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

Abstract This paper describes a simple and efficient methodology to generate a mobility map accounting for two sources of uncertainty, namely measurement errors (RMSE of a Digital Elevation Model) and interpolation error (kriging method). The proposed methodology means a general-purpose solution since it works with standard and publicly-available Digital Elevation Models (DEMs). The different regions in the map are classified according to the geometry of the surface (i.e. slope) and the soil type. A real USGS DEM demonstrates the suitability of the proposed methodology: (1) interpolation of a 26 × 40 -km 2 DEM to a finer resolution (30-m to 20-m); (2) analysis of the number of random realizations to account for the variability of the data; (3) efficient computation time (4-million-point DEM requires less than 30 min to complete the whole process); (4) route planning using the stochastic mobility map (constraints in slope and soil properties). UNCLASSIFIED: Distribution Statement A. Approved for public release; distribution is unlimited. #27681

[1]  Modest Lyasko,et al.  How to calculate the effect of soil conditions on tractive performance , 2010 .

[2]  Thierry Peynot,et al.  Learned Stochastic Mobility Prediction for Planning with Control Uncertainty on Unstructured Terrain , 2014, J. Field Robotics.

[3]  Min-Wook Kang,et al.  A GIS-BASED SIMULATION MODEL FOR MILITARY PATH PLANNING OF UNMANNED GROUND ROBOTS , 2011 .

[4]  D. C. Erbach,et al.  Effect of wetting and drying on soil physical properties 1 Joint contribution USDAAgricultural Rese , 1999 .

[5]  Trevor J. Davis,et al.  Modelling Uncertainty in Natural Resource Analysis Using Fuzzy Sets and Monte Carlo Simulation: Slope Stability Prediction , 1997, Int. J. Geogr. Inf. Sci..

[6]  Jody D. Priddy,et al.  Clarification of vehicle cone index with reference to mean maximum pressure , 2006 .

[7]  M. G. Bekker.  Introduction to Terrain-Vehicle Systems , 1969 .

[8]  Alonzo Kelly,et al.  Real-Time, Multi-Perspective Perception for Unmanned Ground Vehicles , 2003 .

[9]  Pietro Perona,et al.  Learning and prediction of slip from visual information , 2007, J. Field Robotics.

[10]  Michael F. Goodchild,et al.  Modeling the Uncertainty of Slope and Aspect Estimates Derived from Spatial Databases , 2010 .

[11]  E. Çokça,et al.  Effects of compaction moisture content on the shear strength of an unsaturated clay , 2004 .

[12]  Allan Lessem,et al.  Stochastic Vehicle Mobility Forecasts Using the NATO Reference Mobility Model. Report 2 Extension of Procedures and Application to Historic Studies , 1993 .

[13]  Chuanfa Chen,et al.  An Adaptive Method of Non-stationary Variogram Modeling for DEM Error Surface Simulation , 2012, Trans. GIS.

[14]  Clifford J Nuttall,et al.  An Analysis of Ground Mobility Models (ANAMOB) , 1971 .

[15]  Jeffrey J. Biesiadecki,et al.  Traverse Performance Characterization for the Mars Science Laboratory Rover , 2013, J. Field Robotics.

[16]  Karl Iagnemma,et al.  Stochastic mobility prediction of ground vehicles over large spatial regions: a geostatistical approach , 2017, Auton. Robots.

[17]  Edward McKyes,et al.  Characterization of cohesion, friction and sensitivity of two hardsetting soils from Zimbabwe , 1994 .

[18]  Karl Iagnemma,et al.  Predicting mobility performance of a small, lightweight track system using the computer-aided method NTVPM , 2015 .

[19]  Anthony Stentz,et al.  The Focussed D* Algorithm for Real-Time Replanning , 1995, IJCAI.

[20]  Pavla Pilařová,et al.  Influence of water content on the shear strength parameters of clayey soil in relation to stability analysis of a hillside in Brno region , 2013 .

[21]  Jonathan Edau Loh.  Speed Map for Autonomous Rovers over Rough Terrain , 2012 .

[22]  Martial Hebert,et al.  Unmanned Ground Vehicle Navigation Using Aerial Ladar Data , 2006, Int. J. Robotics Res..

[23]  Larry Matthies,et al.  Stereo vision and rover navigation software for planetary exploration , 2002, Proceedings, IEEE Aerospace Conference.

[24]  Rémi Bardenet,et al.  Monte Carlo Methods , 2013, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[25]  Arturo Martínez Rodríguez,et al.  Validación del modelo de cálculo de la potencia consumida por las picadoras de forraje del tipo de tambor con alimentación manual , 2012 .

[26]  Karl Iagnemma,et al.  Predictable mobility , 2009, IEEE Robotics & Automation Magazine.

[27]  Peter M. Atkinson,et al.  Increasing the accuracy of kriging and the kriging variance through fractal-based segmentation: Application to a photogrammetrically-derived DTM , 1999 .

[28]  P. Kyriakidis,et al.  Error in a USGS 30-meter digital elevation model and its impact on terrain modeling , 2000 .

[29]  Sebastian Thrun,et al.  Probabilistic Terrain Analysis For High-Speed Desert Driving , 2006, Robotics: Science and Systems.

[30]  Jo Yung Wong,et al.  Theory of ground vehicles , 1978 .

[31]  Michael Edward Hohn,et al.  An Introduction to Applied Geostatistics: by Edward H. Isaaks and R. Mohan Srivastava, 1989, Oxford University Press, New York, 561 p., ISBN 0-19-505012-6, ISBN 0-19-505013-4 (paperback), $55.00 cloth, $35.00 paper (US) , 1991 .