11 Sep 2024

Terrestrial Vision-Based Localization Using Synthetic Horizons

Abstract: Vision-based localization could improve navigation and routing solutions in GPS-denied environments. In this study, data from a Carnegie Robotics MultiSense S7 stereo camera were matched to a synthetic horizon derived from foundation sources using novel two-dimensional correlation techniques. Testing was conducted at multiple observation locations over known ground control points (GCPs) at the US Army Engineer Research and Development Center (ERDC), Geospatial Research Laboratory (GRL), Corbin Research Facility. Testing was conducted at several different observational azimuths for these locations to account for the many possible viewing angles in a scene. Multiple observational azimuths were also tested together to see how the amount of viewing angles affected results. These initial tests were conducted to help future efforts testing the S7 camera under more realistic conditions, in different environments, and while expanding the collection and processing methodologies to additional sensor systems.

9 Sep 2024

Leveraging Artificial Intelligence and Machine Learning (AI/ML) for Levee Culvert Inspections in USACE Flood Control Systems (FCS)

Abstract: Levee inspections are essential in preventing flooding within populated regions. Risk assessments of structures are performed to identify potential failure modes to maintain the safety and health of the structure. The data collection and defect coding parts of the inspection process can be labor-intensive and time-consuming. The integration of machine learning (ML) and artificial intelligence (AI) techniques may increase accuracy of assessments and reduce time and cost. To develop a foundation for a fully autonomous inspection process, this research investigates methods to gather information for levees, structures, and culverts as well as methods to identify indicators of future failures using AI and ML techniques. Robotic plat-form and instrumentation options that can be used in the data collection process are also explored, and a platform-agnostic solution is proposed.

21 Jun 2024

Multiscale Observation Product (MOP) for Temporal Flood Inundation Mapping of the 2015 Dallas Texas Flood

Abstract: This paper presents a new data fusion multiscale observation product (MOP) for flood emergencies. The MOP was created by integrating multiple sources of contributed open-source data with traditional spaceborne remote sensing imagery to provide a sequence of high spatial and temporal resolution flood inundation maps. The study focuses on the 2015 Memorial Day floods that caused up to US$61 million of damage. The Hydraulic Engineering Center River Analysis System (HEC-RAS) model was used to simulate water surfaces for the northern part of the Trinity River in Dallas, using reservoir surcharge releases and topographic data provided by the US Army Corps of Engineers. A measure of fit assessment is performed on the MOP flood maps with the HEC-RAS simulated flood inundation output to quantify spatial differences. Estimating possible flood inundation using individual datasets that vary spatially and temporally allow an understanding of how much each observational dataset contributes to the overall water estimation. Results show that water surfaces estimated by MOP are comparable with the simulated output for the duration of the flood event. Additionally, contributed data, such as Civil Air Patrol, although they may be geographically sparse, become an important data source when fused with other observation data.

20 Jun 2024

Spatial Variations in Vegetation Fires and Emissions in South and Southeast Asia during COVID-19 and Pre-pandemic

Abstract: Vegetation fires are common in South/Southeast Asian (SA/SEA) countries. However, few studies focused on vegetation fires and the changes during COVID compared to pre pandemic. This study fills an information gap and reports total fire incidences, total burnt area, type of vegetation burnt, and total particulate matter emission variations. Results from the short term 2020 COVID versus 2019 non COVID year showed a decline in fire counts varying from -2.88 to 79.43%. The exceptions in South Asia include Afghanistan and Sri Lanka, and Cambodia and Myanmar in Southeast Asia. The burnt area decline for 2020 compared to 2019 varied from -0.8% to 92% for South/Southeast Asian countries, with most burning in agricultural landscapes than forests. Several patches in S/SEA showed a decrease in fires for the 2020 pandemic year compared to long term 2012–2020 pre pandemic record, with Z scores greater or less than two denoting statistical significance. However, on a country scale, the results were not statistically significant in both S/SEA, with Z scores ranging from -0.24 to -1, although most countries experienced a decrease in fire counts. The study highlights variations in fires and emissions useful for fire management and mitigation.

3 Apr 2024

Deployable Resilient Installation Water Purification and Treatment System (DRIPS): Geoenabled Water Production and Disinfection Systems for Installations

Abstract: The Deployable Resilient Installation water Purification and treatment System (DRIPS) was delivered to aid an Organic Industrial Base in increasing their Installation Status Report–Mission Capacity (ISR-MC) score from black to green as part of a Course of Action (COA) within their Installation Energy and Water Plan (IEWP). DRIPS was also intended to help them be better prepared for the future in meeting their water and energy requirement goals for sustainment of critical missions. The IEWP ISR-MC requirements were met upon implementation of this project. Overall, the purpose of the DRIPS is to be a critical asset in disaster response and military operations, providing a reliable and effective means of producing potable water and disinfection in challenging and unpredictable environments. Its adaptability, mobility, and comprehensive water treatment capabilities make it an invaluable resource for addressing water-related emergencies and water disruptions and for sustaining critical missions. It also addresses a point of need by improving the ability to meet demands, reducing convoy requirements and the logistical footprint, facilitating the endurance of expeditionary forces, and ensuring the well-being of affected installations during times of disaster response, training operations, normal water disruptions, and emergency preparation.

21 Nov 2023

Rotorcraft Resupply Site Selection (RRSS) v1.0 and the USACE Model Interface Platform (UMIP): Documentation and User’s Guide

Abstract: This research effort aimed to create an operational prototype of the Geomorphic Oscillation Assessment Tool (GOAT) v1.0, developed by the US Army Engineer Research and Development Center, as a part of the US Army Corps of Engineers’ Model Interface Platform (UMIP). This platform is a web-based software that allows for easy and rapid construction and deployment of spatial planning and analysis capabilities. The prototype tool in UMIP represents the science embedded in GOAT while providing a user-friendly interface for interaction and spatially referenced result viewing. It also includes user access control, data storage, and integration with a long-term data management system, enabling users to access, share, and interrogate past analyses through profile management and result persistence. The prototype tool incorporates surface roughness into terrain suitability assessment tools used in the forward arming and refueling point (FARP) site-selection process.

8 Nov 2023

Leveraging MOVEit for Object Inspection in Simulation

Abstract: Herein we evaluate using a robotic arm with an attached camera to investigate objects of interest in simulation. Specifically, a Husky unmanned ground vehicle with a Panda Powertool was used in the simulation. The code enabled an operator to initiate a preconfigured set of motions when an object of interest was identified. The scan was stored in a database file that was used to generate a 3D mesh of the scanned object. The report describes both setting up the simulation and the code used to scan objects of interest.

8 Nov 2023

Increasing the Degrees of Freedom on a Robot Arm

Abstract: This report provides an implementation of the moveit-commander Python module to generate trajectories for custom six– and seven–degrees of freedom (DoF) arms. The moveit_setup_assistant package was used to modify an existing five-DoF OpenManipulator-X model to increase its range of motion. Specifically, additional joints were fabricated and mounted to the physical arm. Also, the Unified Robot Description Format files were modified to account for the additional joints. In order to optimize the solvers, many changes to the MOVEit configuration files were made. The changes documented in this report lay the groundwork for leveraging MOVEit to expand the capabilities of low-DoF arms.

7 Nov 2023

Integrating MOVEit Motion Constraints on a Novel Robotic Manipulator

Abstract: MOVEit, a widely used Robot Operating System framework, plans composite tasks, where the high-level sequence of actions is fixed and known in advance. However, these tasks need to be tailored and adapted to the environmental context. This framework uses custom trajectory planners, known as controllers, to solve goals that are fully defined within the configuration space. Libraries, such as the Open Motion Planning Library, provide a collection of motion planners that can solve task-space goals. An exact spatial and joint replication of the robotic manipulator’s mechanics, typically Universal Robot Description Format and Semantic Robot Description Format files, is required. Common arms such as the Panda-Manipulator and OpenMANIPULATOR-X provide these files in their respective public repositories, but custom arms require significant modification or even a complete rewrite of these files.

31 Oct 2023

Establishing a Series of Dust Event Case Studies for East Asia

Abstract: Dust aerosols have a wide range of effects on air quality, health, land-management decisions, aircraft operations, and sensor data interpretations. Therefore, the accurate simulation of dust plume initiation and transport is a priority for operational weather centers. Recent advancements have improved the performance of dust prediction models, but substantial capability gaps remain when forecasting the specific location and timing of individual dust events, especially extreme dust outbreaks. Operational weather forecasters and US Army Engineer Research and Development Center (ERDC) researchers established a series of reference case study events to enhance dust transport model evaluation. These reference case studies support research to improve modeled dust simulations, including efforts to increase simulation accuracy on when and where dust is lofted off the ground, dust aerosols transport, and dust-induced adverse air quality issues create hazardous conditions downstream. Here, we provide detailed assessments of four dust events for Central and East Asia. We describe the dust-event lifecycle from onset to end (or when dust transports beyond the area of interest) and the synoptic and mesoscale environ-mental conditions governing the process. Analyses of hourly reanalysis data, spaceborne lidar and aerosol optical depth retrievals, upper-air soundings, true-color satellite imagery, and dust-enhanced false-color imagery supplement the discussions.