Combining Cost and Reliability for Rough Terrain Navigation

Jun-young Kwak, Mihail Pivtoraiko, and Reid Simmons. Combining Cost and Reliability for Rough Terrain Navigation. In 9th International Symposium on Artificial Intelligence, Robotics and Automation in Space, 2008.

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Abstract

This paper presents an improved method for planetary rover path planning in very rough terrain, based on the particle-based Rapidly-exploring Random Tree (pRRT) algorithm. It inherits the benefits of pRRT, an improvement over the conventional RRT algorithm that explicitly considers uncertainty in sensing, modeling, and actuation by treating each addition to the tree as a stochastic process. Although pRRT is well-suited to planning under uncertainty, it has limitations in minimizing the cost of path plans. Our approach addresses these limitations by considering the relevant cost functions explicitly. Such cost functions depend on the application and can include time or distance of traversal, and energy consumption of the rover. The paper demonstrates the planner performance using a specific cost function defined in terms of the energy expenditure. The improved pRRT algorithm has been experimentally validated in simulation, and it has been shown to produce lower-cost plans than the standard pRRT algorithm. The proposed approach is likely to benefit the present and future space missions as an onboard motion planner and as a ground-based tool for plan validation.

BibTeX

@INPROCEEDINGS{kwak_pivtoraiko_simmons_isairas08,
  author = {Jun-young Kwak and Mihail Pivtoraiko and Reid Simmons},
  title = {Combining Cost and Reliability for Rough Terrain Navigation},
  booktitle = {9th International Symposium on Artificial Intelligence, Robotics
	and Automation in Space},
  year = {2008},
  abstract = {This paper presents an improved method for planetary
                  rover path planning in very rough terrain, based on
                  the particle-based Rapidly-exploring Random Tree
                  (pRRT) algorithm. It inherits the benefits of pRRT,
                  an improvement over the conventional RRT algorithm
                  that explicitly considers uncertainty in sensing,
                  modeling, and actuation by treating each addition to
                  the tree as a stochastic process. Although pRRT is
                  well-suited to planning under uncertainty, it has
                  limitations in minimizing the cost of path
                  plans. Our approach addresses these limitations by
                  considering the relevant cost functions
                  explicitly. Such cost functions depend on the
                  application and can include time or distance of
                  traversal, and energy consumption of the rover. The
                  paper demonstrates the planner performance using a
                  specific cost function defined in terms of the
                  energy expenditure. The improved pRRT algorithm has
                  been experimentally validated in simulation, and it
                  has been shown to produce lower-cost plans than the
                  standard pRRT algorithm. The proposed approach is
                  likely to benefit the present and future space
                  missions as an onboard motion planner and as a
                  ground-based tool for plan validation.},
  bib2html_pubtype = {Refereed Conference Papers},
  bib2html_rescat = {Robot Navigation},
  owner = {mihail},
  timestamp = {2010.08.07}
}

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