Realtime Informed Path Sampling for Motion Planning Search

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

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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.

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)},
year = {2011},
month = {August},
} 2017-09-13T10:40:11-04:00