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Speeding up probabilistic roadmap planners with locality-sensitive hashing

Published online by Cambridge University Press:  09 April 2014

Mika T. Rantanen  and
Martti Juhola
Mika T. Rantanen*
Affiliation:
Computer Science, School of Information Sciences, Kalevantie 4, FI-33014, University of Tampere, Tampere, Finland
Martti Juhola
Affiliation:
Computer Science, School of Information Sciences, Kalevantie 4, FI-33014, University of Tampere, Tampere, Finland
*
*Corresponding author. E-mail: [email protected]
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Summary

A crucial part of probabilistic roadmap planners is the nearest neighbor search, which is typically done by exact methods. Unfortunately, searching the neighbors can become a major bottleneck for the performance. This can occur when the roadmap size grows especially in high-dimensional spaces. In this paper, we investigate how well the approximate nearest neighbor searching works with probabilistic roadmap planners. We propose a method that is based on the locality-sensitive hashing and show that it can speed up the construction of the roadmap considerably without reducing the quality of the produced roadmap.

Keywords

Motion planningProbabilistic roadmapsLocality-sensitive hashingObstacle avoidancePath planning
Type
Articles
Information
Robotica , Volume 33 , Issue 7 , August 2015 , pp. 1491 - 1506
Copyright
Copyright © Cambridge University Press 2014 

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