Results for “Estimation” — Page 31

NASA NTRS2021

TLSLEAF: Automatic Leaf Angle Estimates From Single-Scan Terrestrial Laser Scanning

Leaf angle distribution (LAD) in forest canopies affects estimates of leaf area, light interception, and global-scale photosynthesis, but is often simplified to a single theoretical value. Here, we present TLSLeAF (Terrestrial Laser Scanning Leaf Angle Function), an automated open-source method of deriving LADs from terrestrial laser scanning. TLSLeAF produces canopy-scale leaf angle and LADs by relying on gridded laser scanning data. The approach increases processing speed, improves angle estimates, and requires minimal user input. Key features are automation, leaf–wood classification, beta parameter output, and implementation in R to increase accessibility for the ecology community. TLSLeAF precisely estimates leaf angle with minimal distance effects on angular estimates while rapidly producing LADs on a consumer-grade machine. We challenge the popular spherical LAD assumption, showing sensitivity to ecosystem type in plant area index and foliage profile estimates that translate to c. 25% and c. 11% increases in canopy net photosynthesis (c. 25%) and solar-induced chlorophyll fluorescence (c. 11%). TLSLeAF can now be applied to the vast catalog of laser scanning data already available from ecosystems around the globe. The ease of use will enable widespread adoption of the method outside of remote-sensing experts, allowing greater accessibility for addressing ecological hypotheses and large-scale ecosystem modeling efforts.

3D↗

NASA NTRS2013

Assuring software cost estimates : is it an oxymoron?

The software industry repeatedly observes cost growth of well over 100% even after decades of cost estimation research and well-known best practices, so “What’s the problem?” In this paper we will provide an overview of the current state of software cost estimation best practice. We then explore whether applying some of the methods used in software assurance might improve the quality of software cost estimates. This paper especially focuses on issues associated with model calibration, estimate review, and the development and documentation of estimates as part of an integrated plan.

Hihn, Jairus↗

NASA NTRS2021

Collaborative Pose Estimation of An Unknown Target Using Multiple Spacecraft

A reliable method for pose estimation of an unknown and uncooperative space target using monocular vision remains an open problem. Vision-based pose determination can be challenging in case of unfavorable illumination, time-varying conditions due to rotational motion and relative orbit, and scale ambiguity resolution. To address these challenges, we propose a novel collaborative pose determination algorithm called Multi- Spacecraft Simultaneous Estimation of Pose and Shape algorithm or M-SEPS.Within M-SEPS, a team of chaser spacecraft, each equipped with a monocular camera, exchange information over a local network to jointly estimate the relative kinematic state of the target and its sparse shape landmarks. In this approach, each spacecraft processes its own images and observes particular target landmarks in parallel and in a distributed fashion. Then, the local network is exploited by the spacecraft to share their consensus proposals and aggregate them to achieve the joint estimate. We validate our algorithm using simulations of relative orbits and observations, captured by each chaser spacecraft. To the best of the authors’ knowledge, this is the first cooperative, vision-based algorithm for estimating the pose and shape of a space object for an arbitrary number of spacecraft.

Chung, Soon-Jo↗

NASA NTRS2022

California Agriculture: Using Earth Observations to Estimate the Age and Carbon Stock of Perennial Agriculture

California seeks to become a carbon neutral state by 2045. To track progress toward this goal, it is important to quantify the amount of carbon stored by various landcover types across the state. Vineyards and orchards make up a large portion of California's agricultural landcover and store considerable amounts of carbon. For this project, NASA DEVELOP partnered with the California Air Resources Board to estimate the age and carbon stock of crop-specific agricultural regions across California between 1984 and 2021. The DEVELOP team created the Perennial Agriculture Age and Carbon Estimation Tool (PAACET), a Google Earth Engine tool that employs Earth observation from the Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI) to estimate the age and carbon stock of vineyards and almond, walnut, pistachio, and orange orchards. The team used an ocular sampling accuracy assessment consisting of 53 sample vineyards and orchards and found that on average, PAACET estimated ages within ±4.3 years of their actual age. The tool estimated that vineyards and walnut orchards are the oldest woody croplands, and almond orchards store the largest total carbon.

Rachael Ross↗

NASA NTRS2021

Three-dimensional estimation of deciduous forest canopy structure and leaf area using multi-directional, leaf-on and leaf-off airborne lidar data

Airborne laser scanning (ALS) has been widely used to map gap probability and leaf area index (LAI) distribution at plot and landscape scales. As an indirect measurement, most ALS methods to estimate LAI combine waveform or point density information with supporting field measurements such as the leaf angle distribution, gap probability, or direct LAI measures. The development of a more independent estimation approach would facilitate more widespread use of existing ALS data to investigate patterns of forest structure and build realistic 3-D vegetation scenes to simulate remote sensing imagery and energy balance. Here, we develop a data processing workflow (named PVlad) using ALS point cloud apparent reflectance to estimate LAI and voxel-based leaf area density (LAD), aiming to reduce the need for associated field measurements such as the gap probability. The adaptation of the path volume (PV) concept derived from apparent reflectance integrates information from multi-directional ALS pulses, and quantifies the percentage exploration of each voxel for classification and occlusion correction, such that rigorous volumetric sampling approaches can be developed to derive LAI and LAD. The PVlad workflow was applied to discrete-return lidar data (Riegl VQ480i) acquired by NASA Goddard's LiDAR, Hyperspectral and Thermal Imager (G-LiHT) Airborne Imager during leaf-on (summer) and leaf-off (spring) conditions at the Smithsonian Environmental Research Center (SERC). The estimates of LAI and LAD captured structural differences between mature, logged, and intermediate-aged stands over eight deciduous forest plots. The derived LAI values were compared to field litter collection measurements, and the derived LAD vertical distribution was compared to the output of the VoxLAD model using terrestrial laser scan (TLS) field survey data. Using voxel sizes ranging from 0.5 m to 5 m, overall LAI estimation showed linear fitting coefficient bias and for 1 and 2 m voxel sizes, and vertical LAD distribution showed strong correlation with and for 0.5 and 1m voxel sizes. For every forest stand, upper-canopy LAD had a low variance for voxel sizes of ≤ . Application of PVlad to the G-LiHT and other similar ALS data archives enables the development of fine-resolution LAI map products, including voxelization of LAD for ecosystem science and radiative transfer simulations of remote sensing imagery or surface energy balance.

Tiangang Yin↗

NASA NTRS2022

Development and Evaluation of Ensemble Consensus Precipitation Estimates over High Mountain Asia

Precipitation estimates are highly uncertain in complex regions such as High-Mountain Asia (HMA), where ground measurements are very difficult to obtain, and atmospheric dynamics poorly understood. Though gridded products derived from satellite-based observations and/or reanalysis can provide temporally and spatially distributed estimates of precipitation, there are significant inconsistencies in these products. As such, to date, there is little agreement in the community on the best and most accurate gridded precipitation product in HMA, which is likely area dependent because of HMA’s strong heterogeneities and complex orography. Targeting these gaps, this article presents the development of a consensus ensemble precipitation product using three gridded precipitation datasets (the Integrated Multi-satellitE Retrieals for Global Precipitation Measurement IMERG, the Climate Hazards group Infrared Precipitation with Stations CHIRPS, and the ECMWF Reanalysis ERA5) with a localized probability matched mean (LPM) approach. We evaluate the performance of the LPM estimate along with a simple ensemble mean (EM) estimate to overcome the differences and disparities of the three selected constituent products on long-term averages and trends in HMA. Our analysis demonstrates that LPM reduces the high biases embedded in the ensemble members and provides more realistic spatial patterns compared to EM. LPM is also a good alternative for merging data products with different spatio-temporal resolutions. By filtering disparities among the individual ensemble members, LPM overcomes the problem of a certain product performing well only in a particular area and provides a consensus estimate with plausible temporal trends.

Fadji Z Maina↗

NASA NTRSDate not supplied

The Relationship Between Two Methods for Estimating Uncertainties in Data Assimilation

This note examines the relationship between two apparently unrelated methods for estimating error statistics or uncertainties of relevance to data assimilation. The first method is due to (Desroziers et al., 2005, Q. J. R. Meteorol. Soc., 131, 3385–3396; referred to as DBCP hereafter) and relies on residual statistics readily available from data assimilation applications. The second method, the three- cornered hat (3CH) developed by Gray and Allan (1974, IEEE 28th Annual Symp. Freq. Control, 243–246), only recently applied to atmospheric sciences, uses three data sets and can derive estimates of relevant error uncertainties as well. The usefulness of both methods lies in them not requiring knowledge of the true value of the quantities at play. DBCP derives its results by relying explicitly on the constraints as- sociated with the data assimilation minimization problem; 3CH is general and its estimates hold as long as errors in the three data sets of choice are uncorrelated. Establishing the relationship between the methods requires applying the 3CH approach to the same observation, background, and analysis data sets used by DBCP. In this case, the same assumptions of DBCP on residual errors allow for cancellation of error cross–covariance terms in 3CH such that two of its corners derive identical estimates for observation and background error covariances as those of DBCP. The error cross–covariance terms associated with the third corner are shown to add up to twice the analysis error covariance so that the 3CH estimate for the third corner recovers the negative of the analysis error covariance. Illustrations of these findings are provided by deriving uncertainties for radio occultation bending angles.

Ricardo Todling↗

NASA NTRS2022

Estimates of Diurnal and Daily Net Primary Productivity Using the Geostationary Ocean Color Imager (GOCI) Data

For the past three decades, polar-orbiting ocean color satellites have provided local, regional to global scale estimates of oceanic net primary production that have greatly aided studies of ocean carbon cycling, food web dynamics and climate change. Despite considerable progress, accurate estimates of daily ocean productivity from space have not been realized because polar-orbiting satellites are unable to account for variations in phytoplankton physiology and carbon fixation rates from changing incident irradiance levels over the course of the day. Here we have attempted to exploit the unique short-temporal measurements provided by the Korean Geostationary Ocean Color Imager (GOCI), to obtain for the first time, diurnal and daily measurements of surface- and euphotic-column integrated Net Primary Productivity (NPP). These estimates are based on the Absorption Based Productivity Model (AbPM), and are calibrated with a bio-optical database of measurements made at different times of the day during the Korea-US Ocean Color (KORUS-OC) cruise in May–June 2016. Photo-physiological rate estimates, varied across different water types encountered around the Korean Peninsula, but on account of their limited number, were regionally scaled through the use of dynamic optical-biogeochemical (O-BGC) Biomes. Comparisons of GOCI derived estimates of daily and weekly integrated NPP against in-situ measurements, clearly underscore the superiority of geostationary over polar-orbiting ocean color satellites, which consider the strong short-term fluctuations in phytoplankton light absorption and incident irradiance fields over the course of the day.

Diurnal productivity↗

NASA NTRS2021

Center of Gravity Quasi-Static Load Estimate Using Random Vibration Responses

The quasi-static load (QSL) is often levied on flight hardware structural designers as a requirement. This requirement is verified either by test or analysis with engineers using different approaches to meet this requirement. A commonly used approach is to consider the rms acceleration Center of Gravity (CG) responses of test articles undergoing random vibration tests. The differences between structural engineers and/or test conductors within the space industry exist on how the data obtained from flight hardware random vibration tests are used to estimate QSLs. In this paper, a method to accurately estimate the CG grms from the acceleration power spectral densities with an appropriate sigma value to estimate peak CG accelerations is discussed. Recommendations are made to ensure that the community follows consistent QSL estimation from random vibration test data. The method of estimating the CG acceleration responses may also be used to response limit if the flight hardware responses exceed flight limit loads with margins.

Kolaini, Ali↗

NASA NTRS2022

Velocimeter LIDAR Based Bulk Velocity Estimation for Terrain Relative Navigation Applications

A novel batch state estimation approach to estimate the translational and angular velocity states of a vehicle using a state-of-the-art velocimeter Light Detection and Ranging (LIDAR) sensor for use in Terrain and Hazard Relative Navigation (TRN/HRN) applications is presented in this paper. The velocimeter LIDAR is capable of measuring three dimensional position and line-of-sight (LOS) velocity associated with every pixel in the field-of-view (FOV). This new batch state estimation methodology is shown to provide accurate and statistically consistent velocity estimates with no a priori information. In contrast to traditional computer vision approaches for TRN, the proposed technique is not dependent on image features. This feature alleviates the need for accurate feature detection and correspondence to a predefined map making it suitable for unknown operating environments. Following a detailed development of the mathematical details associated with the batch state estimation methodology, the efficacy and utility of the proposed algorithms are evaluated through emulation robotics experiments performed at Texas A&M’s Land, Air, and Space Robotics (LASR) laboratory.

Skulsky, Eli↗

NASA NTRS2022

Robust Vision-based Multi-spacecraft Guidance Navigation and Control using CNN-based Pose Estimation

In this paper, we present an end-to-end simulation framework for tracking an uncooperative Target spacecraft in Low Earth Orbit using a CubeSat-class Ego spacecraft outfitted with a camera. Currently, capturing high-fidelity realistic images in space for this scenario is difficult and exorbitantly expensive. Therefore, we developed a framework to simulate the spacecraft orbits in Basilisk software and generate high-fidelity realistic images of spacecraft in Unreal Engine, including the effects from Sun, Earth, Moon and stars. The Ego spacecraft uses cameras to capture images of the uncooperative Target and estimates its position and attitude using a CNN based 6DOF pose estimation pipeline, eliminating need for large SWAP-C(Size, Weight, Power and Cost) sensors like LIDAR or reliance on inter-spacecraft communication, This CNN, which is motivated by ESA’s Pose Estimation challenge of 2019, is trained using simulated data from our end-to-end simulation framework. We compare the performance of two distinct CNNbased algorithms for pose estimation along a nominal trajectory. In presence of non-Gaussian modeling uncertainties, the statedependent estimation error is characterized with a quadratic upper-bound. The quadratically-bounded error can be used by a robust controller to maneuver

Rahmani, Amir↗

NASA NTRS2023

Accounting for Dose Uncertainty in Dose-Response Curve Estimation Using Hierarchical Bayes Models

As part of their development of the technology for low-boom supersonic flight, the National Aeronautics and Space Administration (NASA) is planning to conduct a set of supersonic aircraft annoyance surveys in select communities in the United States, to measure public perception of the reduced sonic boom. The relationship between a noise exposure level of a supersonic flight event and the probability of an individual being highly annoyed by the event is quantified by a dose-response curve, which is usually based on logistic regression modeling. Unavoidable amounts of estimation error are expected in the upcoming noise measurements, which can bias the results of the logistic regression if ignored. Hence, the development of an estimation approach that accounts for such error when estimating the dose-response curve is imperative. In this paper, an evaluation study is conducted to assess the impact of measurement error on dose-response curve estimation. For this, hierarchical Bayes models using different specifications are fit to data collected by NASA in 2018, as part of a risk-reduction study of supersonic flights affecting Galveston, Texas. It is observed that the estimated dose-response curve appears sensitive to uncertainty in dose measurements, being subject to attenuation bias.

community response↗

NASA NTRS2023

Using Orbiting Carbon Observatory-2 (OCO-2) column CO2 retrievals to rapidly detect and estimate biospheric surface carbon flux anomalies

The global carbon cycle is experiencing continued perturbations via increases in atmospheric carbon concentrations, which are partly reduced by terrestrial biosphere and ocean carbon uptake. Greenhouse gas satellites have been shown to be useful in retrieving atmospheric carbon concentrations and observing surface and atmospheric CO2 seasonal-to-interannual variations. However, limited attention has been placed on using satellite column CO2 retrievals to evaluate surface CO2 fluxes from the terrestrial biosphere without advanced inversion models at low latency. Such applications could be useful to monitor, in near real time, biosphere carbon fluxes during climatic anomalies like drought, heatwaves, and floods, before more complex terrestrial biosphere model outputs and/or advanced inversion modelling estimates become available. Here, we explore the ability of Orbiting Carbon Observatory-2 (OCO-2) column-averaged dry air CO2 (XCO2) retrievals to directly detect and estimate terrestrial biosphere CO2 flux anomalies using a simple mass-balance approach. An initial global analysis of surface–atmospheric CO2 coupling and transport conditions reveals that the western US, among a handful of other regions, is a feasible candidate for using XCO2 for detecting terrestrial biosphere CO2 flux anomalies. Using the CarbonTracker model reanalysis as a test bed, we first demonstrate that a well-established mass-balance approach can estimate monthly surface CO2 flux anomalies from XCO2 enhancements in the western United States. The method is optimal when the study domain is spatially extensive enough to account for atmospheric mixing and has favorable advection conditions with contributions primarily from one background region. We find that errors in individual soundings reduce the ability of OCO-2 XCO2 to estimate more frequent, smaller surface CO2 flux anomalies. However, we find that OCO-2 XCO2 can often detect and estimate large surface flux anomalies that leave an imprint on the atmospheric CO2 concentration anomalies beyond the retrieval error/uncertainty associated with the observations. OCO-2 can thus be useful for low-latency monitoring of the monthly timing and magnitude of extreme regional terrestrial biosphere carbon anomalies.

Andrew F. Feldman↗

NASA NTRSDate not supplied

Preliminary Medical Risk Estimates and Clinical Capability Needs for Artemis IV

BACKGROUND Human exploration spaceflight missions to the Moon and Mars present unprecedented challenges for in- mission medical care. Compared with the ISS, the greater distance from Earth will mean increased mission durations, communication delays, limited to no resupply opportunities, and significant limitations on the evacuation of ill or injured crew. Spacecraft mass, volume, and power will be curtailed while higher demands will be placed on the crew’s knowledge, skills, and abilities. In this higher risk environment, it is important to: a) quantitatively estimate human system risk attributable to medical conditions, a process known as Probabilistic Risk Analysis, and b) use these estimates to inform medical system design. IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a PRA and medical trade space analysis tool developed by NASA to advance exploration mission medical system design. IMPACT v1.0 improves upon and will soon replace NASA’s existing tool, the Integrated Medical Model, with: a novel evidence base baselined to exploration environments; an expanded list of 119 medical conditions; a significant increase in the number of medical resources that can be utilized and in the flexibility of their use; and the modelling of time lost performing mission-specific tasks due to medical conditions. METHODOLOGY: This abstract will present IMPACT estimates of medical system risk and clinical capability needs for the Artemis IV mission. Artemis IV is currently scheduled for 2026 and will visit the Gateway space station in lunar orbit prior to the second lunar landing of the Artemis program. The baseline Artemis IV mission that was modeled was 28 days in duration with phases including Orion outbound, 4 days on the Gateway space station in lunar orbit, 2 crew on the surface of the Moon for approximately one week, an additional 5 days on Gateway, and then return to Earth. This baseline was compared to two alternative 34-day design reference missions (DRMs) that shifted the lunar sortie earlier or later in the mission profile. Assumptions included 2 female and 2 male crew and a notional medical system mass of 25 kg. Medical system risk estimates include loss of crew life (LOCL), consideration of medical evacuation (known as return to definitive care – RTDC), and an estimate of crew time lost due to medical conditions (Task Time Lost – TTL). The presentation will also describe the medical conditions that are the greatest drivers of risk as well as the clinical capabilities and resources that have the largest effect on risk. RESULTS: All three DRMs had very low probability of LOCL from medical conditions, primarily due to short duration. RTDC was also similar across the DRMs. In contrast, TTL was higher in the early lunar sortie DRM due to earlier occurrence of EVA-related medical conditions. Taken as a whole, there was no clinically significant difference in medical risk across the three missions. Results for clinical capabilities and an example medical equipment list will be discussed but were similar across DRMs.

D Hilmers↗

NASA NTRSDate not supplied

Medical Risk Estimates and Clinical Capability Needs for A Long Duration Artemis Mission

BACKGROUND Human exploration spaceflight missions to the Moon and Mars present unprecedented challenges for in-mission medical care. Compared with the ISS, the greater distance from Earth will mean increased mission durations, communication delays, limited to no resupply opportunities, and significant limitations on the evacuation of ill or injured crew. Spacecraft mass, volume, and power will be curtailed while higher demands will be placed on the crew’s knowledge, skills, and abilities. In this higher risk environment, it is important to: a) quantitatively estimate human system risk attributable to medical conditions, a process known as Probabilistic Risk Analysis, and b) use these estimates to inform medical system design. IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a PRA and medical trade space analysis tool developed by NASA to advance exploration mission medical system design. IMPACT v1.0 improves upon and will soon replace NASA’s existing tool, the Integrated Medical Model, with: a novel evidence base baselined to exploration environments; an expanded list of 119 medical conditions; a significant increase in the number of medical resources that can be utilized and in the flexibility of their use; and the modelling of time lost performing mission-specific tasks due to medical conditions. DISCUSSION: This abstract will present IMPACT estimates of medical system risk and clinical capability needs for an extended duration Artemis mission of approximately 9 months, with phases including outbound transit, 3 months on the Lunar Gateway space station, 3 months on the Lunar surface with EVAs, 3 months on Gateway (simulating the return phase of a Mars mission), and transit back to Earth. Medical system risk estimates include loss of crew life (LOCL), consideration of medical evacuation (known as return to definitive care – RTDC), and an estimate of crew time lost due to medical conditions (Task Time Lost – TTL). The presentation will also describe the medical conditions that are the greatest drivers of risk as well as the clinical capabilities and resources that have the largest effect on risk.

W Thompson↗

NASA NTRS2022

Medical Risk Estimates and Clinical Capability Needs for a Late Artemis Mission

BACKGROUND Human exploration spaceflight missions to the Moon and Mars present unprecedented challenges for in-mission medical care. Compared with the ISS, the greater distance from Earth will mean increased mission durations, communication delays, limited to no resupply opportunities, and significant limitations on the evacuation of ill or injured crew. Spacecraft mass, volume, and power will be curtailed while higher demands will be placed on the crew’s knowledge, skills, and abilities. In this higher risk environment, it is important to: a) quantitatively estimate human system risk attributable to medical conditions, a process known as Probabilistic Risk Analysis, and b) use these estimates to inform medical system design. IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a PRA and medical trade space analysis tool developed by NASA to advance exploration mission medical system design. IMPACT v1.0 improves upon and will soon replace NASA’s existing tool, the Integrated Medical Model, with: a novel evidence base baselined to exploration environments; an expanded list of 119 medical conditions; a significant increase in the number of medical resources that can be utilized and in the flexibility of their use; and the modelling of time lost performing mission-specific tasks due to medical conditions. DISCUSSION: This abstract will present IMPACT estimates of medical system risk and clinical capability needs for an extended duration Artemis mission of approximately 9 months, with phases including outbound transit, 3 months on the Lunar Gateway space station, 3 months on the Lunar surface with EVAs, 3 months on Gateway (simulating the return phase of a Mars mission), and transit back to Earth. Medical system risk estimates include loss of crew life (LOCL), consideration of medical evacuation (known as return to definitive care –RTDC), and an estimate of crew time list due to medical conditions (Task Time Lost –TTL). The presentation will also describe the medical conditions that are the greatest drivers of risk as well as the clinical capabilities and resources that have the largest effect on risk.

A Anderson↗

NASA NTRS2024

Preliminary Medical Risk Estimates and Clinical Capability Needs for Late Artemis Missions

BACKGROUND Human exploration spaceflight missions to the Moon and Mars present unprecedented challenges for in- mission medical care. Compared with the ISS, the greater distance from Earth will mean increased mission durations, communication delays, limited to no resupply opportunities, and significant limitations on the evacuation of ill or injured crew. Spacecraft mass, volume, and power will be curtailed while higher demands will be placed on the crew’s knowledge, skills, and abilities. In this higher risk environment, it is important to: a) quantitatively estimate human system risk attributable to medical conditions, a process known as Probabilistic Risk Analysis, and b) use these estimates to inform medical system design. IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a PRA and medical trade space analysis tool developed by NASA to advance exploration mission medical system design. IMPACT v1.0 improves upon and will soon replace NASA’s existing tool, the Integrated Medical Model, with: a novel evidence base baselined to exploration environments; an expanded list of 119 medical conditions; a significant increase in the number of medical resources that can be utilized and in the flexibility of their use; and the modelling of time lost performing mission-specific tasks due to medical conditions. METHODOLOGY: This abstract will present IMPACT estimates of medical system risk and clinical capability needs for the Artemis IV mission. Artemis IV is currently scheduled for 2026 and will visit the Gateway space station in lunar orbit prior to the second lunar landing of the Artemis program. The baseline Artemis IV mission that was modeled was 28 days in duration with phases including Orion outbound, 4 days on the Gateway space station in lunar orbit, 2 crew on the surface of the Moon for approximately one week, an additional 5 days on Gateway, and then return to Earth. This baseline was compared to two alternative 34-day design reference missions (DRMs) that shifted the lunar sortie earlier or later in the mission profile. Assumptions included 2 female and 2 male crew and a notional medical system mass of 25 kg. Medical system risk estimates include loss of crew life (LOCL), consideration of medical evacuation (known as return to definitive care – RTDC), and an estimate of crew time lost due to medical conditions (Task Time Lost – TTL). The presentation will also describe the medical conditions that are the greatest drivers of risk as well as the clinical capabilities and resources that have the largest effect on risk. RESULTS: All three DRMs had very low probability of LOCL from medical conditions, primarily due to short duration. RTDC was also similar across the DRMs. In contrast, TTL was higher in the early lunar sortie DRM due to earlier occurrence of EVA-related medical conditions. Taken as a whole, there was no clinically significant difference in medical risk across the three missions. Results for clinical capabilities and an example medical equipment list will be discussed but were similar across DRMs.

D. Hilmers↗

NASA NTRS2020

Shape Estimation for Elongated Deformable Object using B-spline Chained Multiple Random Matrices Model

In this paper, a B-spline chained multiple random matrix models (RMMs) representation is proposed to model geometric characteristics of an elongated deformable object. The hyper degrees of freedom structure of the elongated deformable object make its shape estimation challenging. Based on the likelihood function of the proposed B-spline chained multiple RMMs, an expectation-maximization (EM) method is derived to estimate the shape of the elongated deformable object. A split and merge method based on the Euclidean minimum spanning tree (EMST) is proposed to provide initialization for the EM algorithm. The proposed algorithm is evaluated for the shape estimation of the elongated deformable objects in scenarios, such as the static rope with various configurations (including configurations with intersection), the continuous manipulation of a rope and a plastic tube, and the assembly of two plastic tubes. The execution time is computed and the accuracy of the shape estimation results is evaluated based on the comparisons between the estimated width values and its ground-truth, and the intersection over union (IoU) metric.

Gang Yao↗