Abstract
Probabilistic Roadmap approaches (PRMs) have been successfully applied in motion planning of robots with many degrees of freedom. In recent years, the community has proposed deterministic sampling as a way to improve the performance in these planners. However, our recent results show that the choice of the sampling source pseudo-random or deterministic- has small impact on a PRM planner’s performance. We used two single-query PRM planners for this comparative study. The advantage of the deterministic sampling on the pseudo-random sampling is only observable in low dimension problems. The results were surprising in the sense that deterministic sampling performed differently than claimed by the designers.
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Sánchez L., A., Juarez G., R., Osorio L., M.A. (2007). On the Performance of Deterministic Sampling in Probabilistic Roadmap Planning. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_104
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DOI: https://doi.org/10.1007/978-3-540-76631-5_104
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