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contributor authorLiu, Yixuan;Barthlow, Dakota;Mourelatos, Zissimos P.;Zeng, Jice;Gorsich, David;Singh, Amandeep;Hu, Zhen
date accessioned2022-12-27T23:17:53Z
date available2022-12-27T23:17:53Z
date copyright8/4/2022 12:00:00 AM
date issued2022
identifier issn1050-0472
identifier othermd_144_9_091709.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4288326
description abstractMobility prediction of off-road autonomous ground vehicles (AGV) in uncertain environments is essential for their model-based mission planning, especially in the early design stage. While surrogate modeling methods have been developed to overcome the computational challenge in simulation-based mobility prediction, it is very challenging for a single surrogate model to accurately capture the complicated vehicle dynamics. With a focus on vertical acceleration of an AGV under off-road conditions, this article proposes a surrogate modeling approach for AGV mobility prediction using a dynamic ensemble of nonlinear autoregressive models with exogenous inputs (NARX) over time. Synthetic vehicle mobility data of an AGV are first collected using a limited number of high-fidelity simulations. The data are then partitioned into different segments using a variational Gaussian mixture model to represent different vehicle dynamic behaviors. Based on the partitioned data, multiple surrogate models are constructed under the NARX framework with different numbers of lags. The NARX models are then assembled together dynamically over time to predict the mobility of the AGV under new conditions. A case study demonstrates the advantages of the proposed method over the classical NARX models for AGV mobility prediction.
publisherThe American Society of Mechanical Engineers (ASME)
titleMobility Prediction of Off-Road Ground Vehicles Using a Dynamic Ensemble of NARX Models
typeJournal Paper
journal volume144
journal issue9
journal titleJournal of Mechanical Design
identifier doi10.1115/1.4054908
journal fristpage91709
journal lastpage91709_15
page15
treeJournal of Mechanical Design:;2022:;volume( 144 ):;issue: 009
contenttypeFulltext


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