A framework for generation of reliability-based stochastic off-road mobility maps is developed to support the next generation NATO reference mobility model (NG-NRMM) using full stochastic knowledge of terrain properties and modern complex terramechanics modeling and simulation capabilities. The framework is for carrying out uncertainty quantification (UQ) and reliability assessment for Speed Made Good and GO/NOGO decisions for the ground vehicle based on the input variability models of the terrain elevation and soil property parameters. To generate the distribution of the slope at given point, realizations of the elevation raster are generated using the normal distribution. For the soil property parameters, such as cohesion, friction, and bulk density, the min and max values obtained from geotechnical databases for each of the soil types are used to generate the normal distribution with a 99% confidence value range. In the framework, the ranges of terramechanics input parameters that will cover the regions of interest are first identified. Within these ranges of input parameters, a dynamic kriging (DKG) surrogate model is obtained for the maximum speed of the nevada automotive test center (NATC) wheeled vehicle platform complex terramechanics model. Finally, inverse reliability analysis using Monte Carlo simulation is carried out to generate the reliability-based stochastic mobility maps for Speed Made Good and GO/NOGO decisions. It is found that the deterministic map of the region of interest has probability of only 25% to achieve the indicated speed.

References

References
1.
McCullough
,
M.
,
Jayakumar
,
P.
,
Dasch
,
J.
, and
Gorsich
,
D.
,
2016
, “
Developing the Next Generation NATO Reference Mobility Model
,”
Ground Vehicle Systems Engineering and Technology Symposium (GVSETS)
, Aug. 2–4, Novi, MI.
2.
Rula
,
A. A.
, and
Nuttall
,
C. J.
,
1971
, “
An Analysis of Ground Mobility Models (ANAMOB)
,” US Army WES, Vicksburg, MS, Technical Report No. M-71-4.
3.
Haley
,
P. W.
,
Jurkat
,
M. P.
, and
Brady
,
P. M.
,
1979
, “
NATO Reference Mobility Model, Edition I
,” US Army TARDEC, Warren, MI, Technical Report No. 12503.
4.
Lessem
,
A.
,
Ahlvln
,
R.
,
Mason
,
G.
, and
Mlakar
,
P.
,
1992
, “
Stochastic Vehicle Mobility Forecasts Using the NATO Reference Mobility Model—Report I: Basic Concepts and Procedures
,” US Army TARDEC, Warren, MI, Technical Report No. GL-92-11.
5.
Lessem
,
A.
,
Ahlvln
,
R.
,
Mlakar
,
P.
, and
Stough
,
W.
,
1993
, “
Stochastic Vehicle Mobility Forecasts Using the NATO Reference Mobility Model—Report II: Extension of Procedures and Application to Historic Studies
,” US Army TARDEC, Warren, MI, Technical Report No. GL-93-15.
6.
Lessem
,
A.
,
Mason
,
G.
, and
Ahlvin
,
R.
,
1996
, “
Stochastic Vehicle Mobility Forecasts Using the NRMM
,”
J. Terramechanics
,
33
(
6
), pp.
273
280
.
7.
Vong
,
T.
,
Haas
,
G.
, and
Henry
,
C.
,
1999
, “
NATO Reference Mobility Model (NRMM) Modeling of the DEMO III Experimental Unmanned Ground Vehicle (XUV)
,” Army Research Laboratory, Aberdeen Proving Ground, Adelphi, MD, Technical Report No. ARL-MR-435.
8.
Bradbury
,
M.
,
Dasch
,
J.
,
Gonzalez-Sanchez
,
R.
,
Hodges
,
H.
,
Iagnemma, K, Jain
,
A.
,
Jayakumar
,
P.
,
Letherwood
,
M.
,
McCullough
,
M.
,
Priddy
,
J.
, and
Wojtysiak
,
B.
, 2018, “
Next-Generation NATO Reference Mobility Model (NG-NRMM)
,” North Atlantic Treaty Organisation and Science and Technology Organisation, Washington, DC, Final Report No.
STO-AVT-ET-148
.
9.
Bekker
,
M.
,
1969
,
Introduction to Terrain-Vehicle Systems
,
University of Michigan Press
, Ann Arbor, MI.
10.
Wong
,
J. Y.
,
2008
,
Theory of Ground Vehicles
,
4th ed.
,
Wiley
,
Hoboken, NJ
.
11.
Gonzalez
,
R.
,
Jayakumar
,
P.
, and
Iagnemma
,
K.
,
2017
, “
Generation of Stochastic Mobility Maps for Large-Scale Route Planning of Ground Vehicles: A Case Study
,”
J. Terramechanics
,
69
, pp.
1
11
.
12.
Azhar
,
A. T. S.
,
Norhaliza
,
W.
,
Ismail
,
B.
,
Abdullah
,
M. E.
, and
Zakaria
,
M. N.
,
2016
, “
Comparison of Shear Strength Properties for Undisturbed and Reconstituted Parit Nipah Peat, Johor
,”
IOP Conf. Ser. Mater. Sci. Eng.
,
160
, p.
12058
.
13.
Cressie
,
N.
,
1986
, “
Kriging Nonstationary Data
,”
J. Am. Stat. Assoc.
,
81
(
395
), pp.
625
634
.
14.
Zhang
,
J.
,
Atkinson
,
P. M.
, and
Goodchild
,
M. F.
,
2014
,
Scale in Spatial Information and Analysis
,
CRC Press
,
New York
.
15.
Atkinson
,
P. M.
, and
Lloyd
,
C. D.
,
2007
, “
Non-Stationary Variogram Models for Geostatistical Sampling Optimisation: An Empirical Investigation Using Elevation Data
,”
Comput. Geosciences
,
33
(
10
), pp.
1285
1300
.
16.
Zhao
,
L.
,
Choi
,
K. K.
, and
Lee
,
I.
,
2011
, “
Metamodeling Method Using Dynamic Kriging for Design Optimization
,”
AIAA J.
,
49
(
9
), pp.
2034
2046
.
17.
Chen
,
C.
, and
Li
,
Y.
,
2012
, “
An Adaptive Method of Non-Stationary Variogram Modeling for DEM Error Surface Simulation
,”
Trans. GIS
,
16
(
6
), pp.
885
899
.
18.
Wallin
,
J.
, and
Bolin
,
D.
,
2015
, “
Geostatistical Modeling Using Non-Gaussian Matérn Fields
,”
Scand. J. Stat.
,
42
(3), pp. 872–890.
19.
Lewis
,
R. M.
, and
Torczon
,
V.
,
1999
, “
Pattern Search Algorithms for Bound Constrained Minimization
,”
SIAM J. Optim.
,
9
(
4
), pp.
1082
1099
.
20.
Zhao
,
L.
,
Choi
,
K. K.
,
Lee
,
I.
, and
Gorsich
,
D.
,
2013
, “
Conservative Surrogate Model Using Weighted Kriging Variance for Sampling-Based RBDO
,”
ASME J. Mech. Des.
,
135
(
9
), pp.
1
10
.
21.
Song
,
H.
,
Choi
,
K. K.
, and
Lamb
,
D.
,
2013
, “
A Study on Improving the Accuracy of Kriging Models by Using Correlation Model/Mean Structure Selection and Penalized Log-Likelihood Function
,”
Tenth World Congress on Structural and Multidisciplinary Optimization
, Orlando, FL, Paper No. 5100.
22.
Sen
,
O.
,
Davis
,
S.
,
Jacobs
,
G.
, and
Udaykumar
,
H. S.
,
2015
, “
Evaluation of Convergence Behavior of Metamodeling Techniques for Bridging Scales in Multi-Scale Multimaterial Simulation
,”
J. Comput. Phys.
,
294
, pp.
585
604
.
23.
Moon
,
M.
,
Cho
,
H.
,
Choi
,
K. K.
,
Gaul
,
N.
,
Lamb
,
D.
, and
Gorsich
,
D.
,
2018
, “
Confidence-Based Reliability Assessment With Limited Numbers of Input and Output Test Data
,”
Struct. Multidiscip. Optim.
,
57
(5), pp. 2027–2043.
24.
Wasfy
,
T. M.
,
Jayakumar
,
P.
,
Mechergui
,
D.
, and
Sanikommu
,
S.
,
2016
, “
Prediction of Vehicle Mobility on Large-Scale Soft-Soil Terrain Maps Using Physics-Based Simulation
,”
NDIA Ground Vehicle Systems Engineering and Technology Symposium, Modeling and Simulation, Testing and Validation (MSTV) Technical Session
, Novi, MI.
25.
Wasfy
,
T. M.
,
Wasfy
,
H. M.
, and
Peters
,
J. M.
,
2015
, “
High-Fidelity Multibody Dynamics Vehicle Model Coupled With a Cohesive Soil Discrete Element Model for Predicting Vehicle Mobility
,”
ASME
Paper No. DETC2015-47134.
26.
RAMDO,
2018
,
RAMDO Solutions
,
LLC
,
Iowa City, IA
.
You do not currently have access to this content.