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Tag: Autonomous robots
  • Mapping and Localization Within a Mock Sewer System

    Abstract: Herein, we explored a robot’s ability to localize and map, both in simulation and on a physical robot, within a mock sewer system. Mapping and localization techniques were first developed and tested in simulation and were then transitioned to the actual robot for additional physical testing. Several odometry and simultaneous localization and mapping (SLAM) techniques, including gmapping, SLAM toolbox, elevation mapping, and RTABMap, were evaluated for this particular environment. The results of the odometry and the various SLAM approaches are discussed in detail.
  • A General-Purpose Multiplatform GPU-Accelerated Ray Tracing API

    Abstract: Real-time ray tracing is an important tool in computational research. Among other things, it is used to model sensors for autonomous vehicle simulation, efficiently simulate radiative energy propagation, and create effective data visualizations. However, raytracing libraries currently offered for GPU platforms have a high level of complexity to facilitate the detailed configuration needed by gaming engines and high-fidelity renderers. A researcher wishing to take advantage of the performance gains offered by the GPU for simple ray casting routines would need to learn how to use these ray tracing libraries. Additionally, they would have to adapt this code to each GPU platform they run on. Therefore, a C++ API has been developed that exposes simple ray casting endpoints that are implemented in GPU-specific code for several contemporary device platforms. This API currently supports the NVIDIA OptiX ray tracing library, Vulkan, AMD Radeon Rays, and even Intel Embree. Benchmarking tests using this API provide insight to help users determine the optimal backend library to select for their ray tracing needs. HPC research will be well-served by the ability to perform general purpose raytracing on the increasing amount of graphics and machine learning nodes offered by the DoD High Performance Computing Modernization Program.
  • Autonomous GPR Surveys using the Polar Rover Yeti

    Abstract: The National Science Foundation operates stations on the ice sheets of Antarctica and Greenland to investigate Earth’s climate history, life in extreme environments, and the evolution of the cosmos. Understandably, logistics costs predominate budgets due to the remote locations and harsh environments involved. Currently, manual ground-penetrating radar (GPR) surveys must preceed vehicle travel across polar ice sheets to detect subsurface crevasses or other voids. This exposes the crew to the risks of undetected hazards. We have developed an autonomous rover, Yeti, specifically to conduct GPR surveys across polar ice sheets. It is a simple four-wheel-drive, battery-powered vehicle that executes autonomous surveys via GPS waypoint following. We describe here three recent Yeti deployments, two in Antarctica and one in Greenland. Our key objective was to demonstrate the operational value of a rover to locate subsurface hazards. Yeti operated reliably at −30 ◦C, and it has good oversnow mobility and adequate GPS accuracy for waypoint-following and hazard georeferencing. It has acquired data on hundreds of crevasse encounters to improve our understanding of heavily crevassed traverse routes and to develop automated crevasse-detection algorithms. Importantly, it helped to locate a previously undetected buried building at the South Pole. Yeti can improve safety by decoupling survey personnel from the consequences of undetected hazards. It also enables higher-quality systematic surveys to improve hazard-detection probabilities, increase assessment confidence, and build datasets to understand the evolution of these regions. Yeti has demonstrated that autonomous vehicles have great potential to improve the safety and efficiency of polar logistics.
  • Ground-penetrating Radar Profiles of the McMurdo Shear Zone, Antarctica, Acquired with an Unmanned Rover: Interpretation of Crevasses, Fractures, and Folds within Firn and Marine Ice

    Abstract: The crevassed firn of the McMurdo shear zone (SZ) within the Ross Ice Shelf may also contain crevasses deep within its meteoric and marine ice, but the surface crevassing prevents ordinary vehicle access to investigate its structure geophysically. We used a lightweight robotic vehicle to tow 200- and 40 MHz ground-penetrating radar antennas simultaneously along 10 parallel transects over a 28 km2 grid spanning the SZ width. Transects were generally orthogonal to the ice flow. Total firn and meteoric ice thickness was approximately 160 m. Firn crevasses profiled at 400 MHz were up to 16 m wide, under snow bridges up to 10 m thick, and with strikes near 35°–40° to the transect direction. From the top down, 200- MHz profiles revealed firn diffractions originating to a depth of approximately 40 m, no discernible structure within the meteoric ice, a discontinuous transitional horizon, and at least 20 m of stratified marine ice; 28–31 m of freeboard found more marine ice exists. Based on 10 consecutive transects covering approximately 2.5 km2, we preliminarily interpreted the transitional horizon to be a thin saline layer, and marine ice hyperbolic diffractions and reflections to be responses to localized fractures, and crevasses filled with unstratified marine ice, all at strikes from 27° to 50°. We preliminarily interpreted off nadir, marine ice horizons to be responses to linear and folded faults, similar to some in firn. The coinciding and synchronously folded areas of fractured firn and marine ice suggested that the visibly unstructured meteoric ice beneath our grid was also fractured, but either never crevassed, crevassed and sutured without marine ice inclusions, or that any ice containing crevasses might have eroded before marine ice accretion. We will test these interpretations with analysis of all transects and by extending our grid and increasing our depth ranges.
  • PUBLICATION NOTICE: Sensor and Environment Physics in the Virtual Autonomous Navigation Environment (VANE)

    Abstract: This report documents the physics models that are implemented in the Virtual Autonomous Navigation Environment (VANE), a sensor simulator that uses physics-based ray tracing to simulate common robotic sensors such as cameras, LiDAR, GPS, and automotive RADAR. The report will provide information about the underlying assumptions and implementation details regarding the physics models used in VANE simulations. These include surface reflectance and texture models, atmospheric models, weather effects, and sensor properties. The purpose of this report is to provide information for VANE users, developers, and analysts who would like to use the VANE for sensor simulations.