Stochastic mobility prediction of ground vehicles over large spatial regions: a geostatistical approach
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 Eleva...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Springer US
2017
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| Online Access: | http://hdl.handle.net/1721.1/106845 https://orcid.org/0000-0002-3261-7991 |
| _version_ | 1826188863064768512 |
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| author | Jayakumar, Paramsothy Gonzalez Sanchez, Ramon Iagnemma, Karl |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Jayakumar, Paramsothy Gonzalez Sanchez, Ramon Iagnemma, Karl |
| author_sort | Jayakumar, Paramsothy |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T08:06:09Z |
| format | Article |
| id | mit-1721.1/106845 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:06:09Z |
| publishDate | 2017 |
| publisher | Springer US |
| record_format | dspace |
| spelling | mit-1721.1/1068452022-09-23T10:55:24Z Stochastic mobility prediction of ground vehicles over large spatial regions: a geostatistical approach Jayakumar, Paramsothy Gonzalez Sanchez, Ramon Iagnemma, Karl Massachusetts Institute of Technology. Department of Mechanical Engineering Gonzalez Sanchez, Ramon Iagnemma, Karl 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. U.S. Army Tank-Automotive Research, Development, and Engineering Center 2017-02-02T23:02:13Z 2017-02-02T23:02:13Z 2016-01 2015-04 2017-01-24T04:39:47Z Article http://purl.org/eprint/type/JournalArticle 0929-5593 1573-7527 http://hdl.handle.net/1721.1/106845 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. https://orcid.org/0000-0002-3261-7991 en http://dx.doi.org/10.1007/s10514-015-9527-z Autonomous Robots 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. Springer Science+Business Media New York application/pdf Springer US Springer US |
| spellingShingle | Jayakumar, Paramsothy Gonzalez Sanchez, Ramon Iagnemma, Karl Stochastic mobility prediction of ground vehicles over large spatial regions: a geostatistical approach |
| title | Stochastic mobility prediction of ground vehicles over large spatial regions: a geostatistical approach |
| title_full | Stochastic mobility prediction of ground vehicles over large spatial regions: a geostatistical approach |
| title_fullStr | Stochastic mobility prediction of ground vehicles over large spatial regions: a geostatistical approach |
| title_full_unstemmed | Stochastic mobility prediction of ground vehicles over large spatial regions: a geostatistical approach |
| title_short | Stochastic mobility prediction of ground vehicles over large spatial regions: a geostatistical approach |
| title_sort | stochastic mobility prediction of ground vehicles over large spatial regions a geostatistical approach |
| url | http://hdl.handle.net/1721.1/106845 https://orcid.org/0000-0002-3261-7991 |
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