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

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• 作者：Ramón González ; Paramsothy Jayakumar ; Karl Iagnemma
• 关键词：NATO Reference Mobility Model (NRMM) ; Mission planning ; Geographical Information Systems (GIS) ; D* path planner ; Statistical sampling
• 刊名：Autonomous Robots
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：41
• 期：2
• 页码：311-331
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Robotics and Automation; Artificial Intelligence (incl. Robotics); Computer Imaging, Vision, Pattern Recognition and Graphics; Control, Robotics, Mechatronics;
• 出版者：Springer US
• ISSN：1573-7527
• 卷排序：41

文摘

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.