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.
CITATION STYLE
Knepper, R. A., & Mason, M. T. (2017). Realtime informed path sampling for motion planning search. In Springer Tracts in Advanced Robotics (Vol. 100, pp. 401–417). Springer Verlag. https://doi.org/10.1007/978-3-319-29363-9_23
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