Stochastic mobility prediction of ground vehicles over large spatial regions: a geostatistical approach

Abstract

This paper describes a stochastic approach to vehicle mobility prediction over large spatial regions [>\(5 \times 5\) (km\(^2\))]. The main source of uncertainty considered in this work derives from uncertainty in terrain elevation, which arises from sampling (at a finer resolution) a Digital Elevation Model. In order to account for such uncertainty, Monte Carlo simulation is employed, leading to a stochastic analysis of vehicle mobility properties. Experiments performed on two real data sets (namely, the Death Valley region and Sahara desert) demonstrate the advantage of stochastic analysis compared to classical deterministic mobility prediction. These results show the computational efficiency of the proposed methodology. The robotic simulator ANVEL has also been used to validate the proposed methodology.

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Notes

  1. 1.

    A DEM is defined as a set of elevation values which are recorded on a regular grid (i.e. square form) (Fisher and Tate 2006).

  2. 2.

    Both DEMs were downloaded from Webgis (2015).

  3. 3.

    Videos about this realistic simulation are available at: http://www.ual.es/personal/rgonzalez/videos.html.

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Acknowledgments

The research described in this publication was carried out at the Massachusetts Institute of Technology, under the Army Research Project Grant W911NF-13-1-0063 funded by US Army TARDEC. The authors also thank Justin Crawford from Quantum Signal for his support with ANVEL. The authors thank anonymous reviewers for providing useful comments on the paper.

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Correspondence to Ramón González.

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UNCLASSIFIED: Distribution Statement A. Approved for public release. #26532.

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González, R., Jayakumar, P. & Iagnemma, K. Stochastic mobility prediction of ground vehicles over large spatial regions: a geostatistical approach. Auton Robot 41, 311–331 (2017). https://doi.org/10.1007/s10514-015-9527-z

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Keywords

  • NATO Reference Mobility Model (NRMM)
  • Mission planning
  • Geographical Information Systems (GIS)
  • D* path planner
  • Statistical sampling