2016
DOI: 10.1016/j.jterra.2015.10.002
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Mobility algorithm evaluation using a consolidated database developed for wheeled vehicles operating on dry sands

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Cited by 19 publications
(10 citation statements)
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“…Because neither of these models accounted for damage accumulation under repeated load cycles, they were not applicable to predicting deformation underneath a matted surface. Vahedifard et al (2016) and Vahedifard et al (2017) assembled a database of thousands of off-road mobility algorithms for sand and clay soils to evaluate numerous parameters such as sinkage. The database is referred to as DROVE (Database Records for Off-Road Vehicle Environments).…”
Section: Soil Classificationmentioning
confidence: 99%
“…Because neither of these models accounted for damage accumulation under repeated load cycles, they were not applicable to predicting deformation underneath a matted surface. Vahedifard et al (2016) and Vahedifard et al (2017) assembled a database of thousands of off-road mobility algorithms for sand and clay soils to evaluate numerous parameters such as sinkage. The database is referred to as DROVE (Database Records for Off-Road Vehicle Environments).…”
Section: Soil Classificationmentioning
confidence: 99%
“…As a part of this multi-year effort, DROVE was created and is being further extended using the existing test results performed on coarse-grained and fine-grained soils. Vahedifard et al (2016) presented the first release of DROVE which includes tests conducted on sand. Mason et al (2016) used the sand database to reduce the complexity of previous sinkage algorithms while improving the correlations.…”
Section: Applications and Future Extensionsmentioning
confidence: 99%
“…A significant number of test results are required to test and develop prediction equations. A recently created database, DROVE 1.0, 12 provided the historic test data considered in this report. DROVE contains records of over 5000 test results with a variety of parameters and experimental procedures.…”
Section: Database Records For Off-road Vehicle Environments 10mentioning
confidence: 99%
“…11 Despite numerous advances in the development of predictive algorithms, the calibration and simplification of existing equations based on a larger database is required. Vahedifard et al 12 evaluated the predictive accuracy of several algorithms used in the Vehicle Terrain Interface (VTI) model by comparing their predictions against measured values available through Database Records for Off-road Vehicle Environments (DROVE) version 1.0, a recently developed database of historic vehicle tests. The comparison results revealed that the VTI algorithms can be further improved to provide more accurate predictions for off-road vehicle performance.…”
Section: Introductionmentioning
confidence: 99%