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KI - Künstliche Intelligenz

, Volume 27, Issue 3, pp 201–211 | Cite as

Statistic Methods for Path-Planning Algorithms Comparison

  • Pablo Muñoz
  • David F. Barrero
  • María D. R-Moreno
Technical Contribution
  • 247 Downloads

Abstract

The path-planning problem for autonomous mobile robots has been addressed by classical search techniques such as A* or, more recently, Theta* or S-Theta*. However, research usually focuses on reducing the length of the path or the processing time. The common practice in the literature is to report the run-time/length of the algorithm with means and, sometimes, some dispersion measure. However, this practice has several drawbacks, mainly due to the loose of valuable information that this reporting practice involves such as asymmetries in the run-time, or the shape of its distribution. Run-time analysis is a type of empirical tool that studies the time consumed by running an algorithm. This paper is an attempt to bring this tool to the path-planning community. To this end the paper reports an analysis of the run-time of the path-planning algorithms with a variety of problems of different degrees of complexity, indoors, outdoors and Mars surfaces. We conclude that the time required by these algorithms follows a lognormal distribution.

Keywords

Path-planning Run-time analysis Robotics Planetary exploration 

Notes

Acknowledgements

Pablo Muñoz is supported by the European Space Agency (ESA) under the Networking and Partnering Initiative (NPI) Cooperative systems for autonomous exploration missions. This work was partially supported by the Spanish CDTI project colsuvh, leaded by the Ixion Industry and Aerospace company.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Pablo Muñoz
    • 1
  • David F. Barrero
    • 1
  • María D. R-Moreno
    • 1
  1. 1.Departamento de AutomáticaUniversidad de AlcaláMadridSpain

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