Training object detection algorithms to operate in complex geo-environments remains a significant challenge, necessitating large and diverse datasets (i.e., unique backgrounds and conditions) that are not always readily available. Physically generating requisite data can also be both cost and time prohibitive depending on the object(s) and area(s) of interest, especially in the case of multi-spectral and hyper-spectral imagery. Thus, there is increasing interest in the use of synthetic data to supplement existing physical datasets. To this end, the US Army Engineer Research and Development Center (ERDC) continues to develop a computational test-bed with a tool suite called the VESPA or, the Virtual Environmental Simulation for Physics-based Analysis, to support synthetic multi-spectral and hyper-spectral EO/IR imagery generation. The VESPA consists of integrated (1) scene generation tools, (2) multi-fidelity models for simulating heat and mass transfer and atmospheric energy propagation in geo-environments and climates worldwide that are optimized for high performance computing (3) data interrogation utilities, and (4) component-level sensor models capable of producing AI/ML ready near- and far-field imagery that is comparable to that produced by real sensors. This study presents an overview of the VESPA, new advances/capabilities, and results from a recent detailed validation and verification study.
The accumulation of falling snow is a complex physical process that involves a variety of environmental factors. While much past work has been done on the rendering of accumulated snow for gaming applications, scientific simulation of snow accumulation has been limited to large-scale mountain ranges and watersheds. These largescale simulations are not relevant for simulations of autonomous ground vehicle (AGV) performance, for which the relevant length scales are a few meters to a few hundred meters. In this work, we present a physics-based simulation of the accumulation of falling snow that is implemented using smoothed-particle hydrodynamics (SPH) to represent snow mass elements. SPH has been used in past work to simulate not only fluids but also deformable and continuous media ranging from concrete to fabric to soil. In this work we show that SPH can be parametrized to have material properties that reasonably approximate the bulk properties of accumulated snow. We present several example simulations in which SPH has been used to calculate the accumulation of fallen snow in an off-road scene. Finally, we show how the SPH simulation output can be combined with a rendering simulation to create realistic synthetic images.
Autonomous vehicles (AVs) employ a wide range of sensing modalities including LiDAR, radar, RGB cameras, and more recently infrared (IR) sensors. IR sensors are becoming an increasingly common component of AVs’ sensor packages to provide redundancy and enhanced capabilities in conditions that are adverse for other types of sensors. For example, while RGB cameras are sensitive to lighting conditions and LiDAR performance is degraded in inclement weather such as rain, IR sensors are unaffected by lighting conditions and can contribute additional meaningful information in inclement weather. The US Army Corps of Engineers, Engineer Research and Development Center (ERDC) has developed the ERDC Computational Test Bed (CTB) to provide a suite of tools that can be used to support virtual development and testing of AVs. The CTB includes physics-based vehicle-terrain interaction, sensor and environment modeling, geo-environmental thermal modeling, software-inthe- loop capabilities, and virtual environment generation. Thermal modeling capabilities within the CTB utilize decades of near-surface phenomenology and autonomy research. Recent additions have been made to support large-domains commonly required for autonomous vehicle operations. These additions provide high-fidelity, physics-based thermal transfer and IR sensor models for creating high-quality synthetic imagery simulating IR sensors mounted on AVs. Highly parallelized thermal and IR sensor models for large-domain AV operations will be presented in this paper.
The United State Army Corp of Engineers (USACE) Engineering Research and Development Center (ERDC) has developed a suite of computational tools called the Computational Test Bed (CTB) for advanced high-fidelity physics-based autonomous vehicle sensor and environment simulations. These tools provide insights into onboard navigation, image processing, sensor fusion techniques, and rapid data generation for artificial intelligence and machine learning techniques across the full spectrum (visible, NIR, MWIR, and LWIR) and for various sensor modalities (LiDAR, EO, radar). This paper presents ERDC’s CTB that allows the community to design, develop, test, and evaluate the entire autonomy space from machine learning algorithm development using augmented synthetic data to large-scale autonomous system testing.
Autonomous unmanned ground vehicles (UGVs) are beginning to play a more critical role in military operations. As the size of the fighting forces continues to draw down, the U.S. and coalition partner Armed Forces will become increasingly reliant on UGVs to perform mission-critical roles. These roles range from squad-level manned-unmanned teaming to large-scale autonomous convoy operations. However, as more UGVs with increasing levels of autonomy are entering the field, tools for accurately predicting these UGVs performance and capabilities are lacking. In particular, the mobility of autonomous UGVs is a largely unsolved problem. While legacy tools for predicting ground vehicle mobility are available for both assessing performance and planning operations, in particular the NATO Reference Mobility Model, no such toolset exists for autonomous UGVs. Once autonomy comes into play, ground vehicle mechanical-mobility is no longer enough to characterize vehicle mobility performance. Not only will vehicle-terrain interactions and driver concerns impact mobility, but sensor-environment interactions will also affect mobility. UGV mobility will depend in a large part on the sensor data available to drive the UGVs autonomy algorithms. A limited amount of research has been focused on the concept of perception-based mobility to date. To that end, the presented work will provide a review of the tools and methods developed thus far for modeling, simulating, and assessing autonomous mobility for UGVs. This review will highlight both the modifications being made to current mobility modeling tools and new tools in development specifically for autonomous mobility modeling. In light of this review, areas of current need will also be highlighted, and recommended steps forward will be proposed.
KEYWORDS: 3D modeling, Physics, Data modeling, Thermal modeling, Energy harvesting, Transducers, Electronics, Systems modeling, Numerical analysis, Finite element methods, Device simulation, Mechanics
Energy Harvesting is a powerful process that deals with exploring different possible ways of converting energy dispersed in the environment into more useful form of energy, essentially electrical energy. Piezoelectric materials are known for their ability of transferring mechanical energy into electrical energy or vice versa. Our work takes advantage of piezoelectric material’s properties to covert thermal energy into electrical energy in an oscillating heat pipe. Specific interest in an oscillating heat pipe has relevance to energy harvesting for low power generation suitable for remote electronics operation as well as low-power heat reclamation for electronic packaging. The aim of this paper is develop a 2D multi-physics design analysis model that aids in predicting electrical power generation inherent to an oscillating heat pipe. The experimental design shows a piezoelectric patch with fixed configuration, attached inside an oscillating heat pipe and its behavior when subjected to the oscillating fluid pressure was observed. Numerical analysis of the model depicting the similar behavior was done using a multiphysics FEA software. The numerical model consists of a threeway physics interaction that takes into account fluid flow, solid mechanics, and electrical response of the harvester circuit.
A series of experiments were conducted to investigate and characterize the concept of ferrofluidic induction - a process for generating electrical power via cyclic oscillation of ferrofluid (iron-based nanofluid) through a solenoid. Experimental parameters include: number of bias magnets, magnet spacing, solenoid core, fluid pulse frequency and ferrofluid-particle diameter. A peristaltic pump was used to cyclically drive two aqueous ferrofluids, consisting of 7-10 nm iron-oxide particles and commercially-available hydroxyl-coated magnetic beads (~800 nm), respectively. The solutions were pulsated at 3, 6, and 10 Hz through 3.2 mm internal diameter Tygon tubing. A 1000 turn copper-wire solenoid was placed around the tube 45 cm away from the pump. The experimental results indicate that the ferrofluid is capable of inducing a maximum electric potential of approximately +/- 20 μV across the solenoid during its cyclic passage. As the frequency of the pulsating flow increased, the ferro-nanoparticle diameter increased, or the bias magnet separation decreased, the induced voltage increased. The type of solenoid core material (copper or plastic) did not have a discernible effect on induction. These results demonstrate the feasibility of ferrofluidic induction and provide insight into its dependence on fluid/flow parameters. Such fluidic/magneto-coupling can be exploited for energy harvesting and/or conversion system design for a variety of applications.
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