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Carnegie Mellon Robotics Institute

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Abstract

Robot motions typically originate from an uninformed path sampling process such as random or low-dispersion sampling. We demonstrate an alternative approach to path sampling that closes the loop on the expensive collision-testing process. Although all necessary information for collision-testing a path is known to the planner, that information is typically stored in a relatively unavailable form in a costmap. By summarizing the most salient data in a more accessible form, our process delivers a denser sampling of the free space per unit time than open-loop sampling techniques. We obtain this result by probabilistically modeling—in real time and with minimal information—the locations of obstacles, based on collision test results. We demonstrate up to a 780% increase in paths surviving collision test.

Notes

Number of pages: 16

Text Reference

Ross Alan Knepper and Matthew T. Mason, "Realtime Informed Path Sampling for Motion Planning Search ," 15th International Symposium on Robotics Research (ISRR), August, 2011.

BibTeX Reference

@inproceedings{Knepper_2011_6907,
   author = "Ross Alan Knepper and Matthew T. Mason",
   title = "Realtime Informed Path Sampling for Motion Planning Search ",
   booktitle = "15th International Symposium on Robotics Research (ISRR)",
   month = "August",
   year = "2011",
   number= "CMU-RI-TR-",
}

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