@COMMENT This file was generated by bib2html.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <http://sourceforge.net/users/patstg/>
@COMMENT This file came from Mihail Pivtoraiko's publication pages at
@COMMENT http://www.seas.upenn.edu/~mihailp/pub/class_type.html
@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}
}