30 Aug 2022

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.

22 Nov 2021

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.

30 Sep 2021

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.

16 Sep 2021

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.

10 May 2021

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.

1 Dec 2020

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.

1 Sep 2020

PUBLICATION NOTICE: Seasonal Effects on Vehicle Mobility: High-Latitude Case Study

Abstract: Seasonality plays a key role in altering the terrain of many military operating environments. Since seasonality has such a large impact on the terrain, it needs to be properly accounted for in vehicle dynamics models. This work outlines a variety of static and dynamic seasonal terrain conditions and their impacts on vehicle mobility in an austere region of Europe. Overall the vehicles performed the best in the dry season condition. The thaw season condition had the most drastic impact on mobility with all but the heavy tracked vehicle being almost completely NOGO in the region. Overall, the heavy tracked vehicle had the best performance in all terrain conditions. These results highlight the importance of incorporating seasonal impacts on terrain into NRMM or any vehicle dynamics model. Future work will focus on collecting more data to improve the empirical relationships between vehicles and seasonal terrain conditions, thereby allowing for more accurate speed predictions.

29 Apr 2020

PUBLICATION NOTICE: Preliminary Assessment of Landform Soil Strength on Glaciated Terrain in New Hampshire

Abstract: Accurate terrain characterization is important for predicting off-road vehicle mobility. Soil strength is a significant terrain characteristic affecting vehicle mobility. Collecting soil strength measurements is laborious, making in-situ observations sparse. Research has focused on providing soil strength estimates using remote sensing techniques that can provide large spatial and temporal estimates, but the results are often inaccurate. Past attempts have quantified the soil properties of arid environments using landform assessments; yet many military operating environments occupy high latitude regions with landscapes dominated by glacial deposits. This study took preliminary strength measurements for glacial landforms deposited from the Laurentide Ice Sheet in New England. A range of common glacial landforms were sampled to assess shear strength, bearing capacity, and volumetric moisture content. Glacial outwash landforms had the highest average shear strengths, glacial deltas the lowest. There was a significant negative correlation between silt content and shear strength of the soil, a significant positive correlation between bearing capacity and clay content, and a significant negative correlation with sand content. Moisture content of soils was inversely correlated to the abundance of gravel in the deposit. This work provides initial insight to this approach on glaciated terrain, but continued sampling will provide more robust correlations.