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Category: Publications: Geospatial Research Laboratory (GRL)
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  • Unmanned Ground Vehicle (UGV) Full Coverage Planning with Negative Obstacles

    Abstract: We explored approaches that offer full coverage path planning while simultaneously avoiding negative obstacles. These approaches are specific to unmanned ground vehicles (UGVs), which need to constantly interact with a traversable ground surface. We tested multiple potential solutions in simulation, and the results are presented herein. Full coverage path planner (FCPP) approaches were evaluated based on their ability to discretize their paths, use waypoints effectively, and be easily integrated with our current robot platform. For negative obstacles, we explored approaches that will integrate with our current navigation stack. The preferred solution will allow for teleoperation, waypoint navigation, and full autonomy while avoiding positive and negative obstacles
  • Unmanned Ground Vehicle (UGV) Path Planning in 2.5D and 3D

    Abstract: Herein, we explored path planning in 2.5D and 3D for unmanned ground vehicle (UGV) applications. For real-time 2.5D navigation, we investigated generating 2.5D occupancy grids using either elevation or traversability to determine path costs. Compared to elevation, traversability, which used a layered approach generated from surface normals, was more robust for the tested environments. A layered approached was also used for 3D path planning. While it was possible to use the 3D approach in real time, the time required to generate 3D meshes meant that the only way to effectively path plan was to use a preexisting point cloud environment. As a result, we explored generating 3D meshes from a variety of sources, including handheld sensors, UGVs, UAVs, and aerial lidar.
  • Docker Containers and Images for Robot Operating System (ROS)–Based Applications

    Abstract: Docker is a tool designed to make it easier to create, deploy, and run applications by using containers. Containers allow a developer to package and ship out an application with all of the parts it needs, such as libraries and other dependencies. Herein, we investigate using a Docker image to deploy and run our Robot Operating System (ROS)–based payload on a robot platform. Ultimately, this would allow us to quickly and efficiently deploy our payload on multiple platforms.
  • 3D Mapping and Navigation Using MOVEit

    Abstract: Until recently, our focus has been primarily on the development of a low SWAP-C payload for deployment on a UGV that leverages 2D mapping and navigation. Due to these efforts, we are able to autonomously map and navigate very well within flat indoor environments. This report will explore the implementation of 3D mapping and navigation to allow unmanned vehicles to operate on a variety of terrains, both indoor and outdoor. The method we followed uses MOVEit, a motion planning framework. The MOVEit application is typically used in the control of robotic arms or manipulators, but its handling of 3D perception using OctoMaps makes it a promising software for robots in general. The challenges of using MOVEit outside of its intended use case of manipulators are discussed in this report.
  • Application of a Satellite-Retrieved Sheltering Parameterization (v1.0) for Dust Event Simulation with WRF-Chem v4.1

    Abstract: Roughness features (e.g., rocks, vegetation, furrows) that attenuate wind flow over the soil surface can affect the magnitude and distribution of sediment transport in aeolian environments. Existing transport models often rely on vegetation attributes derived from static land use datasets or remotely sensed greenness indicators to incorporate sheltering effects on simulated particle mobilization. These approaches do not represent the 3D nature or spatiotemporal changes of roughness element sheltering and ignore the sheltering contribution of nonvegetation roughness features and brown vegetation common to dryland environments. We used an albedo-based sheltering parameterization in a dust transport modeling application of the Weather Research and Forecasting model with Chemistry (WRF-Chem). This method estimates sheltering effects on surface wind friction speeds and dust entrainment from the shadows cast by subgrid-scale roughness elements. We applied the albedo-derived drag partition to the Air Force Weather Agency (AFWA) dust emission module and studied simulated PM10 concentrations using the Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model as implemented in WRF-Chem v4.1. Our results demonstrate how dust transport simulation and forecasting with the AFWA dust module can be improved in vegetated drylands by calculating dust emission flux with surface wind friction speed from a drag partition treatment.
  • Three-Dimensional Geospatial Product Generation from Tactical Sources, Co-Registration Assessment, and Considerations

    Abstract: According to Army Multi-Domain Operations (MDO) doctrine, generating timely, accurate, and exploitable geospatial products from tactical platforms is a critical capability to meet threats. The US Army Corps of Engineers, Engineer Research and Development Center, Geospatial Research Laboratory (ERDC-GRL) is carrying out 6.2 research to facilitate the creation of three-dimensional (3D) products from tactical sensors to include full-motion video, framing cameras, and sensors integrated on small Unmanned Aerial Systems (sUAS). This report describes an ERDC-GRL processing pipeline comprising custom code, open-source software, and commercial off-the-shelf (COTS) tools to geospatially rectify tactical imagery to authoritative foundation sources. Four datasets from different sensors and locations were processed against National Geospatial-Intelligence Agency–supplied foundation data. Results showed that the co-registration of tactical drone data to reference foundation varied from 0.34 m to 0.75 m, exceeding the accuracy objective of 1 m described in briefings presented to Army Futures Command (AFC) and the Assistant Security of the Army for Acquisition, Logistics and Technology (ASA(ALT)). A discussion summarizes the results, describes steps to address processing gaps, and considers future efforts to optimize the pipeline for generation of geospatial data for specific end-user devices and tactical applications.
  • Establishing a Series of Dust Event Case Studies for North Africa

    Abstract: Dust aerosols often create hazardous air quality conditions that affect human health, visibility, agriculture, and communication in various parts of the world. While substantial progress has been made in dust-event simulation and hazard mitigation over the last several decades, accurately forecasting the spatial and temporal variability of dust emissions continues to be a challenge. This report documents an analysis of atmospheric conditions for a series of dust events in North Africa. The researchers highlight four analyzed events that occurred between January 2016 to present in the following locations: (1) the western Sahara Desert; (2) East Algeria and the Iberian Peninsula; (3) Chad-Bodélé Depression; (4) Algeria and Morocco. For each event, the researchers developed an overview of the general synoptic, mesoscale, and local environmental forcing conditions that controlled the event evolution and used a combination of available lidar data, surface weather observations, upper-air soundings, aerosol optical depth, and satellite imagery to characterize the dust conditions. These assessments will support downstream forecast model evaluation and sensitivity testing; however, the researchers also encourage broader use of these assessments as reference case studies for dust transport, air quality modeling, remote sensing, soil erosion, and land management research applications.
  • The DEM Breakline and Differencing Analysis Tool—Step-by-Step Workflows and Procedures for Effective Gridded DEM Analysis

    Abstract: The DEM Breakline and Differencing Analysis Tool is the result of a multi-year research effort in the analysis of digital elevation models (DEMs) and the extraction of features associated with breaklines identified on the DEM by numerical analysis. Developed in the ENVI/IDL image processing application, the tool is designed to serve as an aid to research in the investigation of DEMs by taking advantage of local variation in the height. A set of specific workflow exercises is described as applied to a diverse set of four sample DEMs. These workflows instruct the user in applying the tool to extract and analyze features associated with terrain, vegetative canopy, and built structures. Optimal processing parameter choices, subject to user modification, are provided along with sufficient explanation to train the user in elevation model analysis through the creation of customized output overlays.
  • Cross Country Mobility (CCM) Modeling Using Triangulated Irregular Networks (TIN)

    Abstract: Cross country mobility (CCM) models terrain that has insufficient or unavailable infrastructure for crossing. This historically has been done with either hand-drawn and estimated maps or with raster-based terrain analysis, both of which have their own strengths and weaknesses. In this report the authors explore the possibility of using triangulated irregular networks (TINs) as a means of representing terrain characteristics used in CCM and discuss the possibilities of using such networks for routing capabilities in lieu of a traditional road-based network. The factors used to calculate CCM are modified from previous methods to capture a more accurate measurement of terrain characteristics. Using a TIN to store and represent CCM information achieves comparable results to raster cost analysis with the additional benefits of an integrated network useful for visualization and routing and a reduction in the number of related files. Additionally, TINs can in some cases more accurately show the contours of the landscape and reveal feature details or impediments that may be lost within a raster, thus improving the quality of CCM overlays.
  • Snow-Covered Region Improvements to a Support Vector Machine-Based Semi-Automated Land Cover Mapping Decision Support Tool

    Abstract: This work builds on the original semi-automated land cover mapping algorithm and quantifies improvements to class accuracy, analyzes the results, and conducts a more in-depth accuracy assessment in conjunction with test sites and the National Land Cover Database (NLCD). This algorithm uses support vector machines trained on data collected across the continental United States to generate a pre-trained model for inclusion into a decision support tool within ArcGIS Pro. Version 2 includes an additional snow cover class and accounts for snow cover effects within the other land cover classes. Overall accuracy across the continental United States for Version 2 is 75% on snow-covered pixels and 69% on snow-free pixels, versus 16% and 66% for Version 1. However, combining the “crop” and “low vegetation” classes improves these values to 86% for snow and 83% for snow-free, compared to 19% and 83% for Version 1. This merging is justified by their spectral similarity, the difference between crop and low vegetation falling closer to land use than land cover. The Version 2 tool is built into a Python-based ArcGIS toolbox, allowing users to leverage the pre-trained model—along with image splitting and parallel processing techniques—for their land cover type map generation needs.