NDLI: Cloud $RRT^{∗}:$ Sampling Cloud based $RRT^{∗}$

Content Provider	IEEE Xplore Digital Library
Author	Donghyuk Kim Junghwan Lee Sung-eui Yoon
Copyright Year	2014
Description	Author affiliation: Dept. of CS, KAIST, Daejeon, South Korea (Donghyuk Kim; Junghwan Lee; Sung-eui Yoon)
Abstract	We present a novel biased sampling technique, Cloud RRT*, for efficiently computing high-quality collision-free paths, while maintaining the asymptotic convergence to the optimal solution. Our method uses sampling cloud for allocating samples on promising regions. Our sampling cloud consists of a set of spheres containing a portion of the C-space. In particular, each sphere projects to a collision-free spherical region in the workspace. We initialize our sampling cloud by conducting a workspace analysis based on the generalized Voronoi graph. We then update our sampling cloud to refine the current best solution, while maintaining the global sampling distribution for exploring understudied other homotopy classes. We have applied our method to a 2D motion planning problem with kinematic constraints, i.e., the Dubins vehicle model, and compared it against the state-of-the-art methods. We achieve better performance, up to three times, over prior methods in a robust manner.
Starting Page	2519
Ending Page	2526
File Size	1522309
Page Count	8
File Format	PDF
ISBN	9781479936854
DOI	10.1109/ICRA.2014.6907211
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-31
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Planning Convergence Trajectory Vehicles Collision avoidance Mobile robots
Content Type	Text
Resource Type	Article

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