Autonomous Robots

, Volume 41, Issue 2, pp 311–331 | Cite as

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

  • Ramón González
  • Paramsothy Jayakumar
  • Karl Iagnemma


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.


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



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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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Ramón González
    • 1
  • Paramsothy Jayakumar
    • 3
  • Karl Iagnemma
    • 2
  1. 1.Massachusetts Institute of TechnologyCambridgeUSA
  2. 2.Massachusetts Institute of TechnologyCambridgeUSA

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