Please use this identifier to cite or link to this item:
|Title:||Narrow passage sampling for probabilistic roadmap planning|
Probabilistic roadmap (PRM) planner
|Citation:||Sun, Z., Hsu, D., Jiang, T., Kurniawati, H., Reif, J.H. (2005). Narrow passage sampling for probabilistic roadmap planning. IEEE Transactions on Robotics 21 (6) : 1105-1115. ScholarBank@NUS Repository. https://doi.org/10.1109/TRO.2005.853485|
|Abstract:||Probabilistic roadmap (PRM) planners have been successful in path planning of robots with many degrees of freedom, but sampling narrow passages in a robot's configuration space remains a challenge for PRM planners. This paper presents a hybrid sampling strategy in the PRM framework for finding paths through narrow passages. A key ingredient of the new strategy is the bridge test, which reduces sample density in many unimportant parts of a configuration space, resulting in increased sample density in narrow passages. The bridge test can be implemented efficiently in high-dimensional configuration spaces using only simple tests of local geometry. The strengths of the bridge test and uniform sampling complement each other naturally. The two sampling strategies are combined to construct the hybrid sampling strategy for our planner. We implemented the planner and tested it on rigid and articulated robots in 2-D and 3-D environments. Experiments show that the hybrid sampling strategy enables relatively small roadmaps to reliably capture the connectivity of configuration spaces with difficult narrow passages. © 2005 IEEE.|
|Source Title:||IEEE Transactions on Robotics|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jul 9, 2018
WEB OF SCIENCETM
checked on Jun 20, 2018
checked on May 12, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.