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A configuration deactivation algorithm for boosting probabilistic roadmap planning of robots

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Abstract

We present a method to improve the execution time used to build the roadmap in probabilistic roadmap planners. Our method intelligently deactivates some of the configurations during the learning phase and allows the planner to concentrate on those configurations that are most likely going to be useful when building the roadmap. The method can be used with many of the existing sampling algorithms. We ran tests with four simulated robot problems typical in robotics literature. The sampling methods applied were purely random, using Halton numbers, Gaussian distribution, and bridge test technique. In our tests, the deactivation method clearly improved the execution times. Compared with pure random selections, the deactivation method also significantly decreased the size of the roadmap, which is a useful property to simplify roadmap planning tasks.

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Authors and Affiliations

  1. Computer Sciences, School of Information Sciences, University of Tampere, Tampere, Finland

    Mika T. Rantanen & Martti Juhola

Authors
  1. Mika T. Rantanen
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  2. Martti Juhola
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Correspondence to Mika T. Rantanen.

Additional information

Mika T. Rantanen received the M. Sc. degree at the Department of Computer Sciences, University of Tampere, Finland in 2009, and now continues his research on robotics toward a Ph.D. degree.

His research interest includes probabilistic roadmap algorithms.

Martti Juhola received the M. Sc. and Ph.D. degrees in computer science from the University of Turku, Finland in the 1980 s, where he was an academic assistant, lecturer, and researcher, later becoming a professor at the University of Kuopio, Finland. Since 1997, he is a professor at the University of Tampere, Finland.

His research interests include medical informatics, signal analysis, pattern recognition, and information retrieval.

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Rantanen, M.T., Juhola, M. A configuration deactivation algorithm for boosting probabilistic roadmap planning of robots. Int. J. Autom. Comput. 9, 155–164 (2012). https://doi.org/10.1007/s11633-012-0628-2

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  • DOI: https://doi.org/10.1007/s11633-012-0628-2

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