Framework of Reliability-Based Stochastic Mobility Map for Next Generation NATO Reference Mobility Model

Gaul, Nicholas J.; Jayakumar, Paramsothy; Wasfy, Tamer M.; Funk, Matthew D.

doi:10.1115/1.4041350

Cited by 16 publications

(3 citation statements)

References 18 publications

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“…Since the resolution of the original DEM is too sparse for accurate prediction of vehicle mobility, an interpolation process is required to obtain a refined terrain model with a higher resolution. In our study, open-source remote sensing topographic data with a resolution of 30 m was used as the raw data; the required resolution for mobility prediction is usually about 10 m [23]. To further characterize other features of the selected terrain, the obtained land use data and soil type distribution data were projected onto the resulting refined DEM data.…”

Section: Terrain Reconstructionmentioning

confidence: 99%

A Global Path Planning Method for Unmanned Ground Vehicles in Off-Road Environments Based on Mobility Prediction

Chen

Niu

et al. 2022

Machines

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In a complex off-road environment, due to the low bearing capacity of the soil and the uneven features of the terrain, generating a safe and effective global route for unmanned ground vehicles (UGVs) is critical for the success of their motion and mission. Most traditional global path planning methods simply take the shortest path length as the optimization objective, which makes it difficult to plan a feasible and safe route in complex off-road environments. To address this problem, this research proposes a global path planning method, which considers the influence of terrain factors and soil mechanics on UGV mobility. First, we established a high-resolution 3D terrain model with remote sensing elevation terrain data, land use and soil type distribution data, based on a geostatistical method. Second, we analyzed the vehicle mobility by the terramechanical method (i.e., vehicle cone index and Bakker’s theory), and then calculated the mobility cost based on a fuzzy inference method. Finally, based on the calculated mobility cost, the probabilistic roadmap method was used to establish the connected matrix and the multi-dimensional traffic cost evaluation matrix among the sampling nodes, and then an improved A* algorithm was proposed to generate the global route.

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Section: Terrain Reconstructionmentioning

confidence: 99%

A Global Path Planning Method for Unmanned Ground Vehicles in Off-Road Environments Based on Mobility Prediction

Chen

Niu

et al. 2022

Machines

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“…to predict vehicle mobility over large spatial regions considering elevation uncertainty. In another example, Choi et el 5 . proposed a dynamic Kriging model for efficient uncertainty propagation of an off-road vehicle mobility model and generation of a stochastic mobility map 5 .…”

Section: Introductionmentioning

confidence: 99%

“…In another example, Choi et el 5 . proposed a dynamic Kriging model for efficient uncertainty propagation of an off-road vehicle mobility model and generation of a stochastic mobility map 5 . Quantifying the uncertainty of vehicle mobility was investigated in previously mentioned resources.…”

Section: Introductionmentioning

confidence: 99%

Autonomy and mobility simulation time reduction through machine learning while considering uncertainty and reliability prediction

Mange

Skowronska

2023

Opt. Eng.

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.Modeling and simulation (M&S) tools are used extensively throughout the Ground Vehicle Systems Center and the U.S. Army to perform an analysis of ground vehicles more quickly and less expensively than through physical testing. The Computational Research and Engineering Acquisition Tools and Environments-Ground Vehicles (CREATE-GV) project is an M&S software effort that focuses on mobility and autonomous vehicle simulation and analysis, using physics-based three-dimensional modeling to accurately calculate a variety of ground vehicle metrics and parameters of interest. However, because these simulations are high fidelity, they often require a great deal of computational power and time. One approach to reducing simulation time that has proved effective in certain contexts is the creation of “surrogate models” through machine learning (ML) algorithms. However, it is often very challenging to accurately predict the mobility of a ground vehicle system in general, and there is no existing model that can predict the mobility of autonomous systems. A great deal of uncertainty exists in the mobility and autonomy area of physics-based simulation models related to modeling assumptions, terrain conditions, and insufficient knowledge of interactions between the vehicle and terrain. Understanding how the uncertainties inherent in autonomous mobility prediction affect model accuracy is still an open fundamental research question. We present a surrogate modeling approach leveraging ML algorithms to work with CREATE-GV to increase the computation speed of mobility assessments while still considering the reliability of the mobility predictions under uncertainty.

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A penalized blind likelihood Kriging method for surrogate modeling

Zhang

Yao²,

Chen³

et al. 2019

Struct Multidisc Optim

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Framework of Reliability-Based Stochastic Mobility Map for Next Generation NATO Reference Mobility Model

Cited by 16 publications

References 18 publications

A Global Path Planning Method for Unmanned Ground Vehicles in Off-Road Environments Based on Mobility Prediction

A Global Path Planning Method for Unmanned Ground Vehicles in Off-Road Environments Based on Mobility Prediction

Autonomy and mobility simulation time reduction through machine learning while considering uncertainty and reliability prediction

A penalized blind likelihood Kriging method for surrogate modeling

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