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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Mapping Impervious Surfaces Globally at 30m Resolution Using Global Land Survey Data

Impervious surfaces, mainly artificial structures and roads, cover less than 1% of the world's land surface (1.3% over USA). Regardless of the relatively small coverage, impervious surfaces have a significant impact on the environment. They are the main source of the urban heat island effect, and affect not only the energy balance, but also hydrology and carbon cycling, and both land and aquatic ecosystem services. In the last several decades, the pace of converting natural land surface to impervious surfaces has increased. Quantitatively monitoring the growth of impervious surface expansion and associated urbanization has become a priority topic across both the physical and social sciences. The recent availability of consistent, global scale data sets at 30m resolution such as the Global Land Survey from the Landsat satellites provides an unprecedented opportunity to map global impervious cover and urbanization at this resolution for the first time, with unprecedented detail and accuracy. Moreover, the spatial resolution of Landsat is absolutely essential to accurately resolve urban targets such a buildings, roads and parking lots. With long term GLS data now available for the 1975, 1990, 2000, 2005 and 2010 time periods, the land cover/use changes due to urbanization can now be quantified at this spatial scale as well. In the Global Land Survey - Imperviousness Mapping Project (GLS-IMP), we are producing the first global 30 m spatial resolution impervious cover data set. We have processed the GLS 2010 data set to surface reflectance (8500+ TM and ETM+ scenes) and are using a supervised classification method using a regression tree to produce continental scale impervious cover data sets. A very large set of accurate training samples is the key to the supervised classifications and is being derived through the interpretation of high spatial resolution (approx. 2 m or less) commercial satellite data (Quickbird and Worldview2) available to us through the unclassified archive of the National Geospatial Intelligence Agency (NGA). For each continental area several million training pixels are derived by analysts using image segmentation algorithms and tools and then aggregated to the 30m resolution of Landsat. Here we will discuss the production/testing of this massive data set for Europe, North and South America and Africa, including assessments of the 2010 surface reflectance data. This type of analysis is only possible because of the availability of long term 30m data sets from GLS and shows much promise for integration of Landsat 8 data in the future.

global land survey↗
NASA NTRS2015

Langley Research Center Utility Risk from Future Climate Change

The successful operation of NASA Langley Research Center (LaRC) depends on services provided by several public utility companies. These include Newport News Waterworks, Dominion Virginia Power, Virginia Natural Gas and Hampton Roads Sanitation District. LaRC's plan to respond to future climate change should take into account how these companies plan to avoid interruption of services while minimizing cost to the customers. This report summarizes our findings from publicly available documents on how each company plans to respond. This will form the basis for future planning for the Center. Our preliminary findings show that flooding and severe storms could interrupt service from the Waterworks and Sanitation District but the potential is low due to plans in place to address climate change on their system. Virginia Natural Gas supplies energy to produce steam but most current steam comes from the Hampton trash burning plant, thus interruption risk is low. Dominion Virginia Power does not address climate change impacts on their system in their public reports. The potential interruption risk is considered to be medium. The Hampton Roads Sanitation District is projecting a major upgrade of their system to mitigate clean water inflow and infiltration. This will reduce infiltration and avoid overloading the pump stations and treatment plants.

De Young, Russell J.↗
NASA NTRS2011

Toward a Communications Satellite Network for Humanitarian Relief

Since the introduction in 2008 of the "Ring Road" concept, proposing a communications satellite network designed to support disadvantaged populations, there have been a number of advances in the underlying technologies, CubeSat picosatellites and Delay-Tolerant Networking. We review the original Ring Road proposal, discuss relevant recent technological progress, and offer some tentative notes on projected cost and performance.

CubeSat↗
NASA NTRSDate not supplied

Global Landslide Hazard Assessment for Situational Awareness (LHASA) Version 2: New Activities and Future Plans

A remote sensing-based system has been developed to characterize the potential for rainfall-triggered landslides across the globe in near real-time. The Landslide Hazard Assessment for Situational Awareness (LHASA) model uses a decision tree framework to combine a static susceptibility map derived from information on slope, rock characteristics, forest loss, distance to fault zones and distance to road networks with satellite precipitation estimates from the Global Precipitation Measurement (GPM) mission. Since 2016, the LHASA model has been providing near real-time and retrospective estimates of potential landslide activity. Results of this work are available at https://landslides.nasa.gov. In order to advance LHASA’s capabilities to characterize landslide hazards and impacts dynamically, we have implemented a new approach that leverages machine learning, new parameters, and new inventories. LHASA 2.0 uses the XGBoost machine learning model to bring in dynamic variables as well as additional static variables to better represent landslide hazard globally. Global rainfall forecasts are also being evaluated to provide a 1-3 day forecast of potential landslide activity. Additional factors such as recent seismicity and burned areas are also being considered to represent the preconditioning or changing interactions with subsequent rainfall over affected areas. A series of parameters are being tested within this structure using NASA’s Global Landslide Catalog as well as many other event-based and multi-temporal inventories mapped by the project team or provided by project partners. In addition to estimates of landslide hazard, LHASA Version 2 will incorporate dynamic estimates of exposure including population, roads and infrastructure to highlight the potential impacts that rainfall-triggered landslides. The ultimate goal of LHASA Version 2.0 is to approximate the relative probabilities of landslide hazard and exposure across different space and time scales to inform hazard assessment retrospectively over the past 20 years, in near real-time, and in the future. In addition to the hazard. This presentation will outline the new activities for LHASA Version 2.0 and present some next steps for this system.

Dalia Kirschbaum↗
NASA NTRS2020

Lessons Learned: Using UTM Paradigm for Urban Air Mobility

Urban Air Mobility (UAM) aims to reduce congestion on the roads and highways by offering air taxi as an alternative to driving on surface roads. Integration of UAM operations in the National Airspace System (NAS) has been the focus of the research conducted at NASA Ames Research Center. A simulation was performed in collaboration with Uber Technologies Inc to investigate if NASA’s UTM architecture and its implementation as demonstrated in the 2019 UTM field tests were extensible for UAM operations, and if the data exchange between multiple operators as planned under UTM were adequate for UAM operations in the shared airspace. In order to explore these research questions, three Use Cases were defined to investigate different airspace management challenges. This paper will describe the lessons learned from exercising the uses cases and the airspace management services including scheduling and separation developed to facilitate initial UAM operations.

Urban Air Mobility↗
NASA NTRSDate not supplied

Lessons Learned: Using UTM paradigm for Urban Air Mobility Operations

Urban Air Mobility (UAM) aims to reduce congestion on the roads and highways by offering air taxi as an alternative to driving on surface roads. Integration of UAM operations in the National Airspace System (NAS) has been the focus of the research conducted at NASA Ames Research Center. A simulation was performed in collaboration with Uber Technologies Inc to investigate if NASA’s UTM architecture and its implementation as demonstrated in the 2019 UTM field tests were extensible for UAM operations, and if the data exchange between multiple operators as planned under UTM were adequate for UAM operations in the shared airspace. In order to explore these research questions, three Use Cases were defined to investigate different airspace management challenges. This paper will describe the lessons learned from exercising the uses cases and the airspace management services including scheduling and separation developed to facilitate initial UAM operations.

Urban Air Mobility↗
NASA NTRSDate not supplied

Upper E Traffic Management

This is a slide set as part of a meeting series with members of a working group aimed at the development of a concept that addresses needs and gaps in the management of high altitude airspace operations. This concept leverages elements developed through the UAS Traffic Management project with respect to a cooperative, service-based approach that provides services and capabilities in areas (e.g., Upper E airspace) that currently receive no or limited service from Air Traffic Control. This concept is meant to provide a safe, fair, and scalable approach to management of Upper E operations that reduces the burden on ATC while providing the flexibility and access desired by current and new users of the airspace. This set of slides includes an overview of discussions and industry news covering the time since the previous group meeting, a recap of the group's history and road ahead, a discussion of industry's initial position on, 'Industry principles for ETM rules of the road: Conflict identification and resolution,' and a simulation roadmap for planned work ahead.

Upper E↗
NASA NTRSDate not supplied

Using Satellite Soil Moisture and Rainfall in the Landslide Hazard Assessment for Situational Awareness System

The Landslide Hazard Assessment for Situational Awareness system(LHASA)gives a global view of landslide hazard in nearly real time. Currently, it is being upgraded from version 1 to version 2, which entails improvements along several dimensions. These include the incorporation of new predictors, machine learning, and new event-based landslide inventories. As a result, LHASA version 2 substantially improves on the prior performanceand introduces a probabilistic element to the global landslide nowcast. Data from the soil moisture active-passive (SMAP) satellite has been assimilated into a globally consistent data product with a latency less than 3 days, known as SMAP Level 4. In LHASA, thesedata representthe antecedent conditions prior to landslide-triggering rainfall. In some cases, soil moisture may have accumulated over aperiod of many months. The model behind SMAP Level 4 also estimates the amount of snow on the ground, which is an important factor in some landslide events. LHASA also incorporates this information as an antecedent condition that modulates the response torainfall. Slope, lithology, and active faults were also used as predictor variables. These factors can have a strong influence on where landslides initiate.LHASA relies on precipitation estimates from the Global Precipitation Measurement mission to identify the locations where landslides are most probable. The low latency and consistent global coverage of these data make them ideal for real-time applications at continental to global scales. LHASA relies primarily on rainfall from the last 24 hours to spothazardous sites, which is rescaled by the local 99thpercentile rainfall.However, the multi-day latency of SMAP requires the use of a 2-day antecedent rainfall variable to represent the accumulation of rain between the antecedent soil moisture and current rainfall. LHASA merges these predictors with XGBoost, a commonly used machine-learning tool, relying on historical landslide inventories to develop the relationship between landslide occurrence and various risk factors. The resulting model relies heavily on current daily rainfall, but other factors also play an important role. LHASA outputsthe probability oflandslide occurrence ona grid of roughly one kilometer over all continents from 60 North to 60 South latitude. Evaluation over the period 2019-2020 showsthat LHASA version 2 doubles the accuracy of the global landslide nowcast without increasing the global false alarm rate. LHASA also identifies the areas where the human exposure to landslide hazard is most intense. Landslide hazard is divided into 4 levels: minimal, low, moderate, and high. Next, the number of persons and the length of major roads (primary and secondary roads)within each of these areas is calculated for every second-level administrative district (county). These results can be viewedthrough a web portal hosted at the Goddard Space Flight Center. In addition, users can download daily hazard and exposure data.LHASAversion 2uses machine learning and satellite data to identify areas of probable landslide hazard within hours of heavy rainfall. Itsglobal maps are significantly more accurate, and it now includes rapid estimates of exposed populations and infrastructure. In addition, a forecast mode will be implemented soon.

Thomas Stanley↗
NASA NTRS2021

Peru Health & Air Quality: Land Use Change in the Rapidly Developing Peruvian Amazon and Implications on Zoonotic Disease Incidence

In the Madre de Dios region of the Peruvian Amazon, forests are being cleared for mining, timber harvesting, road construction, and hydroelectric dam development. These rapid land use changes are increasing human presence in previously sparsely populated areas, disrupting ecosystems and increasing the proximity of human settlement to zoonotic disease vectors. Dengue fever and leishmaniasis are two neglected tropical diseases which are prevalent in Madre de Dios and have been associated with urbanization and road construction. In partnership with the Peruvian Ministries of Health (MINSA) and the Environment (MINAM) and other in-country collaborators, our team examined Land Use Land Cover (LULC) correlations with reported dengue and leishmaniasis incidence in the Madre de Dios region to help partners understand the spatial relationship between land use change and zoonotic disease incidence. We created a LULC classification script using Google Earth Engine with Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager imagery to classify land cover in 2010, 2015, and 2020 and evaluate changes over this time period. We then used the quantified results of the LULC assessment in conjunction with reported disease cases to evaluate correlations between disease incidence and key land cover changes across Madre de Dios’s 11 districts. In the second term, the team will use these products to develop more detailed disease incidence risk maps and models. High risk areas will then be classified, using PeruSat-1 that allow for even higher resolution mapping at less than 3 meters. These products will allow the partners to understand hotspots of land cover change in Peru and the relationship with outbreaks to inform public health decision making and environmental policy.

Elizabeth Stapleton↗
NASA NTRS2022

Precious Cargo: Transporting Contamination-Sensitive Instruments & Optics

The James Webb Space Telescope (JWST) is a multi-national program with instruments and hardware supplied by companies all over the world and numerous states in the United States. In order to transport larger assemblies, like the Optical Telescope Element / Integrated Science Instrument Module (OTIS), and ultimately JWST, the Space Telescope Transporter for Air, Road and Sea (STTARS) was designed and constructed. STTARS is a massive mobile cleanroom (longer than 2 semi-trailers) that provides an ISO class 7 payload environment while being transported by road, airborne and marine vehicles. Temperature, humidity, and particle counts are controlled and continuously tracked, with fallout and NVR witness samples placed in strategic locations for confirmation. Instruments or sensitive hardware were purged continuously during transport. STTARS has 5 main components: the upper tent frame, lower tent frame, pallet, strong back and lid. After transporting OTIS to Northrup Grumman (NG), STTARS was modified to increase its height to house the JWST Observatory on its voyage to French Guiana. This new configuration was designated Observatory STTARS (OSTTARS). OSTTARS was too tall to travel by C5 aircraft, so the trip to the launch site was made by ship. Through JWST’s land, air, and sea transports, STTARS and OSTTARS kept JWST hardware exceptionally clean and safe.

James Webb Space Telescope↗
NASA NTRSDate not supplied

Precious Cargo: Transporting contamination-sensitive instruments & optics

The James Webb Space Telescope (JWST) is a multi-national program with instruments and hardware supplied by companies all over the world and numerous states in the United States. In order to transport larger assemblies, like the Optical Telescope Element / Integrated Science Instrument Module (OTIS), and ultimately JWST, the Space Telescope Transporter for Air, Road and Sea (STTARS) was designed and constructed. STTARS is a massive mobile cleanroom (longer than 2 semi-trailers) that provides an ISO class 7 payload environment while being transported by road, airborne and marine vehicles. Temperature, humidity, and particle counts are controlled and continuously tracked, with fallout and NVR witness samples placed in strategic locations for confirmation. Instruments or sensitive hardware were purged continuously during transport. STTARS has 5 main components: the upper tent frame, lower tent frame, pallet, strong back and lid. After transporting OTIS to Northrup Grumman (NG), STTARS was modified to increase its height to house the JWST Observatory on its voyage to French Guiana. This new configuration was designated Observatory STTARS (OSTTARS). OSTTARS was too tall to travel by C5 aircraft, so the trip to the launch site was made by ship. Through JWST’s land, air, and sea transports, STTARS and OSTTARS kept JWST hardware exceptionally clean and safe.

James Webb Space Telescope↗
NASA NTRS2022

The Influence of Land Use and Land Cover Change on Landslide Susceptibility in the Lower Mekong River Basin

The Lower Mekong River Basin in Southeast Asia experiences frequent rainfall-triggered landslides especially during the monsoon season. In this study, the influence of land use and land cover change and other causative factors on landslide susceptibility is evaluated in the Lower Mekong Basin. Frequency ratio analysis is performed to quantify the relationship between LULC change and susceptibility. Detailed landslide inventory maps are used for analysis with yearly LULC maps. The LULC change is used as a contributing variable in a logistic regression-based susceptibility model with other variables including distance to roads, slope, aspect, forest loss, and soil properties. The Receiver Operating Characteristic (ROC) curve and Area Under the Curve are estimated for the model trained by each landslide inventory. The models show good performance, with AUC values ranging from 0.697 to 0.958 and an average AUC equal to 0.820. Both the Frequency Ratio analysis and the Logistic Regression models indicate LULC change from agricultural land to forest has a positive correlation with landslide occurrence. The most significant factors in the models are found to be distance to roads, slope, and aspect. A better understanding of the effects of LULC on landslide susceptibility can be useful for local land and disaster management and for the implementation of LULC as a factor in future susceptibility models. Using datasets that are unique to the Lower Mekong region, this study provides additional insights into the relationship between causative factors and landslide activity to better inform regional and global landslide susceptibility modeling.

River Basin↗
NASA NTRS2023

Fire Station #2, Former Sewage Treatment Plant #17, and Towway Area-SWMU 114 PFAS Site Assessment Progress Report Kennedy Space Center, Florida

This PFAS Site Assessment Progress Report (SAPR) presents the findings of the 2022 PFAS investigation conducted from November 2021 through August 2022 at Solid Waste Management Unit (SWMU) 114 located within Kennedy Space Center (KSC), Florida. SWMU 114 includes area around Fire Station #2, Former Sewage Treatment Plant #17, the southern portion of the Shuttle Landing Facility Runway, Remote Launch Vehicle Hangar, and the Towway area. Fire Station #2 was constructed in 2008 and is currently active, housing fire station personnel and equipment, including aqueous film forming foam (AFFF). Releases of AFFF has occurred at SWMU 114. Previous environmental assessments have been performed at SWMU 114, including soil, groundwater, and surface water sampling for volatile organic compounds, polycyclic aromatic hydrocarbons, total petroleum hydrocarbons, and metals. No active remediation has been performed at the SWMU 114. PFAS site assessment field activities were conducted at SWMU 114 from November 2021 through August 2022. During the 2021-2022 Site Assessment, 124 direct-push samples were collected from 27 locations, 17 surface water samples were collected from 15 locations, 41 groundwater samples were collected from 37 newly installed monitoring wells, five concrete samples were collected from four locations, and one asphalt sample was collected. Additionally, two soil borings were advanced to 60 feet for lithologic descriptions, ten staff gauges were installed, and one round of water level measurements were collected from monitoring wells and staff gauges for groundwater flow determination. All groundwater and surface water samples were analyzed for 25 PFAS analytes by USEPA Method 537M. Concrete, and asphalt samples were analyzed by synthetic precipitation leaching procedure (SPLP) PFAS analysis by USEPA Modified Method 537M. Groundwater results were compared to the most recent Regional Screening Levels (RSLs) published by USEPA for residential tap water (USEPA, 2022a) to determine the extent of PFAS contamination at SWMU 114 for six PFAS analytes (PFOA, PFNA, PFBS, PFHxS, PFOS, and GenX). Surface water results were compared to the FDEP surface water screening levels (SW SLs) (FDEP, 2020) for PFOA and PFOS. The PFAS investigation concluded that groundwater exceeding RSLs extends east and south to Banana Creek and west to approximately the center of the SLF runway. The extent of PFAS is approximated to the north, to an area between Sharkey Road and Astronaut Road, where samples below the RSLs do not fully bound SWMU 114. At least two distinct PFAS source areas are located near Fire Station #2 and along Towway, at S114-MW0007S, within SWMU 114. Groundwater head measurements indicate that flow at SWMU 114 is generally similar across the shallow and intermediate water tables. A northeast to southwest trending groundwater divide is located near the middle of Towway where groundwater southeast of the divide flows to the south and groundwater northwest of the divide flows to the west. The groundwater divide generally separates the two areas of higher PFAS concentrations. Surface water samples indicated concentrations of PFOS above the SW SLs in surface water bodies across the site. Further assessment and sampling are required to better understand the interaction between groundwater and surface water at SWMU 114. Concrete and asphalt samples collected confirm that a significant release of AFFF occurred near the stormwater pond northwest of Fire Station #2. Additional concrete samples surrounding Fire Station #2 and at the northwest and southeast edges of the SLF tarmac indicate elevated PFOS concentrations. These results indicate that discharges of AFFF in these locations have infiltrated into asphalt and concrete and may act as a continuing source of PFAS to groundwater and surface water after rain events. Additional direct-push samples are required to delineate PFAS at SWMU 114. Samples may need to extend beyond the Former SLF Rescue Building and Morpheous Test Site to delineate PFAS in groundwater. Monitoring wells should be sampled and gauged quarterly to determine if seasonal impacts are observable, especially in shallow wells near surface water features. Furthermore, surface water samples should be collected from additional ditches to further define the extent of surface water impacts at SWMU 114 and extending along the SLF runway. Staff gauges should be gauged quarterly with groundwater gauging to determine surface water flow and interaction with groundwater. The PFAS sampling results and path forward for SWMU 114 were presented to the KSC Remediation Team in October 2022. Once the PFAS SAPR is approved, it will be submitted to the Florida Department of Environmental Protection.

Howard Franklin Fowler↗
NASA NTRS2023

Mobile Observations of Ozone and Aerosols in Alabama: Southeastern US Summer Pollution and Coastal Variability

We present a detailed analysis of measurements of ozone (O 3 ) and aerosols collected using multiple platforms and instruments during a 5-day mini-campaign in Alabama, for improving our understanding of the chemistry and air quality in the Southeastern United States. Our study features the first on-road O 3 lidar profiling on highways and the coordinated airborne observations that captured O 3 and aerosol structures associated with synoptic weather conditions, stratospheric intrusions, wildfire smoke transport, and enhancements in urban areas. The Doppler wind lidar data suggest that the vertical structures and variability of O 3 and aerosols at Dauphin Island, a coastal site in the Gulf of Mexico, were related to both the synoptic flow and local-scale land-sea breeze circulation. The onshore gulf breeze, which typically started in the afternoon, helped to quickly dilute air pollutants near the surface, resulting in large gradients toward inland areas. In contrast, gradients were much smaller on days without a gulf breeze. O 3 and humidity were highly anti-correlated in the lower free troposphere, but weakly anti-correlated in the planetary boundary layer (PBL) suggesting more complicated emissions and sink of O 3 in the PBL. Our on-road and airborne measurement strategies to quantify the vertical and horizontal chemical gradients will be useful for future geostationary satellite validation.

Shi Kuang↗
NASA NTRS2024

Presound: UAV Diagnostic System Enabled by Vibration-Based Machine Learning

A low-weight, inexpensive small unmanned aerial system (sUAS) that takes off, performs a mission, lands, and safely stows and recharges itself has myriad future applications ranging from agricultural imaging to last-mile package delivery. Likewise, Urban Air Mobility (UAM) systems will enable people to take air taxis from point to point in cities, rapidly moving commuters long distances without concern for road traffic and congestion. Fully electric aviation systems will be cleaner and quieter than ground transport. Cities could eliminate cars and buses, and convert roads to higher capacity bike and pedestrian throughways. Yet, for sUAS as well as UAM, system reliability and assurance is a limiting factor to deploying affordable autonomous flight systems. For this bright future of aviation to be realized, aircraft must be able to autonomously and accurately self-diagnose health issues both before takeoff and during flight. The GreenSight PreSound system is designed to identify defects on aircraft through intelligent analysis of vibration. It accomplishes this by measuring structural vibrations induced by the vehicle’s own propellers, and analyzing that data using a machine learning model that determines whether a defect is present. The PreSound system is designed to require no human oversight, and to operate across a wide array of vehicles through re-training of the model for each target aircraft. PreSound has been developed and seen limited early success using data collected from the GreenSight Dreamer sUAS, a 5lb quadrotor vehicle designed for aerial imaging applications. The final detection model, trained on data with props spinning at 50% throttle, achieves excellent performance with over 99% average accuracy in detecting blade damage using a single FFT vector input. It demonstrates the ability to generalize to new types of blade damage, correctly classifying a different type of blade damage with 98% accuracy. Full test pulses were classified with 100% accuracy, and in live testing, all sets of data during blade movement were classified accurately with over 95% confidence. When trained on in-flight data, the same model achieves an average accuracy of 85% in distinguishing between undamaged and blade-damaged states in flight. The authors believe that these accuracies show significant potential of this approach to expand unmanned flight safety, with significant potential benefits in accelerating Advanced Aerial Mobility (AAM) and UAM aviation applications.

UAS↗
NASA NTRS2003

Negative obstacle detection by thermal signature

Detecting negative obstacles (ditches, potholes, and other depressions) is one of the most difficult problems in perception for autonomous, off-road navigation. Past work has largely relied on range imagery, because that is based on the geometry of the obstacle, is largely insensitive to illumination variables, and because there have not been other reliable alternatives. However, the visible aspect of negative obstacles shrinks rapidly with range, making them impossible to detect in time to avoid them at high speed. To relive this problem, we show that the interiors of negative obstacles generally remain warmer than the surrounding terrain throughout the night, making thermal signature a stable property for night-time negative obstacle detection. Experimental results to date have achieved detection distances 45% greater by using thermal signature than by using range data alone. Thermal signature is the first known observable with potential to reveal a deep negative obstacle without actually seeing far into it. Modeling solar illumination has potential to extend the usefulness of thermal signature through daylight hours.

unmanned ground vehicles (UGV's)↗
NASA NTRS2013

Real-Time and High-Fidelity Simulation Environment for Autonomous Ground Vehicle Dynamics

This paper reports on a collaborative project between U.S. Army TARDEC and Jet Propulsion Laboratory (JPL) to develop a unmanned ground vehicle (UGV) simulation model using the ROAMS vehicle modeling framework. Besides modeling the physical suspension of the vehicle, the sensing and navigation of the HMMWV vehicle are simulated. Using models of urban and off-road environments, the HMMWV simulation was tested in several ways, including navigation in an urban environment with obstacle avoidance and the performance of a lane change maneuver.

unmanned ground vehicle (UGV)↗