SEARCH · Search NASA

Results for “Pose estimation”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Field Validation of Electrocoagulation Treatment for Oily Wastewater at a Steel Mill

Oily wastewater is a significant byproduct across various industries, including steel production, posing environmental and health risks. Steelmaking is particularly water-intensive, with an estimated 75,000 gallons required to produce 1 ton of steel, although recycling reduces this to 13,000–23,000 gallons per ton.1 Water serves multiple purposes in steelmaking, including equipment cooling, scale removal, steam generation, lubrication, and pollution control.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1996

Orbital debris removal using ground-based lasers

Orbiting the Earth are spent rocket stages, non-functioning satellites, hardware from satellite deployment and staging, fragments of exploded spacecraft, and other relics of decades of space exploration: orbital debris. The United States Space Command tracks and maintains a catalog of the largest objects. The catalog contains over 7000 objects. Recent studies have assessed the debris environment in an effort to estimate the number of smaller particles and the probability of a collision causing catastrophic damage to a functioning spacecraft. The results of the studies can be used to show, for example, that the likelihood of a collision of a particle larger than about one centimeter in diameter with the International Space Station during a 10-year period is a few percent, roughly in agreement with earlier estimates for Space Station Freedom. Particles greater than about one centimeter in diameter pose the greatest risk to shielded spacecraft. There are on the order of 105 such particles in low Earth orbit. The United States National Space Policy, begun in 1988, is to minimize debris consistent with mission requirements. Measures such as venting unused fuel to prevent explosions, retaining staging and deployment hardware, and shielding against smaller debris have been taken by the U.S. and other space faring nations. There is at present no program to remove debris from orbit. The natural tendency for upper atmospheric drag to remove objects from low Earth orbit is more than balanced by the increase in the number of debris objects from new launches and fragmentation of existing objects. In this paper I describe a concept under study by the Program Development Laboratory of Marshall Space Flight Center and others to remove debris with a ground-based laser. A longer version of this report, including figures, is available from the author.

Taylor, Charles R.↗

NASA NTRS1996

First-Order System Least-Squares for the Navier-Stokes Equations

This paper develops a least-squares approach to the solution of the incompressible Navier-Stokes equations in primitive variables. As with our earlier work on Stokes equations, we recast the Navier-Stokes equations as a first-order system by introducing a velocity flux variable and associated curl and trace equations. We show that the resulting system is well-posed, and that an associated least-squares principle yields optimal discretization error estimates in the H(sup 1) norm in each variable (including the velocity flux) and optimal multigrid convergence estimates for the resulting algebraic system.

Bochev, P.↗

NASA NTRS2010

Human Research Program Space Radiation Standing Review Panel (SRP)

The Space Radiation Standing Review Panel (SRP) met at the NASA Johnson Space Center (JSC) on December 9-11, 2009 to discuss the areas of current and future research targeted by the Space Radiation Program Element (SRPE) of the Human Research Program (HRP). Using evidence-based knowledge as a background for identified risks to astronaut health and performance, NASA had identified gaps in knowledge to address those risks. Ongoing and proposed tasks were presented to address the gaps. The charge to the Space Radiation SRP was to review the gaps, evaluate whether the tasks addressed these gaps and to make recommendations to NASA s HRP Science Management Office regarding the SRP's review. The SRP was requested to evaluate the practicality of the proposed efforts in light of the demands placed on the HRP. Several presentations were made to the SRP during the site visit and the SRP spent sufficient time to address the SRP charge. The SRP made a final debriefing to the HRP Program Scientist, Dr. John B. Charles, on December 11, 2009. The SRP noted that current SRPE strategy is properly science-based and views this as the best assurance of the likelihood that answers to the questions posed as gaps in knowledge can be found, that the uncertainty in risk estimates can be reduced, and that a solid, cost-effective approach to risk reduction solutions is being developed. The current approach of the SRPE, based on the use of carefully focused research solicitations, requiring thorough peer-review and approaches demonstrated to be on the path to answering the NASA strategic questions, addressed to a broad extramural community of qualified scientists, optimally positioned to take advantage of serendipitous discoveries and to leverage scientific advances made elsewhere, is sound and appropriate. The SRP viewed with concern statements by HRP implying that the only science legitimately deserving support should be "applied" or, in some instances that the very term "research" might be frowned upon. We understand the desire of management to ensure that research stay focused on mission objectives, but the terms used are code words fraught with different meaning for scientists. Such expressions, taken at face value, convey a profoundly flawed view of science, can easily lead down counterproductive paths, and have the potential to irretrievably corrupt NASA requirements. The SRP understands and endorses the mandate to keep research efforts focused on the mission needs. However, thoughtful application of knowledge gained by understanding the mechanisms and pathways of biological effects cannot be replaced.

Woloschak, Gayle↗

NASA NTRS2022

Calculation of Transformer Leakage Inductance by Simplified Flux Path Geometries

Series inductance is a critical energy storage element in isolated power converters. Many have explored methods of estimating the total leakage inductance of transformers as an integrated series inductance without external inductors. However, this poses challenges with medium frequency converters and magnetic ribbon-based cores, e.g., metal amorphous nanocrystalline. The material’s relatively high conductivity allows eddy currents to develop when magnetic flux intersects wide surfaces. These eddy currents result in significant additional losses that increase with increasing power flow. This paper presents a simplified and accurate method of estimating leakage flux while identifying separate leakage flux paths that correlate with the magnetic material surfaces. This enables an understanding the proportion of leakage flux that will contribute to extra losses. The methods presented are applicable to both leakage and fringing (around heterogeneous material interfaces) fluxes. This paper includes detailed FEA studies and 3D flux vector measurements matching the presented analytical models.

transformer↗

NASA NTRS2007

[Space Weather Impact on the Electricity Market]

Forbes & St. Cyr (2004, hereafter "FISC") have provided evidence that the electricity market can be impacted by space weather. Our analysis indicated that the estimated market impact for PJM was 3.7 % or approximately $500 million dollars over the 19 month sample period. Kappenman has taken exception to this estimate and contends that we have exaggerated the magnitude of the problem that space weather poses to PJM. There are four specific issues: (1) he claims that we have ignored relevant literature; (2) he asserts that Dst is not an appropriate proxy for GICs in PJM; (3) he charges that our findings are inconsistent with the impact of the 17 September 2000 storm; and (4) he alleges that our discussion of October 2003 storms is misleading. In our article, we have explained our methodology, multivariate regression analysis, with a particular focus on how it compares to correlation analysis. We have also explained the limitations of our analysis. We noted that "...While the Dstlprice relationship was found to be robust, the precise estimate should be treated with a relatively high degree of caution given that econometric modeling is not an exact science as well as the fact that the measure of space weather may be a poor proxy for GICs" (paragraph 96). We have also noted that additional research using local magnetometer data are needed (paragraph 97). We did not claim that that our findings for PJM are representative of the impact of space weather on other power grids. On the contrary, we noted that ... "Only analysis of other power grids will tell. " (paragraph 97). Kappenman inaccurately asserts that we have indicated that our findings . . . "imply much higher total costs are likely across the US and elsewhere in the world." He also inaccurately asserts that we have claimed that " . . . Dst is the most suited proxy for GIC in the PJM grid.. ." Moreover, he inaccurately refers to our analysis as a correlation study that uses Dst as quasi-binary indicator.

SaintCyr, O. Chris↗

NASA NTRS2022

Shape Servoing of Deformable Objects using Adaptive Deformation Model Estimation

In this paper, we propose an adaptive shape servoing method to deform a soft object into a desired 3-D shape. The high dimensional representation and the unknown deformation properties of the soft object pose a challenge to actively manipulate its shape. To address this issue, we develop a method to compute the deformation Jacobian matrix in real-time. The Jacobian is estimated using a set of basis functions and its corresponding parameters to capture the dynamics of the system and relate the applied input motion to changes in the soft object's shape. An integral concurrent learning (ICL) based adaptive update law is derived using Lyapunov analysis to estimate the deformation parameters and prove its convergence. A physics-based simulation is used to validate the proposed method and controller by performing manipulation tasks with different desired configurations. The performance is compared with a standard gradient update law to demonstrate the accuracy and robustness of our approach.

Vrithik Raj Guthikonda↗

DOE OSTI2026

Risk-Aware Measurement Synchronization and Recovery for DSSE With Heterogeneous Data Sources

Power distribution systems are increasingly integrating heterogeneous sensors with varying data reporting rates and types, which pose challenges to achieving observability at the desired temporal resolution of distribution system state estimation (DSSE). Multisensor failures caused by extreme events exacerbate these issues, introducing substantial uncertainties into DSSE. This article proposes a novel solution to these challenges by ensuring high-resolution system observability despite heterogeneous data sources and multisensor failures. First, a deep learning architecture combining long short-term memory (LSTM) and graph convolutional network (GCN) is employed to synchronize meters with different reporting rates, aiming to achieve system observability. A random-walk-model-based approach is introduced to generate pseudo-measurements while properly characterizing their uncertainties under multisensor failures. Finally, a disaster-risk-informed observability metric (RiOM) is defined to quantify the uncertainty associated with state estimation results. The proposed framework offers deeper insights into the system observability on the fly compared with conventional analysis. The effectiveness of the framework is demonstrated on an IEEE standard test case and a large-scale real-world distribution feeder in mid-Minnesota in the U.S.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1979

Fundamental mechanisms that influence the estimate of heat transfer to gas turbine blades

Estimates of the heat transfer from the gas to stationary (vanes) or rotating blades poses a major uncertainty due to the complexity of the heat transfer processes. The gas flow through these blade rows is three dimensional with complex secondary viscous flow patterns that interact with the endwalls and blade surfaces. In addition, upstream disturbances, stagnation flow, curvature effects, and flow acceleration complicate the thermal transport mechanisms in the boundary layers. Some of these fundamental heat transfer effects are discussed. The chief purpose of the discussion is to acquaint those in the heat transfer community, not directly involved in gas turbines, of the seriousness of the problem and to recommend some basic research that would improve the capability for predicting gas-side heat transfer on turbine blades and vanes.

Graham, R. W.↗

NASA NTRS1986

Vela X-1 pulse timing. I - Determination of the neutron star orbit

Analysis and interpretation of observations of the pulsed X-ray emission from Vela X-1 are presented. The interrelated tasks of determining the neutron-star orbit and calculating pulse emission times in the rest frame of the source are considered, with the underlying motivation to utilize this time series for analysis of fluctuations in the pulse emission frequency, which are interpreted as noise in the neutron-star rotation rate. However, the redness and strength of this noise in Vela X-1 pose a nontrivial complication, because the parameters of the neutron-star orbit are estimated by pulse timing and hence are themselves affected by fluctuations in the pulse emission frequency. A second challenge is presented by substantial fluctuations in the Vela X-1 pulse shape that, if ignored, would significantly reduce both the precision of the orbital solution and the ability to study fluctuations in the rotation rate. The report emphasizes the analysis techniques that have been developed to obtain the most precise orbital solution possible in the face of these difficulties. These techniques are general and may be applied to other pulsars that exhibit substantial fluctuations in pulse shape and frequency, such as Her X-1 and Cen X-3.

Boynton, P. E.↗

NASA NTRSDate not supplied

Vision-Based Distributed Sensing at Vertiports for Advanced Air Mobility and Urban Air Mobility Approach and Landing

Advanced Air Mobility (AAM) encompasses a broad vision for air transportation, including Urban Air Mobility (UAM) as a subset. AAM aims to create a more connected and efficient transportation network across various geographical settings. However, navigating AAM aircraft in GPS-denied or degraded environments during approach and landing is challenging. Traditional vision aids like glideslopes and localizers are limited in vertiport environments due to narrow beam constraints and reduced landing angle options. This paper addresses the need for accurate navigation solutions at vertiports by proposing a vision-based distributed sensing (VIDIS) system utilizing cameras with bundle adjustment to assist incoming AAM aircraft during approach and landing while monitoring surface movements to enhance safety and efficiency. Key focus areas for current and future vertiport developers include identifying suitable sensor types and infrastructure standards to support AAM operations and including vertiport markings as vision-based navigation aids. The proposed system offers a novel approach to overcoming navigation challenges in AAM operations, particularly in urban settings where traditional aids may be insufficient. Preliminary simulation results with distributed cameras demonstrate promising outcomes for implementing bundle adjustment techniques to enhance vision-based navigation solutions at vertiports. Generating waypoint-based trajectories via waypoint integration using explicit guidance synthesis (WINGS) creates smooth AAM trajectories for landing at vertiports by using the current waypoint's terminal conditions as the initial conditions for the next waypoint. Combining bundle adjustment's ground-based solution of vertiport features with WINGS, Coplanar Pose from Orthography and Scaling with Iterations (COPOSIT), and an extended Kalman filter (EKF) estimates the state of an incoming aircraft during approach and landing at vertiports. Future work includes testing VIDIS in a high-fidelity simulation and with real-world data.

Distributed sensing↗

NASA NTRSDate not supplied

Vision-Based Distributed Sensing at Vertiports for Advanced Air Mobility and Urban Air Mobility Approach and Landing

Distributed sensing↗

NASA NTRS1986

The ill-posed nature of the satellite temperature retrieval problem and the limits of retrievability

The inverse problem of satellite temperature profile retrieval is well known to be ill-posed. This means that not only is a vertical temperature profile solution not unique, but that two solutions can be very different from each other. A set of atmosphere-like, and true atmospheric examples of significantly dissimilar inverse solutions, were sought and found, using an 11-channel simulated HIRS sounding radiometer. Using the Riemann-Lebesgue Lemma for guidance, it is shown that simultaneous, numerical solutions of an atmospheric character may differ by as much as 10 K between 10-1000 mb. However, an empirical search for dissimilar solutions in the natural atmosphere reveals an extremely low probability of finding two significantly different RAOBs which produce radiance measurements whose differences cannot be resolved by the satellite radiometer. The empirical results are used to derive a first estimate of the limits of retrievability, analogous to the limits of predictability derivable from the ill-posed nature of the numerical weather prediction problem.

Thompson, Owen E.↗

NASA NTRS2015

Raven: An On-Orbit Relative Navigation Demonstration Using International Space Station Visiting Vehicles

Since the last Hubble Servicing Mission five years ago, the Satellite Servicing Capabilities Office (SSCO) at the NASA Goddard Space Flight Center (GSFC) has been focusing on maturing the technologies necessary to robotically service orbiting legacy assets-spacecraft not necessarily designed for in-flight service. Raven, SSCO's next orbital experiment to the International Space Station (ISS), is a real-time autonomous non-cooperative relative navigation system that will mature the estimation algorithms required for rendezvous and proximity operations for a satellite-servicing mission. Raven will fly as a hosted payload as part of the Space Test Program's STP-H5 mission, which will be mounted on an external ExPRESS Logistics Carrier (ELC) and will image the many visiting vehicles arriving and departing from the ISS as targets for observation. Raven will host multiple sensors: a visible camera with a variable field of view lens, a long-wave infrared camera, and a short-wave flash lidar. This sensor suite can be pointed via a two-axis gimbal to provide a wide field of regard to track the visiting vehicles as they make their approach. Various real-time vision processing algorithms will produce range, bearing, and six degree of freedom pose measurements that will be processed in a relative navigation filter to produce an optimal relative state estimate. In this overview paper, we will cover top-level requirements, experimental concept of operations, system design, and the status of Raven integration and test activities.

Rendezvous↗

NASA NTRS1990

On three dimensional object recognition and pose-determination: An abstraction based approach

A method of computing reliable Gaussian and mean curvature sign-map descriptors from the polynomial approximation of surfaces was demonstrated. Such descriptors which are invariant under perspective variation are suitable for hypothesis generation. A means for determining the pose of constructed geometric forms whose algebraic surface descriptors are nonlinear in terms of their orienting parameters was developed. This was done by means of linear functions which are capable of approximating nonlinear forms and determining their parameters. It was shown that biquadratic surfaces are suitable companion linear forms for cylindrical approximation and parameter estimation. The estimates provided the initial parametric approximations necessary for a nonlinear regression stage to fine tune the estimates by fitting the actual nonlinear form to the data. A hypothesis-based split-merge algorithm for extraction and pose determination of cylinders and planes which merge smoothly into other surfaces was developed. It was shown that all split-merge algorithms are hypothesis-based. A finite-state algorithm for the extraction of the boundaries of run-length regions was developed. The computation takes advantage of the run list topology and boundary direction constraints implicit in the run-length encoding.

Quek, Kok How Francis↗

NASA NTRS2006

Attenuating Stereo Pixel-Locking via Affine Window Adaptation

For real-time stereo vision systems, the standard method for estimating sub-pixel stereo disparity given an initial integer disparity map involves fitting parabolas to a matching cost function aggregated over rectangular windows. This results in a phenomenon known as 'pixel-locking,' which produces artificially-peaked histograms of sub-pixel disparity. These peaks correspond to the introduction of erroneous ripples or waves in the 3D reconstruction of truly Rat surfaces. Since stereo vision is a common input modality for autonomous vehicles, these inaccuracies can pose a problem for safe, reliable navigation. This paper proposes a new method for sub-pixel stereo disparity estimation, based on ideas from Lucas-Kanade tracking and optical flow, which substantially reduces the pixel-locking effect. In addition, it has the ability to correct much larger initial disparity errors than previous approaches and is more general as it applies not only to the ground plane.

pixel locking effect↗

DOE OSTI2025

An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change

Abstract. This study investigated the computational benefits of using multi-fidelity statistical estimation (MFSE) algorithms to quantify uncertainty in the mass change of Humboldt Glacier, Greenland, between 2007 and 2100 using a single climate change scenario. The goal of this study was to determine whether MFSE can use multiple models of varying cost and accuracy to reduce the computational cost of estimating the mean and variance of the projected mass change of a glacier. The problem size and complexity were chosen to reflect the challenges posed by future continental-scale studies while still facilitating a computationally feasible investigation of MFSE methods. When quantifying uncertainty introduced by a high-dimensional parameterization of the basal friction field, MFSE was able to reduce the mean-squared error in the estimates of the statistics by well over an order of magnitude when compared to a single-fidelity approach that only used the highest-fidelity model. This significant reduction in computational cost was achieved despite the low-fidelity models used being incapable of capturing the local features of the ice-flow fields predicted by the high-fidelity model. The MFSE algorithms were able to effectively leverage the high correlation between each model's predictions of mass change, which all responded similarly to perturbations in the model inputs. Consequently, our results suggest that MFSE could be highly useful for reducing the cost of computing continental-scale probabilistic projections of sea-level rise due to ice-sheet mass change.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Using Machine Learning to Estimate Surface-Level SO2 Concentrations from Satellite-Based Measurements

Sulfur dioxide (SO2) is a criteria air pollutant due to its contributions to aerosol formation, rainfall acidification, and harm to human health. The placement of air quality monitoring sites is typically biased towards urban areas, leaving large areas with very limited monitoring data. The Ozone Monitoring Instrument (OMI) has been used to provide estimates of SO2 vertical column densities (VCDs) globally at spatial resolution of 10s of kms once per day. OMI SO2 VCDs have been previously used to estimate surface SO2 concentrations using chemical transport model (CTM) simulations. The CTMs use estimated emissions and assimilated meteorological data, and simulate the chemical and physical processes that determine the vertical profile of SO2, which can be used to derive a ratio between the surface concentrations and VCDs. These models are complex, computationally expensive, and have large uncertainties in the simulated surface-to-VCD ratio due to biases in emissions and relatively coarse resolution. Machine learning techniques are comparatively easier to use, much less computationally expensive to use after training, and can produce more accurate estimations of surface concentrations than the CTM-based method. The interpretation of machine learning models often poses challenges, and in some cases, non-physical variables unrelated to SO2 are used as predictors. In this work, we create an artificial neural network (ANN) to relate OMI retrievals and archived GEOS-FP boundary layer heights to surface SO2 concentrations from the ChinaHighAirPollutants ChinaHighSO2 dataset (CHAP; Wei et al., 2023) on a seasonal average timescale from 2013-2018. Our model only utilizes five variables that are directly relevant to the satellite retrieval, lifetime, and spatial distribution of SO2. The model was trained on 16 seasons (four of each) with independent validation (one of each season) and testing datasets (one of each season) to avoid overfitting. Our ANN generates surface SO2 concentrations that are sensitive (slope = 0.51) and consistent (r = 0.74) with the CHAP data, but are underpredicted by an average of 1.2 ppbv with a mean absolute error of 2.2 ppbv. These results are better than recent studies utilizing the CTM method. To our knowledge, this is the best performing machine learning model that only uses physical variables to predict surface SO2. Our work demonstrates that a carefully constructed, simple ML model can accurately estimate surface-based SO2 concentrations from satellite VCD measurements, and this technique has future promise to expend to newer, higher resolution satellites and other air pollutants.

SO2, air quality, OMI, machine learning↗