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

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... Full description

Journal Title: Autonomous Robots 2016, Vol.40(2), pp.361-381
Main Author: Norouzi, Mohammad
Other Authors: Valls Miro, Jaime , Dissanayake, Gamini
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 0929-5593 ; E-ISSN: 1573-7527 ; DOI: 10.1007/s10514-015-9474-8
Link: http://dx.doi.org/10.1007/s10514-015-9474-8
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recordid: springer_jour10.1007/s10514-015-9474-8
title: Probabilistic stable motion planning with stability uncertainty for articulated vehicles on challenging terrains
format: Article
creator:
  • Norouzi, Mohammad
  • Valls Miro, Jaime
  • Dissanayake, Gamini
subjects:
  • Probabilistic path planning
  • Uncertainty analysis
  • Tip-over stability
  • Mechanical reconfiguration
  • Rescue robotics
ispartof: Autonomous Robots, 2016, Vol.40(2), pp.361-381
description: 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.
language: eng
source:
identifier: ISSN: 0929-5593 ; E-ISSN: 1573-7527 ; DOI: 10.1007/s10514-015-9474-8
fulltext: fulltext
issn:
  • 1573-7527
  • 15737527
  • 0929-5593
  • 09295593
url: Link


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titleProbabilistic stable motion planning with stability uncertainty for articulated vehicles on challenging terrains
creatorNorouzi, Mohammad ; Valls Miro, Jaime ; Dissanayake, Gamini
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subjectProbabilistic path planning ; Uncertainty analysis ; Tip-over stability ; Mechanical reconfiguration ; Rescue robotics
descriptionA 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.
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titleProbabilistic stable motion planning with stability uncertainty for articulated vehicles on challenging terrains
descriptionA 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.
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abstractA 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.
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date2016-02