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Adaptive Hybrid Sampling
Overview
Several 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. We investigate
general and systematic approaches for adaptively combining
multiple sampling strategies so that their individual strengths
are preserved. Our preliminary results show that although
the performance of individual sampling strategies varies
across different environments, the adaptive hybrid sampling
strategies that we have constructed perform consistently
well. We can also show that, under
reasonable assumptions, the adaptive strategies are provably
competitive against all individual strategies used.
References
- D. Hsu, G. Sánchez-Ante, and Z. Sun. Hybrid PRM sampling with a cost-sensitive adaptive
strategy.
In Proc. IEEE Int. Conf. on Robotics & Automation, 2005.
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