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|Title:||Hybrid PRM sampling with a cost-sensitive adaptive strategy|
|Authors:||Hsu, D. |
Probabilistic roadmap planners
|Citation:||Hsu, D.,Sánchez-Ante, G.,Sun, Z. (2005). Hybrid PRM sampling with a cost-sensitive adaptive strategy. Proceedings - IEEE International Conference on Robotics and Automation 2005 : 3874-3880. ScholarBank@NUS Repository. https://doi.org/10.1109/ROBOT.2005.1570712|
|Abstract:||A number of advanced sampling strategies have been proposed in recent years to address the narrow passage problem for probabilistic roadmap (PRM) planning. These sampling strategies all have unique strengths, but none of them solves the problem completely. In this paper, we present a general and systematic approach for adaptively combining multiple sampling strategies so that their individual strengths are preserved. We have performed experiments with this approach on robots with up to 12 degrees of freedom in complex 3-D environments. Experiments show that although the performance of individual sampling strategies varies across different environments, the adaptive hybrid sampling strategies constructed with this approach perform consistently well in all environments. Further, we show that, under reasonable assumptions, the adaptive strategies are provably competitive against all individual strategies used. © 2005 IEEE.|
|Source Title:||Proceedings - IEEE International Conference on Robotics and Automation|
|Appears in Collections:||Staff Publications|
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