Toward Derandomizing PRM Planners

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.
Type de document :
Communication dans un congrès
MICAI: Mexican International Conference on Artificial Intelligence, Apr 2004, Mexico City, Mexico. 3rd Mexican International Conference on Artificial Intelligence, LNCS (2972), pp.911-920, 2004, MICAI 2004: Advances in Artificial Intelligence. 〈10.1007/978-3-540-24694-7_94〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00108932
Contributeur : Christine Carvalho de Matos <>
Soumis le : lundi 23 octobre 2006 - 12:57:18
Dernière modification le : jeudi 11 janvier 2018 - 06:26:17

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Abraham Sánchez Lopez, René Zapata. Toward Derandomizing PRM Planners. MICAI: Mexican International Conference on Artificial Intelligence, Apr 2004, Mexico City, Mexico. 3rd Mexican International Conference on Artificial Intelligence, LNCS (2972), pp.911-920, 2004, MICAI 2004: Advances in Artificial Intelligence. 〈10.1007/978-3-540-24694-7_94〉. 〈lirmm-00108932〉

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