Stochastic mobility prediction of ground vehicles over large spatial regions: a geostatistical approach
Name
10514_2015_9527_ReferencePDF.pdf
Size
20.11 MB
Format
Adobe PDF
Checksum (MD5)
41d217ae2992e5275f1119d5d9506ddb
Author(s)
Jayakumar, Paramsothy
Gonzalez Sanchez, Ramon
Iagnemma, Karl
Date Issued
January 2016
Journal
Autonomous Robots
Publisher
Springer US
Citation
González, Ramón, Paramsothy Jayakumar, and Karl Iagnemma. “Stochastic Mobility Prediction of Ground Vehicles over Large Spatial Regions: a Geostatistical Approach.” Autonomous Robots 41, no. 2 (January 28, 2016): 311–331.
Version
Author's final manuscript
Abstract
This paper describes a stochastic approach to vehicle mobility prediction over large spatial regions [>5×5 (km[superscript 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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10514-015-9527-z
