Abstract
Probabilistic roadmap methods (PRM) have been successfully applied in motion planning for robots with many degrees of freedom. Many recent PRM approaches have demonstrated improved performance by concentrating samples in a nonuniform way. This work replace the random sampling by the deterministic one. We present several implementations of PRM-based planners (multiple-query, single-query and Lazy PRM) and lattice-based roadmaps. Deterministic sampling can be used in the same way than random sampling. Our work can be seen as an important part of the research in the uniform sampling field. Experimental results show performance advantages of our approach.
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Sánchez, A., Zapata, R. (2004). Towards Derandomizing PRM Planners. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_94
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DOI: https://doi.org/10.1007/978-3-540-24694-7_94
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