Generation of stochastic mobility maps for large-scale route planning of ground vehicles: A case study

González, Roberto J.; Jayakumar, Paramsothy; Iagnemma, Karl

doi:10.1016/j.jterra.2016.10.001

Cited by 17 publications

(7 citation statements)

References 19 publications

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“…In this sense, the learning algorithms return a continuous value for slip and, even, the variance associated with such value. This information might be exploited by path planners in order to avoid those areas with high uncertainty / variability in the estimated slip (Gonzalez et al, 2017a;Lee et al, 2016). Additionally, motion controllers might generate robust control actions despite uncertainty in slippage (Gonzalez et al, 2014(Gonzalez et al, , 2011.…”

Section: Discussionmentioning

confidence: 99%

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Slippage prediction for off-road mobile robots via machine learning regression and proprioceptive sensing

González

Fiacchini

Iagnemma

2018

Robotics and Autonomous Systems

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This paper presents a new approach for predicting slippage associated with individual wheels in off-road mobile robots. More specifically, machine learning regression algorithms are trained considering proprioceptive sensing. This contribution is validated by using the MIT single-wheel testbed equipped with an MSL spare wheel. The combination of IMU-related and torque-related features outperforms the torque-related features only. Gaussian process regression results in a proper trade-off between accuracy and computation time. Another advantage of this algorithm is that it returns the variance associated with each prediction, which might be used for future route planning and control tasks. The paper also provides a comparison between machine learning regression and classification algorithms.

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Section: Discussionmentioning

confidence: 99%

“…GPR accounts for a broad body of research and has been used by many references, specially in the field of geostatistics as a way to generate terrain models and mobility maps. For example, in (Gonzalez et al, 2017a), a method based on GPR (i.e. Ordinary Kriging)…”

Section: Related Workmentioning

confidence: 99%

Slippage prediction for off-road mobile robots via machine learning regression and proprioceptive sensing

González

Fiacchini

Iagnemma

2018

Robotics and Autonomous Systems

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“…The propagation of variability involves calculating propagation of variabilities from elevation and soil property measurements into to mobility, such as Speed Made Good and GO/NO-GO, using terramechanics simulation models for generation of reliability-based stochastic mobility map, across the given geographic area. In this paper, we describe a framework that is developed for a stochastic approach for vehicle mobility prediction over a region of interest [11]. In this framework, an input model of the terrain is created using geostatistical methods.…”

Section: Figure 1 Ng-nrmm Mobility Map Generation -mentioning

confidence: 99%

“…The previous NG-NRMM uncertainty treatment effort [11] used simple Kriging (i.e., ordinary Kriging) to fit elevation data via the ArcGIS Geostatistical extension.…”

Section: Advanced Kriging For Surrogate Modellingmentioning

confidence: 99%

“…• The first one is a subregion-based Kriging model to deal with the common issue of nonstationary variogram models [11,17,18,19,20], since the variogram can be considered stationary within the smaller subregion. In addition, the subregion method will allow parallel processing in generation of the Kriging models and thus achieve faster computational time.…”

Section: Advanced Kriging For Surrogate Modellingmentioning

confidence: 99%

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

Gaul

Jayakumar

Wasfy

et al. 2019

Journal of Computational and Nonlinear Dynamics

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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.

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2019

Path Planning of Cooperative Mobile Robots Using Discrete Event Models

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Generation of stochastic mobility maps for large-scale route planning of ground vehicles: A case study

Cited by 17 publications

References 19 publications

Slippage prediction for off-road mobile robots via machine learning regression and proprioceptive sensing

Slippage prediction for off-road mobile robots via machine learning regression and proprioceptive sensing

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

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