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Tag: Trafficability
  • Analysis of Vegetation as Terrain: The “How” and “Why” of US Army Doctrine

    Abstract: There is a significant knowledge gap for Army doctrine concerning civilian research scientists. A relatively small number of soldiers make the transition from warfighter to research and development at the basic and applied levels. That number is even less when considering former warfighters that have applied Army doctrine in an operational or advanced Army schooling environment. This special report is intended to focus solely on the Army’s current capabilities and doctrinally defined processes to analyze vegetation as an essential component of the natural terrain. The objective of this report is to review current Army doctrine related to analysis of the vegetated terrain; to explore currently leveraged tactics, techniques, and procedures (TTPs); and identify valuable geospatial resources as they apply to military planning. For ease to readers unfamiliar with US Army doctrine, much of the referenced material is directly presented herein as tables and figures throughout the document and appendices (e.g., data sources, product examples, and glossary).
  • Full-Scale Trafficability Testing of Prototype Submersible Matting Systems

    Abstract: This report describes the full-scale evaluation of prototype submersible matting systems (SUBMAT) at a test site at the US Army Engineer Research and Development Center’s Vicksburg, Mississippi, site. The SUBMAT prototypes were designed to bridge the gap between high and low tide at a beach interface to enable 24-hour operation at an expeditionary watercraft landing site. This phase of the SUBMAT prototype development was intended to determine prototype system durability by applying military vehicle loads representing a combat brigade insertion across a littoral zone. The two mat systems evaluated in this study were the PYRACELL Road Building System (PRBS) and a basaltic rebar mat system. The results of the study showed that the PRBS system was able to sustain 1,000 Medium Tactical Vehicle Replacement, 350 Heavy Expanded Mobility Tactical Truck, and over 150 M1A1 main battle tank passes without significant damage. The basaltic rebar mat failed early in the test and was removed from further consideration for the SUBMAT application. Observations and lessons learned from this phase of the prototype PRBS development will be used to improve the PRBS design and modify its installation procedures for improved efficiency.
  • Incorporating Advanced Snow Microphysics and Lateral Transport into the Noah-Multiparameterization (Noah-MP) Land Surface Model

    Abstract: The dynamic state of the land surface presents challenges and opportunities for military and civil operations in extreme cold environments. In particular, the effects of snow and frozen ground on Soldier and vehicle mobility are hard to overstate. Current authoritative weather and land models are run at global scales (i.e., dx > 10 km) and are of limited use at the Soldier scale (dx < 100 m). Here, we describe several snow physics upgrades made to the Noah-Multiparameterization (Noah-MP) community land surface model (LSM). These upgrades include a blowing snow overlay to simulate the lateral redistribution of snow by the wind and the addition of new prognostic snow microstructure variables, namely grain size and bond radius. These additions represent major upgrades to the snow component of the Noah-MP LSM because they incorporate processes and methods used in more specialized snow modeling frameworks. These upgrades are demonstrated in idealized and real-world applications. The test simulations were promising and show that the newly added snow physics replicate observed behavior with reasonable accuracy. We hope these upgrades facilitate ongoing and future research on characterizing the effects of the integrated snow and soil land surface in extreme cold environments at the tactical scale.
  • Bridge Resource Inventory Database for Gap Emplacement Selection (BRIDGES)

    Abstract: Wet gap crossings are one of the most complex maneuvers undertaken by military engineers, who, along with engineer planners, require better tools to increase the capacity for efficient use of limited bridging resources across the battlespace. Planning for bridging maneuvers often involves a complicated and inefficient system of ad hoc spreadsheets combined with an overreliance on the personal experience and training of subject matter experts (SMEs). Bridge Resource Inventory Database for Gap Emplacement Selection (BRIDGES) uses interactive mapping and database technology in order to streamline the bridging planning process and provide answers to question about myriad scenarios to maximize efficiency and provide better means of data persistence across time and data sharing across operational or planning units.
  • Automated Detection of Austere Entry Landing Zones: A “GRAIL Tools” Validation Assessment

    Abstract: The Geospatial Remote Assessment for Ingress Locations (GRAIL) Tools software is a geospatial product developed to locate austere entry landing zones (LZs) for military aircraft. Using spatial datasets like land classification and slope, along with predefined LZ geometry specifications, GRAIL Tools generates binary suitability filters that distinguish between suitable and unsuitable terrain. GRAIL Tools combines input suitability filters, searches for LZs at user‐defined orientations, and plots results. To refine GRAIL Tools, we: (a) verified software output; (b) conducted validation assessments using five unpaved LZ sites; and (c) assessed input dataset resolution on outcomes using 30 and 1‐m datasets. The software was verified and validated in California and the Baltics, and all five LZs were correctly identified in either the 30 or the 1‐m data. The 30‐m data provided numerous LZs for consideration, while the 1‐m data highlighted hazardous conditions undetected in the 30‐m data. Digital elevation model grid size affected results, as 1‐m data produced overestimated slope values. Resampling the data to 5 m resulted in more realistic slopes. Results indicate GRAIL Tools is an asset the military can use to rapidly assess terrain conditions.
  • Imagery Classification for Autonomous Ground Vehicle Mobility in Cold Weather Environments

    Abstract: Autonomous ground vehicle (AGV) research for military applications is important for developing ways to remove soldiers from harm’s way. Current AGV research tends toward operations in warm climates and this leaves the vehicle at risk of failing in cold climates. To ensure AGVs can fulfill a military vehicle’s role of being able to operate on- or off-road in all conditions, consideration needs to be given to terrain of all types to inform the on-board machine learning algorithms. This research aims to correlate real-time vehicle performance data with snow and ice surfaces derived from multispectral imagery with the goal of aiding in the development of a truly all-terrain AGV. Using the image data that correlated most closely to vehicle performance the images were classified into terrain units of most interest to mobility. The best image classification results were obtained when using Short Wave InfraRed (SWIR) band values and a supervised classification scheme, resulting in over 95% accuracy.
  • Incorporating Terrain Roughness into Helicopter Landing Zone Site Selection by Using the Geomorphic Oscillation Assessment Tool (GOAT) v1.0

    ABSTRACT: The Geomorphic Oscillation Assessment Tool (GOAT) quantifies terrain roughness as a mechanism to better explain forward arming and refueling point (FARP) suitability for Army aviation. An empirically driven characteristic of FARP consideration, surface roughness is a key discriminator for site utility in complex terrain. GOAT uses a spatial sampling of high-resolution elevation and land cover data to construct data frames, which enable a relational analysis of component and aggregate site suitability. By incorporating multiple criteria from various doctrinal sources, GOAT produces a composite quality assessment of the areal options available to the aviation commander. This report documents and demonstrates version 1.0 of the GOAT algorithms developed by the U.S. Army Engineer Research and Development Center (ERDC). These details will allow users familiar with R to implement it as a stand-alone program or in R Studio.
  • An Investigation of the Feasibility of Assimilating COSMOS Soil Moisture into GeoWATCH

    Abstract: This project objective evaluated the potential of improving linked weather-and-mobility model predictions by blending soil moisture observations from a Cosmic-ray Soil Moisture Observing System (COSMOS) sensor with weather-informed predictions of soil moisture and soil strength from the Geospatial Weather-Affected Terrain Conditions and Hazards (GeoWATCH). Assimilating vehicle-borne COSMOS observations that measure local effects model predictions of soil moisture offered potential to produce more accurate soil strength and vehicle mobility forecast was the hypothesis. This project compared soil moisture observations from a COSMOS mobile sensor driven around an area near Iowa Falls, IA, with both GeoWATCH soil moisture predictions and in situ probe observations. The evaluation of the COSMOS rover data finds that the soil moisture measurements contain a low measurement bias while the GeoWATCH estimates more closely matched the in situ data. The COSMOS rover captured a larger dynamic range of soil moisture conditions as compared to GeoWATCH, capturing both very wet and very dry soil conditions, which may better flag areas of high risk for mobility considerations. Overall, more study of the COSMOS rover is needed to better understand sensor performance in a variety of soil conditions to determine the feasibility of assimilating the COSMOS rover estimates into GeoWATCH.
  • Automated Terrain Classification for Vehicle Mobility in Off-Road Conditions

    ABSTRACT:  The U.S. Army is increasingly interested in autonomous vehicle operations, including off-road autonomous ground maneuver. Unlike on-road, off-road terrain can vary drastically, especially with the effects of seasonality. As such, vehicles operating in off-road environments need to be informed about the changing terrain prior to departure or en route for successful maneuver to the mission end point. The purpose of this report is to assess machine learning algorithms used on various remotely sensed datasets to see which combinations are useful for identifying different terrain. The study collected data from several types of winter conditions by using both active and passive, satellite and vehicle-based sensor platforms and both supervised and unsupervised machine learning algorithms. To classify specific terrain types, supervised algorithms must be used in tandem with large training datasets, which are time consuming to create. However, unsupervised segmentation algorithms can be used to help label the training data. More work is required gathering training data to include a wider variety of terrain types. While classification is a good first step, more detailed information about the terrain properties will be needed for off-road autonomy.
  • Snow-Covered Obstacles’ Effect on Vehicle Mobility

    ABSTRACT:  The Mobility in Complex Environments project used unmanned aerial systems (UAS) to identify obstacles and to provide path planning in forward operational locations. The UAS were equipped with remote-sensing devices, such as photogrammetry and lidar, to identify obstacles. The path-planning algorithms incorporated the detected obstacles to then identify the fastest and safest vehicle routes. Future algorithms should incorporate vehicle characteristics as each type of vehicle will perform differently over a given obstacle, resulting in distinctive optimal paths. This study explored the effect of snow-covered obstacles on dynamic vehicle response. Vehicle tests used an instrumented HMMWV (high mobility multipurpose wheeled vehicle) driven over obstacles with and without snow cover. Tests showed a 45% reduction in normal force variation and a 43% reduction in body acceleration associated with a 14.5 cm snow cover. To predict vehicle body acceleration and normal force response, we developed two quarter-car models: rigid terrain and deformable snow terrain quarter-car models. The simple quarter models provided reasonable agreement with the vehicle test data. We also used the models to analyze the effects of vehicle parameters, such as ground pressure, to understand the effect of snow cover on vehicle response.