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Presentation outlining the the intention and procedures of a mobile radiation detection system for emergency response and monitoring
Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important role in agricultural management. Traditional orthophoto generation strategies suffer from several artifacts (e.g., double mapping, excessive pixilation, and seamline distortions). The above problems are more pronounced when dealing with mid- to late-season imagery, which is often used for establishing flowering date (e.g., tassel and panicle detection for maize and sorghum crops, respectively). In response to these challenges, this paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles. The orthophoto generation strategies are valid for both frame and push-broom imaging systems. The target function of these strategies is striking a balance between the improved visual appearance of tassels/panicles and their geolocation accuracy. The new strategies are based on generating a smooth digital surface model (DSM) that maintains the geolocation quality along the plant rows while reducing double mapping and pixilation artifacts. Moreover, seamline control strategies are applied to avoid having seamline distortions at locations where the tassels and panicles are expected. The quality of generated orthophotos is evaluated through visual inspection as well as quantitative assessment of the degree of similarity between the generated orthophotos and original images. Several experimental results from both UAV and ground platforms show that the proposed strategies do improve the visual quality of derived orthophotos while maintaining the geolocation accuracy at tassel/panicle locations.
Oyster reefs around the world are declining rapidly, and although they haven t received as much attention as coral reefs, they are just as important to their local ecosystems and economies. Oyster reefs provide habitats for many species of fish, invertebrates, and crustaceans, as well as the next generations of oysters. Oysters are also harvested from many of these reefs and are an important segment of many local economies, including that of Mobile Bay, where oysters rank in the top five commercial marine species both by landed weight and by dollar value. Although the remaining Mobile Bay oyster reefs are some of the least degraded in the world, projected climate change could have dramatic effects on the health of these important ecosystems. The viability of oyster reefs depends on water depth and temperature, appropriate pH and salinity levels, and the amount of dissolved oxygen in the water. Projected increases in sea level, changes in precipitation and runoff patterns, and changes in pH resulting from increases in the amount of carbon dioxide dissolved in the oceans could all affect the viability of oyster reefs in the future. Human activities such as dredging and unsustainable harvesting practices are also adversely impacting the oyster reefs. Fortunately, several projects are already under way to help rebuild or support existing or previously existing oyster reefs. The success of these projects will depend on the local effects of climate change on the current and potential habitats and man s ability to recognize and halt unsustainable harvesting practices. As the extent and health of the reefs changes, it will have impacts on the Mobile Bay ecosystem and economy, changing the resources available to the people who live there and to the rest of the country, since Mobile Bay is an important national source of seafood. This project identified potential climate change impacts on the oyster reefs of Mobile Bay, including the possible addition of newly viable habitats in the southeastern regions of the Bay.
No abstract available
The Kinematic Navigation and Cartography Knapsack (KNaCK) team at NASA’s Marshall Space Flight Center in Huntsville, Alabama is developing tools that enable ultra-high resolution terrain mapping and navigation using mobile LiDAR (Light Detection and Ranging) and SLAM (Simultaneous Localization and Mapping) algorithms in fully GPS-denied and unilluminated environments. The backpack mounted LiDAR instrument under development by our team demonstrates the potential of mobile SLAM LiDAR for use in challenging lunar and planetary surface environments as well as for lunar, planetary, and terrestrial cave exploration, study, and utilization.
This paper examines techniques for a mobile robot to perform self-localization in natural terrain by comparing a dense range map computed from stereo imagery to a range map in a known frame of reference.
An algorithm for the optical computation of potential field maps suitable for mobile robot navigation is described and experimentally produced maps and paths are presented. The parallel analog optical computation employs a two-dimensional spatial light modulator on which an image of the potential field map is generated. Optically calculated fields contain no local minima, tend to produce paths centered in gaps between obstacles, and produce paths which give preference to wide gaps. Calculation of 128 x 128 pixel fields at a few hertz are possible with current technology, and calculation time vs. map size scales favorably in comparison to digital electronic computation.
Want to achieve best representative characterization of the entire area efficiently and accurately-Unmanned aerial/ground vehicles (UAV/UGVs) for contamination mapping. Some major challenges include: Limited battery life (move smart), Human operated (fully autonomous controls) and Many measurements (predictive mapping capabilities). The objective: Develop fully autonomous controls for mobile sensor platforms to improve efficiency and maintain performance.
Distributed and networked mobile sensor platforms using unmanned aerial and/or ground vehicles to survey areas of interest offer a safer and more efficient method for radiological contamination mapping; however, most applications rely on uniformly sweeping of the area in a raster-type motion without utilizing the information available in a dynamic sense. We have developed a fully autonomous optimal motion planning procedure for networks with two or more mobile sensors. The procedure utilizes well-established concepts of Gaussian processes in combination with control laws based on centroidal Voronoi tessellations to achieve optimal next-iteration sensor movements. A new method of informing optimal motion planning is proposed, whereby the absolute difference between the prior and current full-map prediction, referred to as the prediction-difference map, is used as the spatial density function within each Voronoi cell, providing immediate and iterative feedback for dynamic use of available information. The Gaussian process regression model used to estimate the contamination in unvisited locations also provides prediction uncertainties, and can be used as a quantitative metric to assess the confidence in the calculated contamination map; these estimates and prediction uncertainties are unavailable for standard uniform survey routines as they can only produce maps in the vicinity of observed locations. We present through simulation the achievable performance gains from using this new method by directly comparing to a uniform survey method. Results show that using the prediction-difference maps to inform motion planning procedures offers a faster rate of producing an accurate and convergent map relative to a uniform survey route.
The population is increasingly becoming tractable as more and more people carry handheld devices as part of their everyday activities. Recent studies have shown that handheld devices' generated traffic share is now more than 50% of total global online traffic. This has created an unprecedented opportunity for modeling human mobility behavior. For example, aggregate check-ins and dwell time can reveal building level occupancies. However, there are clear limits to accurate modeling (e.g. reproducible, repeatable, and realistic), unless we decipher the underlying reason causing typical mobility patterns. We know that human behavior is a reflection of a set of activities, such as going to the gym or work, and which can be seen as a catalyst for humans to move from one location to another. This work envisions the use of activity characterization for modeling human mobility by introducing a context that maps activities to certain mobility patterns. In the end, we highlight the efficacy of the proposed approach by analyzing the impact of public policies surrounding stay at home order on human mobility.
Abstract not provided.
We describe techniques to optimally select landmarks in order to perform mobile robot localization in matching terrain maps.
Renewed interest in deep space exploration has made establishing a persistent human presence on the Moon and eventually Mars a priority. Sustained habitation of the Moon will require in-situ resource utilization (ISRU) and protection from radiation at the lunar surface. Lunar caves are of interest to the space community because they could provide shelter from radiation, access to water deposits, and a space for habitation, as well as hold information about the geology, volcanology, and evolution of the Moon. Developing the tools to explore, map, and characterize these voids is key to understanding the Moon as a planetary body and to establishing a permanent human presence there. Our team is currently developing tools that enable ultra-high resolution terrain mapping and navigation using mobile light detection and ranging (LIDAR) technology and simultaneous localization and mapping (SLAM) algorithms in fully GPS-denied and no-light environments. 3D mapping with LiDAR and SLAM is relatively under-used, particularly in the context of planetary exploration and planetary analog environments. The backpack mounted LiDAR instrument currently under development by the KNaCK (Kinematic Navigation and Cartography Knapsack) team has demonstrated the potential of mobile SLAM LiDAR for lunar, planetary, and terrestrial cave exploration, study, and utilization. A description of the instrument and associated SLAM algorithms used for mapping in GPS-denied environments are referenced here.
Bacterial evolution through horizontal gene transfer (HGT) reflects their community interactions. In this way, HGT networks do well at mapping community interactions, but offer little toward controlling them—an important step in the translation of synthetic strains into natural contexts. Toxin–antitoxin (TA) systems serve as ubiquitous and diverse agents of selection; however, their utility is limited by their erratic distribution in hosts. Here we examine the heterogeneous distribution of TAs as a consequence of their mobility. By systematically mapping TA systems across a 10,000 plasmid network, we find HGT communities have unique and predictable TA signatures. We propose these TA signatures arise from plasmid competition and have further potential to signal the degree to which plasmids, hosts, and phage interact. To emphasize these relationships, we construct an HGT network based solely on TA similarity, framing specific selection markers in the broader context of bacterial communities. This work both clarifies the evolution of TA systems and unlocks a common framework for manipulating community interactions through TA compatibility.
We describe and present experimental results of the optical calculation of potential field maps suitable for mobile robot navigation. The optical computation employs two write modes of a microchannel spatial light modulator (MSLM). In one mode, written patterns expand spatially, and this characteristic is used to create an extended two dimensional function representing the influence of the goal in a robot's workspace. Distinct obstacle patterns are written in a second, non-expanding, mode. A model of the mechanisms determining MSLM write mode characteristics is developed and used to derive the optical calculation time for full potential field maps. Field calculations at a few hertz are possible with current technology, and calculation time vs. map size scales favorably in comparison to digital electronic computation.
This paper presents an algorithm for localization and mapping for a mobile robot using monocular vision and odometry as its means of sensing. The approach uses the Variable State Dimension filtering (VSDF) framework to combine aspects of Extended Kalman filtering and nonlinear batch optimization. This paper describes two primary improvements to the VSDF. The first is to use an interpolation scheme based on Gaussian quadrature to linearize measurements rather than relying on analytic Jacobians. The second is to replace the inverse covariance matrix in the VSDF with its Cholesky factor to improve the computational complexity. Results of applying the filter to the problem of localization and mapping with omnidirectional vision are presented.
Understanding soil texture and organic matter content supports our understanding of hydrology and ecology of Arctic sites. Soil organic matter content and composition of sand, silt, and clay were measured from soils collected at the Teller 27 field site on the Seward Peninsula and at the Barrow Environmental Observatory (BEO) near Utqiaġvik, Alaska, on August 2nd and 6th 2024, respectively. Soil samples were collected from the active layer to varied depths. Precise location data were collected at each observation point using Avenza Maps on a mobile device. Sand, silt, clay, and organic matter percentages were measured at Desert Research Institute Soil Characterization and Quaternary Pedology Laboratory in Reno, NV. This dataset contains a *.csv file of soil properties, a *.kml file of measurement locations, a *.pdf user guide, a *.csv data dictionary, and a *.csv file level metadata.
The Mobile Atmospheric Pollutant Mapping System (MAPM) is a dual CO2 laser DIAL system with heterodyne detection that is being developed for large distance range resolved measurement of organic solvent vapors and aerosol clouds. The components have been chosen to allow measurements to be made to distances of 6 to 7 km in a period of 20 to 30 s. The major components of the system are listed. MAPM is being integrated into a system and will be tested with several organic solvent gases and vapors in a remotely positioned sample chamber and with a free release of ethylene. Experimental results and system performance are discussed.