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On the Use of Randomized Low-Discrepancy Sequences in Sampling-Based Motion Planning

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MICAI 2005: Advances in Artificial Intelligence (MICAI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3789))

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Abstract

This paper shows the performance of randomized low-discre-pancy sequences compared with others low-discrepancy sequences. We used two motion planning algorithms to test this performance: the expansive planner proposed in [1], [2] and SBL [3] . Previous research already showed that the use of deterministic sampling outperformed PRM approaches [4], [5], [6]. Experimental results show performance advantages when we use randomized Halton and Sobol sequences over Mersenne-Twister and the linear congruential generators used in random sampling.

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Author information

Authors and Affiliations

  1. Facultad de Ciencias de la Computación, BUAP, 14 Sur esq. San Claudio, CP 72550, Puebla, Pue., México

    Abraham Sánchez & Maria A. Osorio

Authors
  1. Abraham Sánchez
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  2. Maria A. Osorio
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Editor information

Editors and Affiliations

  1. National Polytechnic Institute, Center for Computing Research, 07738, Mexico City, México

    Alexander Gelbukh

  2. Technológico de Monterrey (ITESM), Campus Ciudad de México (CCM), Calle del Puente 222, Col. Ejudos de Huipulco, 14360 DF, Tlalpan, Mexico

    Álvaro de Albornoz

  3. Center for Intelligent Systems, Tecnológico de Monterrey, Campus Monterrey, 64849, Monterrey, N.L., Mexico

    Hugo Terashima-Marín

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© 2005 Springer-Verlag Berlin Heidelberg

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Sánchez, A., Osorio, M.A. (2005). On the Use of Randomized Low-Discrepancy Sequences in Sampling-Based Motion Planning. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science(), vol 3789. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11579427_100

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  • DOI: https://doi.org/10.1007/11579427_100

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29896-0

  • Online ISBN: 978-3-540-31653-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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