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

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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. 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. Both DEMs were downloaded from Webgis (2015).

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

References

  • AM General LLC specialized vehicles for commercial and military customers. http://www.amgeneral.com/vehicles/hmmwv; Visited: November, 2015.

  • American Society for Testing and Materials (1996) Standard guide for analysis of spatial variation in geostatistical site investigations. In Annual book of ASTM standards. (vol. 04.08). West Conshohocken, PA: ASTM.

  • Amidi, O. (1990). Integrated mobile robot control. Technical Report CMU–RI–TR–90–17, Robotics Institute, Carnegie Mellon University.

  • Anderson, A., Wang, G., & Gertner, G. (2006). Local variability based sampling for mapping a soil erosion cover factor by co-simulation with landsat TM images. International Journal of Remote Sensing, 27(12), 2423–2447.

    Article  Google Scholar 

  • Arieira, J., Karssenberg, D., de Jong, S., Addink, E., Couto, E., da Cunha, C. N., et al. (2011). Integrating field sampling, geostatistics and remote sensing to map wetland vegetation in the Pantanal, Brazil. Biogeosciences, 8, 667–686.

    Article  Google Scholar 

  • Basaran, M., Erpul, G., Ozcan, A., Saygin, D., Kibar, M., Bayramin, I., et al. (2011). Spatial information of soil hydraulic conductivity and performance of Cokriging over Kriging in a semi-arid basin scale. Environ Earth Sci, 63, 827–838.

    Article  Google Scholar 

  • Bechler, A., Romary, T., Jeannee, N., & Desnoyers, Y. (2013). Geostatistical sampling optimization of contaminated facilities. Stochastic Environmental Research and Risk Assessment, 27(8), 1967–1974.

    Article  Google Scholar 

  • Bivand, R., Pebesma, E., & Gomez-Rubio, V. (2013). Use R (2nd ed.)., Applied spatial data analysis with R New York: Springer.

    MATH  Google Scholar 

  • Bohling, G. (2005). Kriging. http://people.ku.edu/~gbohling/cpe940; Visited: November, 2015.

  • Brus, D., & Gruijter, J. (1994). Estimation of non-ergodic variograms and their sampling variance by design-based sampling strategies. Mathematical Geology, 26(4), 437–454.

    Article  Google Scholar 

  • Chiles, J., & Delfiner, P. (2012). Geostatistics. modeling spatial uncertainty, probabily and statistics (2nd ed.). New York: Wiley.

    Book  MATH  Google Scholar 

  • Corke, P. (2011). Robotics, vision and control., Fundamental algorithms in matlab. Springer Tracts in Advanced Robotics Dordrecht: Springer.

    Book  MATH  Google Scholar 

  • Davis, T., & Keller, C. (1997). Modelling uncertainty in natural resource analysis using fuzzy sets and Monte Carlo simulation: Slope stability prediction. International Journal of Geographical Information Science, 11(5), 409–434.

    Article  Google Scholar 

  • Fisher, P. (1991). Modelling soil map-unit inclusions by Monte Carlo simulation. International Jornal of Geographical Information Systems, 5(2), 193–208.

    Article  Google Scholar 

  • Fisher, P., & Tate, N. (2006). Causes and consequences of error in digital elevation models. Progress in Physical Geography, 30(4), 467–489.

    Article  Google Scholar 

  • Goldberg, S., Maimone, M., & Matthies, L. (2002). Stereo vision and rover navigation software for planetary exploration. IEEE Aerospace Conference, 5, 2025–2036.

    Google Scholar 

  • Gorsich, D., & Genton, M. (2000). Variogram model selection via nonparametric derivative estimation. Mathematical Geology, 32(3), 249–270.

    Article  Google Scholar 

  • Haley, P. W., Jurkat, M. P., & Brady, P. M. (1979). NATO reference mobility model, Edition I. Technical Report 12503. US Army TARDEC, Warren, MI.

  • Hadsell, R., Sermanet, P., Ben, J., Erkan, A., Scoffier, M., Kavukcuoglu, K., et al. (2009). Learning long-range vision for autonomous off-road driving. Journal of Field Robotics, 26(2), 120–144.

    Article  Google Scholar 

  • Helmick, D., Angelova, A., & Matthies, L. (2009). Terrain adaptive navigation for planetary rovers. Journal of Field Robotics, 26(4), 391–410.

    Article  Google Scholar 

  • Hosseini, S., Kappas, M., Bodaghabadi, M., Chahouki, M., & Khojasteh, E. (2014). Comparison of different geostatistical methods for soil mapping using remote sensing and environment variables in Pshtkouh rangelands, Iran. Polish Journal of Environmental Studies, 23(3), 737–751.

    Google Scholar 

  • Hunter, G., & Goodchild, M. (1997). Modeling the uncertainty of slope and aspect estimates derived from spatial databases. Geographical Analysis, 29(1), 35–49.

    Article  Google Scholar 

  • Isaaks, E., & Srivastava, R. (1989). An introduction to applied geostatistics. Oxford: Oxford University Press.

    MATH  Google Scholar 

  • Ishigami, G., Nagatani, K., & Yoshida, K. (2009). Slope traversal controls for planetary exploration rover on sandy terrain. Journal of Field Robotics, 26(3), 264–286.

    Article  Google Scholar 

  • Karumachi, S., Allen, T., Bailey, T., & Scheding, S. (2010). Non-parametric learning to aid path planning over slopes. The International Journal of Robotics Research, 29(8), 997–1018.

    Article  Google Scholar 

  • Kweon, I. S., & Kanade, T. (1992). High-resolution terrain map from multiple sensor data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2), 278–292.

    Article  Google Scholar 

  • Kerry, R., Oliver, M., & Frogbrook, Z. (2010). Sampling in precision agriculture. Geostatistical applications for precision agriculture (pp. 35–63). Dordrecht: Springer.

    Chapter  Google Scholar 

  • Kulis, B., & Jordan, M. (2012). Revisiting K-means: New algorithms via Bayesian nonparametrics. In International conference on machine learning (pp. 513–520): International Machine Learning Society (IMLS).

  • Kumar, J., Mills, R., Hoffman, F., & Hargrove, W. (2011). Parallel K-means clustering for quantitative ecoregion delineation using large data sets. Procedia Computer Science, 4, 1602–1611.

    Article  Google Scholar 

  • Lakhankar, T., Jones, A., Combs, C., Sengupta, M., Haar, T. V., & Khanbilvardi, R. (2010). Analysis of large scale spatial variability of soil moisture using a geostatistical method. Sensors, 10, 913–932.

    Article  Google Scholar 

  • LaValle, S. M. (2006). Planning algorithms. Cambridge: Cambridge University Press. http://planning.cs.uiuc.edu; Visited: November, 2015.

  • Lessem, A., Mason, G., & Ahlvin, R. (1996). Stochastic vehicle mobility forecasts using the NATO reference mobility model. Journal of Terramechanics, 33(6), 273–280.

    Article  Google Scholar 

  • Li, J., & Heap, A. (2011). A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors. Ecological Informatics, 6(3–4), 228–241.

    Article  Google Scholar 

  • mGstat: A geostatistical matlab toolbox. http://mgstat.sourceforge.net; Visited: November, 2015.

  • Papadakis, P. (2013). Terrain traversability analysis methods for unmanned ground vehicles: A survey. Engineering Applicatons of Artificial Intelligence, 26(4), 1373–1385.

    Article  Google Scholar 

  • Pengelly, J. (2002). Monte Carlo methods. http://www.cs.otago.ac.nz/cosc453; Visited: November, 2015.

  • Peynot, T., Lui, S., McAllister, R., Fitch, R., & Sukkarieh, S. (2014). Learned stochastic mobility prediction for planning with control uncertainty on unstructured terrain. Journal of Field Robotics, 31(6), 969–995.

    Article  Google Scholar 

  • Rubinstein, R., & Kroese, D. (2007). Simulation and the Monte Carlo method (2nd ed.)., Applied Probability and Statistics New York: Wiley.

    Book  MATH  Google Scholar 

  • Stentz, A. (1995). The focussed D* algorithm for real-time replanning. In Proceedings of the international joint conference on artificial intelligence.

  • Stentz, A., Kelly, A., Rander, P., Herman, H., Amidi, O., Mandelbaum, R., et al. (2003). Real-time, multi-perspective perception for unmanned ground vehicles. In Proceedings of of AUVSI unmanned systems symposium.

  • Stentz,T., Kelly, A., Herman, H., Rander, P., & Amidi, O. (2002). Integrated air/ground vehicle system for semi-autonomous off-road navigation. In AUVSI symposium (pp. 1–15).

  • Srivastava, R. (2013). Geostatistics: A toolkit for data analysis, spatial prediction and risk management in the coal industry. International Journal of Coal Geology, 112, 2–13.

    Article  Google Scholar 

  • Thompson, S. (2012). Sampling, probability and statistics (3rd ed.). New York: Wiley.

    Google Scholar 

  • Thrun, S., Montemerlo, M., et al. (2006). STANLEY, the robot that won the DARPA gran challenge. Journal of Field Robotics, 23(9), 661–692.

    Article  Google Scholar 

  • Tsui, O., Coops, N., Wulder, M., & Marshall, P. (2013). Integrating airborne LIDAR and space-borne radar via multivariable Kriging to estimate above-ground biomass. Remote Sensing of Environment, 139, 340–352.

    Article  Google Scholar 

  • Vandapel, N., Donamukkala, R., & Hebert, M. (2006). Unmanned ground vehicle navigation using aerial ladar data. International Journal of Robotics Research, 25(1), 31–51.

    Article  Google Scholar 

  • van der Meer, F. (2012). Remote-sensing image analysis and geostatistics. International Journal of Remote Sensing, 33(18), 5644–5676.

    Article  Google Scholar 

  • Vedaldi, A., Fulkerson, B. (2008). VLFeat: An open and portable library of computer vision algorithms. http://www.vlfeat.org; Visited: November, 2015.

  • WebGIS. (2105). Geographic information systems resource. http://www.webgis.com; Visited: November, 2015.

  • Webster, R., & Oliver, M. (2007). Geostatistics for environmental scientists (2nd ed.)., Statistics in Practice New York: Wiley.

    Book  MATH  Google Scholar 

  • Willoughby, W., Jones, R., Mason, G., Shoop, S., & Lever, J. (2006). Application of historical mobility testing to sensor-based robotic performance. In: Proceedings of SPIE 6230, unmanned systems technology VIII (pp. 1–8).

  • Wu, J. (2012). Advances in K-means clustering., Springer Theses Berlin: Springer.

    Book  MATH  Google Scholar 

  • Zhou, Q., & Liu, X. (2004). Analysis of errors of derived slope and aspect related to DEM data properties. Journal of Computer & Geosciences, 30, 369–378.

    Article  Google Scholar 

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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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Authors and Affiliations

  1. Massachusetts Institute of Technology, 77 Massachusetts Ave., Room 35-135a, Cambridge, MA, 02139, USA

    Ramón González

  2. Massachusetts Institute of Technology, 77 Massachusetts Ave., Room 35-237a, Cambridge, MA, 02139, USA

    Karl Iagnemma

  3. US Army RDECOM TARDEC, 6501 E. 11 Mile Road, MS 157, Bldg. 215, Warren, MI, 48397-5000, USA

    Paramsothy Jayakumar

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  1. Ramón González
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  2. Paramsothy Jayakumar
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Correspondence to Ramón González.

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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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