Realtime Informed Path Sampling for Motion Planning Search

Ross Alan Knepper and Matthew T. Mason
15th International Symposium on Robotics Research (ISRR), August, 2011.


Download
  • Adobe portable document format (pdf) (477KB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

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-",
}