Results for “Risk algorithm” — Page 18

NASA NTRS2018

A Prognostics Framework Development for Swarm Satellite Formations

Prognostics is the science of predicting the failure(s) of a component or a system and understanding how the performance will change in the event of a failure or degradation mechanism. With accurate predictions of possible failures, autonomous mitigative actions can be taken to correct/repair any issues or alert human operators of a failure threshold exceedance requiring condition-based maintenance. Although there is extensive research on failure predictions for a component or a system, there are significantly more opportunities to foray into failure predictions and prognostics for a system of systems such as an airspace consisting of multiple aircraft, a fleet of unmanned aerial vehicles, and a swarm of intelligent satellite systems. Failure prediction and mitigation are particularly important in autonomous systems such as satellite swarm systems that need effective resource management and minimal human interactions. Based on NASA's decadal survey, there is a clear need to prioritize the development of satellite swarm technology for studies of space physics and Earth science. The science community will propose future missions that return in-situ measurements from a 3-D (three-dimensional) volume of space, with relative spacecraft motion and inter-satellite baselines controlled according to the mission objectives. For such multi-spacecraft missions, it is required that ground operations resources do not scale with the number of satellites, thus compromising the swarm or leading to inefficiencies in resource allocation. Swarms of tens or hundreds of small satellites will require autonomy in attitude control, navigation and failure. Although significant research has been conducted in the areas of autonomous formation flying algorithms, less attention has been given to the development of resilient systems robust to failures.The focus of this research paper is the integration of model-based prognostics into the swarm dynamics control and decision-making algorithms. We simulate swarm management strategies for a subsystem failure to demonstrate the importance of failure predictions by comparing two cases: (i) no health information is provided to the system and utilized in the decision-making process and (2) system health information is obtained using prognostics and employed by the control system. One example scenario presented is for the GPS (Global Positioning System) system of an individual satellite to perform off-nominally due to increasing estimated error. In this scenario, the keep-out zone for that satellite would become more conservative, thereby decreasing the risk of collision. This is achieved via tuning the individual artificial repulsive functions assigned to each satellite.This paper is structured as follows. First we provide an overview of current swarm technology development, where we specifically use the term swarm to define multiple satellites flying in formation in similar orbits, with cross-link communication and station-keeping capabilities. Second, we give an introduction to the Swarm Orbital Dynamics Advisor (SODA), a tool that accepts high-level configuration commands and provides the orbital maneuvers required to achieve the prescribed formation configuration. Third, we provide the details of the model-based prognostics algorithm implementation in SODA. Finally, we present different case studies for potential component/subsystem failures and the swarm responses based with and without failure prediction information.

prognostics↗

NASA NTRS2011

Attitude Estimation in Fractionated Spacecraft Cluster Systems

An attitude estimation was examined in fractioned free-flying spacecraft. Instead of a single, monolithic spacecraft, a fractionated free-flying spacecraft uses multiple spacecraft modules. These modules are connected only through wireless communication links and, potentially, wireless power links. The key advantage of this concept is the ability to respond to uncertainty. For example, if a single spacecraft module in the cluster fails, a new one can be launched at a lower cost and risk than would be incurred with onorbit servicing or replacement of the monolithic spacecraft. In order to create such a system, however, it is essential to know what the navigation capabilities of the fractionated system are as a function of the capabilities of the individual modules, and to have an algorithm that can perform estimation of the attitudes and relative positions of the modules with fractionated sensing capabilities. Looking specifically at fractionated attitude estimation with startrackers and optical relative attitude sensors, a set of mathematical tools has been developed that specify the set of sensors necessary to ensure that the attitude of the entire cluster ( cluster attitude ) can be observed. Also developed was a navigation filter that can estimate the cluster attitude if these conditions are satisfied. Each module in the cluster may have either a startracker, a relative attitude sensor, or both. An extended Kalman filter can be used to estimate the attitude of all modules. A range of estimation performances can be achieved depending on the sensors used and the topology of the sensing network.

Hadaegh, Fred Y.↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗

NASA NTRSDate not supplied

NASA CARA Tools

The NASA Conjunction Assessment Risk Analysis (CARA) team has created and posted to GitHub a set of MATLAB software tools for collision assessment (CA) analysis. The software is provided to the public under a NASA Open-Source Software Agreement and is maintained/updated by the CARA Analysis team. Capabilities provided within the toolset include algorithms in assessing probability of collision (Pc), collision consequence, covariance realism, Orbit Determination (OD) quality assessment, and single covariance max Pc. Additionally, a set of utilities are provided for astrodynamics topics closely related to CA, such as: Conjunction Data Message (CDM) parsing, covariance transformations, coordinate system transformations, etc. The repository is regularly updated on a monthly basis and new functionality will be added as algorithms are approved for public release. This presentation will provide a brief introduction to the toolset, highlight some of the key components which can be used in operations, and will present information on algorithms that are in the release pipeline and can be expected soon. This software will enable operators to independently perform CA calculations using the same core algorithms that CARA uses in operations.

L. Baars↗

NASA NTRSDate not supplied

NASA CARA Tools

The NASA Conjunction Assessment Risk Analysis (CARA) team has created and posted to GitHub a set of MATLAB software tools for collision assessment (CA) analysis. The software is provided to the public under a NASA Open-Source Software Agreement and is maintained/updated by the CARA Analysis team. Capabilities provided within the toolset include algorithms in assessing probability of collision (Pc), collision consequence, covariance realism, Orbit Determination (OD) quality assessment, and single covariance max Pc. Additionally, a set of utilities are provided for astrodynamics topics closely related to CA, such as: Conjunction Data Message (CDM) parsing, covariance transformations, coordinate system transformations, etc. The repository is regularly updated on a monthly basis and new functionality will be added as algorithms are approved for public release. This presentation will provide a brief introduction to the toolset, highlight some of the key components which can be used in operations, and will present information on algorithms that are in the release pipeline and can be expected soon. This software will enable operators to independently perform CA calculations using the same core algorithms that CARA uses in operations.

Luis Baars↗

NASA NTRSDate not supplied

The CLVTOPS Toolchain for NASA Space Launch System Liftoff Analysis and Post Flight Validation

This paper showcases the unique technical capabilities of the CLVTOPS multi-body flight dynamics toolchain developed by Marshall Space Flight Center (MSFC) for analyzing NASA’s Space Launch System (SLS) liftoff events. The CLVTOPS toolchain integrates high-fidelity simulations, geometric algorithms, advanced data analytics, and post-flight telemetry to demonstrate positive clearance between separating bodies and inform design decisions that enhance mission reliability. Proper liftoff separation is crucial to the success of the launch vehicle’s mission; vehicle impacts with the launch tower and supporting components incur a heightened risk of mission failure. For liftoff analysis, the CLVTOPS toolchain enables the integration of vehicle, launch pad, and environmental input models for the investigation of key clearance effectors. Furthermore, recent enhancements to the CLVTOPS toolchain allow for validation via photogrammetric trajectory reconstruction and plume pressure impingement estimation on the tower. The following sections will walk through the tool-chain, SLS liftoff ground rules and assumptions, key models, standard analysis, recent enhancements, and post-flight validation of the Artemis I mission liftoff event.

CLVTOPS↗

NASA NTRSDate not supplied

Space Launch System: CLVTOPS Toolchain for SLS Liftoff Separation Analysis

This presentation showcases the unique technical capabilities of the CLVTOPS multi-body flight dynamics tool chain developed by Marshall Space Flight Center (MSFC) for analyzing NASA’s Space Launch System (SLS) liftoff events. The CLVTOPS tool chain integrates high-fidelity simulations, geometric algorithms, advanced data analytics, and post-flight telemetry to demonstrate positive clearance between separating bodies and inform design decisions that enhance mission reliability. Proper liftoff separation is crucial to the success of the launch vehicle’s mission; vehicle impacts with the launch tower and supporting components incur a heightened risk of mission failure. For liftoff analysis, the CLVTOPS tool chain enables the integration of vehicle, launch pad, and environmental input models for the investigation of key clearance effectors. Furthermore, a novel capability of the CLVTOPS tool chain allows for verification and validation of trajectory reconstruction via photogrammetric imagery analysis. The following sections will walk through the tool chain, SLS liftoff ground rules and assumptions, model integration, pre-flight verification, and post-flight validation of the Artemis I mission liftoff event.

CLVTOPS↗

NASA NTRSDate not supplied

Collaborative Communications Between a Human and a Resilient Safety Support System

Successful introductory UAM integration into the NAS will be contingent on resilient safety systems that support reduced-crew flight operations. In this paper, we present a system that performs three functions: 1) monitors an operator’s physiological state; 2) assesses when the operator is experiencing anomalous states; and 3) mitigates risks by a combination of dynamic, context-based unilateral or collaborative dynamic function allocation of operational tasks. The monitoring process receives high data-rate sensor values from eye-tracking and electrocardiogram sensors. The assessment process takes these values and performs a classification that was developed using machine learning algorithms. The mitigation process invokes a collaboration protocol called DFACC to which, based on context, performs vehicle operations that the operator would otherwise routinely execute. This system has been demonstrated in a UAM flight simulator for an operator incapacitation scenario. The methods and initial results as well as relevant UAM and AAM scenarios will be described.

Advanced Air Mobility,↗

NASA NTRS2021

Collaborative Communications Between A Human and A Resilient Safety Support System

Successful introductory UAM integration into the NAS will be contingent on resilient safety systems that support reduced-crew flight operations. In this paper, we present a system that performs three functions: 1) monitors an operator’s physiological state; 2) assesses when the operator is experiencing anomalous states; and 3) mitigates risks by a combination of dynamic, context-based unilateral or collaborative dynamic function allocation of operational tasks. The monitoring process receives high data-rate sensor values from eye-tracking and electrocardiogram sensors. The assessment process takes these values and performs a classification that was developed using machine learning algorithms. The mitigation process invokes a collaboration protocol called DFACCto which, based on context, performs vehicle operations that the operator would otherwise routinely execute. This system has been demonstrated in a UAM flight simulator for an operator incapacitation scenario. The methods and initial results as well as relevant UAM and AAM scenarios will be described.

Advanced air mobility↗

NASA NTRSDate not supplied

Processing Space Fence Radar Cross-Section Data to produce size and mass estimates

With the addition of the Space Fence (SFK) radar to the Space Surveillance Network (SSN), the NASA Conjunction Assessment Risk Analysis (CARA) team now has access to radar cross-section (RCS) measurements for many Earth orbiting satellites. The CARA team has developed a process to estimate satellite sizes and masses from the SFK RCS measurement data. This study describes the processes used to filter the RCS data, defines the algorithms used to esti-mate satellite sizes and masses, and presents comparisons of estimated values against known satellite sizes and masses.

Radar Cross-Section↗

NASA NTRSDate not supplied

Processing Space Fence RCS Data for Hard-Body Radius and Mass Estimation

With the addition of the Space Fence (SFK) radar to the Space Surveillance Network (SSN), the NASA Conjunction Assessment Risk Analysis (CARA) team now has access to radar cross-section (RCS) measurements for many Earth orbiting satellites. The CARA team has developed a process to estimate satellite sizes and masses from the SFK RCS measurement data. This study describes the processes used to filter the RCS data, defines the algorithms used to estimate satellite sizes and masses, and presents comparisons of estimated values against known nanosat sizes and masses.

Luis Baars↗

NASA NTRS2022

Exploring Applications of Machine Learning for Wildfire Monitoring and Detection using Unmanned Aerial Vehicles

Wildfires are increasing in frequency and severity around the world, including the United States. The losses caused by wildfires could be mitigated if high-risk areas, hotspots, and flare-ups could be monitored continuously, such as through the use of Unmanned Aerial Vehicles (UAVs). This paper documents exploratory efforts using machine learning to determine efficient flight paths for UAVs and to detect wildfires using image classification. On path planning, three machine learning techniques—Genetic Algorithm, Simulated Annealing, and Dynamic Programming—were explored. Genetic Algorithm was found to be an effective approach for path planning for wildfire monitoring and surveillance by UAVs. For a scenario of 25 locations in a circular arrangement, the algorithm was able to return the optimal path. The accuracy and execution time was found to be sensitive to the algorithm hyperparameters selected, which was especially evident in scenarios with hundreds or thousands of locations. Simulated Annealing was also found to be an effective approach for UAV path planning, with a major benefit of avoiding getting trapped in local minima and being straightforward to implement. Like Genetic Algorithm, the performance of Simulated Annealing was also found to be sensitive to the algorithm hyperparameters selected. By comparison, Dynamic Programming guarantees optimality for any number of locations, but it was found to be less practical in terms of execution time for scenarios with more than about a couple dozen locations. On wildfire detection, image classification using deep learning with a convolutional neural network was explored. Transfer learning was found to be a useful technique to efficiently train deep learning models. Also, it was determined that GPU processing can increase training speed by an order of magnitude, which enables significantly faster development. For a validation test set of 500 images, there were only two false negatives and zero false positives. These results demonstrate that detecting wildfires in static cameras using machine learning is feasible and establish a baseline for using images captured by UAVs in flight for wildfire detection.

Wildfire management↗

NASA NTRS1988

Vulnerability-attention analysis for space-related activities

Techniques for representing and analyzing trouble spots in structures and processes are discussed. Identification of vulnerable areas usually depends more on particular and often detailed knowledge than on algorithmic or mathematical procedures. In some cases, machine inference can facilitate the identification. The analysis scheme proposed first establishes the geometry of the process, then marks areas that are conditionally vulnerable. This provides a basis for advice on the kinds of human attention or machine sensing and control that can make the risks tolerable.

Ford, Donnie↗

NASA NTRS1991

The Marshall Engineering Thermosphere model atmosphere Statistical Analysis Mode (MET-SAM)

The minimum, mean, and maximum exospheric temperature on the globe were calculated for every three hour period from 1947 through 1989 using the algorithms in the Marshall Engineering Thermosphere (MET) model and the appropriate solar activity input parameters. Cumulative percent frequency (CPF) distributions were then calculated for each of these temperatures at five levels of solar activity as defined by the 13-month smoothed values of the 10.7-cm solar radio noise flux. Next, the 50, 95, 97.7, and 100 percentile temperature values in each of these five levels of solar activity were curve fit as a function of the 13-month smoothed 10.7-cm flux. The resulting algorithms are used to compute the exospheric temperature in the MET model instead of the technique developed by Jacchia in his 1970 model. These temperatures are then used to enter tables to determine the total mass density and/or the atomic oxygen number density for application to engineering problems. Users can specify the risk level they are willing to accept in the results of analyses that require neutral atmosphere parameters inputs. The model eliminates the guess work in how to combine the solar activity input parameters to insure that the results provide answers at the proper risk levels.

Smith, Robert E.↗

NASA NTRS2019

Vortex Radiometry: Enabling Frequency Agile Communications

Exponential proliferation of UAVs and CubeSats puts NASA's high priority communication systems at risk. UAVs and CubeSats are easy to acquire, modify and build, making it extremely likely that some will be used in unauthorized ways. While these systems provide several benefits to society, their increased use also increases the probability of unintentional signal interference, and even deliberate jamming of NASA's communication systems.This paper presents an algorithm to determine 1) when an interference induced fade will occur, 2) how long the fade will persist for and 3) how intense the fade will be. The algorithm requires data collected from Vortex Radiometers (VRs), which probe the RF environment using annular antenna beam patterns. A set of numerical simulations show that a multi-beam VR system can instruct a cognitive antenna to switch between Ka- and X-Band communications, in order to avert interference from small diameter noise sources. Analysis of the simulation results indicate that practical VR systems will require several concentric annular beam patterns, in order to mitigate fades from noise sources of various sizes. The paper concludes by identifying technology challenges that need to be overcome to achieve these capabilities.

Orbital Angular Momentum↗

NASA NTRS2004

Aerocapture Guidance Performance for the Neptune Orbiter

A performance evaluation of the Hybrid Predictor corrector Aerocapture Scheme (HYPAS) guidance algorithm for aerocapture at Neptune is presented in this paper for a Mission to Neptune and the Neptune moon Triton'. This mission has several challenges not experienced in previous aerocapture guidance assessments. These challengers are a very high Neptune arrival speed, atmospheric exit into a high energy orbit about Neptune, and a very high ballistic coefficient that results in a low altitude acceleration capability when combined with the aeroshell LD. The evaluation includes a definition of the entry corridor, a comparison to the theoretical optimum performance, and guidance responses to variations in atmospheric density, aerodynamic coefficients and flight path angle for various vehicle configurations (ballistic numbers). The benefits of utilizing angle-of-attack modulation in addition to bank angle modulation to improve flight performance is also discussed. The results show that despite large sensitivities in apoapsis targeting, the algorithm performs within the allocated AV budget for the Neptune mission bank angle only modulation. The addition of angle-of-attack modulation with as little as 5 degrees of amplitude significantly improves the scatter in final orbit apoapsis. Although the angle-of-attack modulation complicates the vehicle design, the performance enhancement reduces aerocapture risk and reduces the propellant consumption needed to reach the high energy target orbit for a conventional propulsion system.

Masciarelli, James P.↗

NASA NTRS2009

Rover Slip Validation and Prediction Algorithm

A physical-based simulation has been developed for the Mars Exploration Rover (MER) mission that applies a slope-induced wheel-slippage to the rover location estimator. Using the digital elevation map from the stereo images, the computational method resolves the quasi-dynamic equations of motion that incorporate the actual wheel-terrain speed to estimate the gross velocity of the vehicle. Based on the empirical slippage measured by the Visual Odometry software of the rover, this algorithm computes two factors for the slip model by minimizing the distance of the predicted and actual vehicle location, and then uses the model to predict the next drives. This technique, which has been deployed to operate the MER rovers in the extended mission periods, can accurately predict the rover position and attitude, mitigating the risk and uncertainties in the path planning on high-slope areas.

Yen, Jeng↗