Authors: Hsin-Yi Yeh, Shawna Thomas, David Eppstein, Nancy M. Amato

Venue: IEEE/RSJ International Conference on Intelligent Robots and Systems
DOI: 10.1109/IROS.2012.6385875
Link to Publication

Abstract:
This paper presents a new sampling method for motion planning that can generate configurations more uniformly distributed on C-obstacle surfaces than prior approaches. Here, roadmap nodes are generated from the intersections between C-obstacles and a set of uniformly distributed fixed-length segments in C-space. The results show that this new sampling method yields samples that are more uniformly distributed than previous obstacle-based methods such as OBPRM, Gaussian sampling, and Bridge test sampling. UOBPRM is shown to have nodes more uniformly distributed near C-obstacle surfaces and also requires the fewest nodes and edges to solve challenging motion planning problems with varying narrow passages. 

@inproceedings{Yeh-uaudop-2012, 
 author = {Yeh, Hsin-Yi and Thomas, Shawna and Eppstein, David and Amato, Nancy M.}, 
 booktitle = {2012 IEEE/RSJ International Conference on Intelligent Robots and Systems}, 
 doi = {10.1109/IROS.2012.6385875}, 
 number = {}, 
 pages = {2655-2662}, 
 title = {UOBPRM: A uniformly distributed obstacle-based PRM}, 
 volume = {}, 
 year = {2012} 
}