility. This representation provides value to both inspectors and IAEA headquarters, and facilitates communication between the two. The concept further includes a lightweight, portable field tool to aid the inspector in executing a variety of inspection tasks, including capture of images and 3-d scan data. We develop a detailed description of this concept, including its system components, functionality, and example use cases. The envisioned tool would provide value by reducing inspector cognitive load, streamlining inspection tasks, and facilitating communication between the inspector and teams at IAEA headquarters. We conclude by enumerating the top implementation priorities to pursue in the remaining limited time of the project. Approved for public release; further dissemination unlimited.

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We address the problem of wide-area search of overhead imagery. Given a time sequence of overhead images, we construct a geospatial-temporal semantic graph, which expresses the complex continuous information in the overhead images in a discrete searchable form, including explicit modeling of changes seen from one image to the next. We can then express desired search goals as a template graph, and search for matches using simple and efficient graph search algorithms. This produces a set of potential matches which provide cues for where to examine the imagery in detail, applying human expertise to determine which matches are correct. We include a match quality metric that scores the matches according to how well they match the stated search goal. This enables matches to be presented in sorted order with the best matches first, similar to the results returned by a web search engine. We present an evaluation of the method applied to several examples and data sets, and show that it can be used successfully for some problems. We also remark on several limitations of the method and note additional work needed to improve its scope and robustness. Approved for public release; further dissemination unlimited.

We assess how geospatial-temporal semantic graphs and our GeoGraphy code implementation might contribute to induced seismicity analysis. We focus on evaluating strengths and weaknesses of both 1) the fundamental concept of semantic graphs and 2) our current code implementation. With extensions and research effort, code implementation limitations can be overcome. The paper also describes relevance including possible data input types, expected analytical outcomes and how it can pair with other approaches and fit into a workflow.

Remote sensing systems produce large volumes of high-resolution images that are difficult to search. The GeoGraphy (pronounced Geo-Graph-y) framework [2, 20] encodes remote sensing imagery into a geospatial-temporal semantic graph representation to enable high level semantic searches to be performed. Typically scene objects such as buildings and trees tend to be shaped like blocks with few holes, but other shapes generated from path networks tend to have a large number of holes and can span a large geographic region due to their connectedness. For example, we have a dataset covering the city of Philadelphia in which there is a single road network node spanning a 6 mile x 8 mile region. Even a simple question such as "find two houses near the same street" might give unexpected results. More generally, nodes arising from networks of paths (roads, sidewalks, trails, etc.) require additional processing to make them useful for searches in GeoGraphy. We have assigned the term Path Network Recovery to this process. Path Network Recovery is a three-step process involving (1) partitioning the network node into segments, (2) repairing broken path segments interrupted by occlusions or sensor noise, and (3) adding path-aware search semantics into GeoQuestions. This report covers the path network recovery process, how it is used, and some example use cases of the current capabilities.

Geospatial semantic graphs provide a robust foundation for representing and analyzing remote sensor data. In particular, they support a variety of pattern search operations that capture the spatial and temporal relationships among the objects and events in the data. However, in the presence of large data corpora, even a carefully constructed search query may return a large number of unintended matches. This work considers the problem of calculating a quality score for each match to the query, given that the underlying data are uncertain. We present a preliminary evaluation of three methods for determining both match quality scores and associated uncertainty bounds, illustrated in the context of an example based on overhead imagery data.

This report summarizes preliminary research into uncertainty quantification for pattern ana- lytics within the context of the Pattern Analytics to Support High-Performance Exploitation and Reasoning (PANTHER) project. The primary focus of PANTHER was to make large quantities of remote sensing data searchable by analysts. The work described in this re- port adds nuance to both the initial data preparation steps and the search process. Search queries are transformed from does the specified pattern exist in the data? to how certain is the system that the returned results match the query? We show example results for both data processing and search, and discuss a number of possible improvements for each.

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Modeling geospatial information with semantic graphs enables search for sites of interest based on relationships between features, without requiring strong a priori models of feature shape or other intrinsic properties. Geospatial semantic graphs can be constructed from raw sensor data with suitable preprocessing to obtain a discretized representation. This report describes initial work toward extending geospatial semantic graphs to include temporal information, and initial results applying semantic graph techniques to SAR image data. We describe an efficient graph structure that includes geospatial and temporal information, which is designed to support simultaneous spatial and temporal search queries. We also report a preliminary implementation of feature recognition, semantic graph modeling, and graph search based on input SAR data. The report concludes with lessons learned and suggestions for future improvements.

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This paper presents an implemented algorithm that automatically designs fixtures and assembly pallets to hold three-dimensional parts. The designed fixtures rigidly constrain and locate the part, obey task constraints, are robust to part shape variations, are easy to load, and are economical to produce. The algorithm is guaranteed to find the global optimum solution that satisfies these and other pragmatic conditions. We present the results of the algorithm applied to several practical manufacturing problems. For these complex problems the algorithm typically returns initial high-quality fixture designs in less than two minutes, and identifies th global optimum design in just over an hour.

Costs associated with designing and fabricating fixtures may be a significant portion of the total costs associated with a manufacturing task. The software tool, HoldFast, designs optimal fixtures that hold a single workpiece, are easily fabricated, provide rigid constraint and deterministic location of the workpiece, are robust to workpiece shape variations, obey all associated task constraints, and are easy to load and unload. We illustrate the capabilities of HoldFast by designing fixtures for several examples. Fixtures are designed and built for finish-machining and drilling of a cast part for prototype fabrication and mass-production fabrication. A pallet fixture is designed for vertical assembly of a personal cassette player. Another pallet fixture is designed and built that will hold either the personal cassette player or a glue gun during assembly.

Successful robot systems must employ actions that are robust in the face of task uncertainty. Toward this end, Lozano-Perez, Mason, and Taylor developed a model of manipulation tasks that explicitly considers task uncertainty. In this paper we study the utility of this model applied to real-world tasks. We report the results of two experiments that highlight the strengths and weaknesses of the LMT approach. The first experiment showed that the LMT formalism can successfully plan solutions for a complex real-world task. The second experiment showed a task that the formalism is fundamentally incapable of solving.

A key feature distinguishing robotics from traditional computer science is its connection to the physical world. Robot planning software may use elegant algorithms supported by ironclad analytic proofs, but ultimately nature will decide whether the software output is correct in the sense of accomplishing the task goal. Thus a chief goal of robotics research is to understand and capture this nature in a way that allows algorithmic analysis to produce robust physical results. This is made particularly difficult by the presence of uncertainty, which arises from the inevitable discrepancy between the real task and its idealized computer model. This paper reviews fundamental sets of states, forces, and actions that exist for a broad class of robot manipulation tasks, and ties these sets to past and future approaches to developing robust manipulation planning and execution systems.

This paper addresses the problem of manipulation planning in the presence of uncertainty. We begin by reviewing the worst-case planning techniques introduced in and show that these methods are hampered by an information gap inherent to worst-case analysis techniques. As the task uncertainty increases, these methods fail to produce useful information even though a high-quality plan may exist. To fill this gap, we present the probabilistic backprojection, which describes the likelihood that a given action will achieve the task goal from a given initial state. We provide a constructive definition of the probabilistic backprojection and related probabilistic models of manipulation task mechanics, and show how these models unify and enhance several past results in manipulation planning. These models capture the fundamental nature of the task behavior, but appear to be very complex. Methods for computing these models are sketched, but efficient computational methods remain unknown.

This paper presents two algorithms that construct a set of initial (x, y, {theta}) configurations from which a given action will reliably accomplish a planar manipulation task. The first algorithm applies energy arguments to construct a conservative set of successful initial configurations, while the second algorithm performs numerical integration to construct a set that is much less conservative. The algorithms may be applied to a variety of tasks, including pushing, placing-by-dropping, and force-controlled assembly tasks. Both algorithms consider the task geometry and mechanics, and allow uncertainty in every task parameter except for the object shapes. Experimental results are presented which demonstrate the validity of the algorithms' output for two example manipulation tasks. 16 refs., 8 figs.