Probabilistic stable motion planning with stability uncertainty for articulated vehicles on challenging terrains

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• 作者：Mohammad Norouzi ; Jaime Valls Miro ; Gamini Dissanayake
• 关键词：Probabilistic path planning ; Uncertainty analysis ; Tip ; over stability ; Mechanical reconfiguration ; Rescue robotics
• 刊名：Autonomous Robots
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：40
• 期：2
• 页码：361-381
• 全文大小：3,676 KB
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• 作者单位：Mohammad Norouzi (1)
  Jaime Valls Miro (1)
  Gamini Dissanayake (1)
  
  1. Faculty of Engineering and IT, University of Technology, Sydney (UTS), 15 Broadway, Ultimo, NSW, 2007, Australia
  
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Automation and Robotics
  Electronic and Computer Engineering
  Computer Imaging, Vision, Pattern Recognition and Graphics
  Mechanical Engineering
  Simulation and Modeling
  
• 出版者：Springer Netherlands
• ISSN：1573-7527

文摘

A probabilistic stable motion planning strategy applicable to reconfigurable robots is presented in this paper. The methodology derives a novel statistical stability criterion from the cumulative distribution of a tip-over metric. The measure is dynamically updated with imprecise terrain information, localization and robot kinematics to plan safety-constrained paths which simultaneously allow the widest possible visibility of the surroundings by simultaneously assuming highest feasible vantage robot configurations. The proposed probabilistic stability metric allows more conservative poses through areas with higher levels of uncertainty, while avoiding unnecessary caution in poses assumed at well-known terrain sections. The implementation with the well known grid based A* algorithm and also a sampling based RRT planner are presented. The validity of the proposed approach is evaluated with a multi-tracked robot fitted with a manipulator arm and a range camera using two challenging elevation terrains data sets: one obtained whilst operating the robot in a mock-up urban search and rescue arena, and the other from a publicly available dataset of a quasi-outdoor rover testing facility.