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.
- 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.