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Advances in Self-Organizing Maps and Learning Vector Quantization pp 143–153Cite as

On Self-Organizing Map and Rapidly-Exploring Random Graph in Multi-Goal Planning

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 428))

Abstract

This paper reports on ongoing work towards an extension of the self-organizing maps for the traveling salesman problem to more challenging problems of multi-goal trajectory planning for complex robots with a high-dimensional configuration space. The main challenge of this problem is that the distance function needed to find a sequence of the visits to the goals is not known a priori and it is not easy to compute. To address this challenge, we propose to utilize the unsupervised learning in a trade-off between the exploration of the distance function and exploitation of its current model. The proposed approach is based on steering the sampling process in a randomized sampling-based motion planning technique to create a suitable motion planning roadmap, which represents the required distance function. The presented results shows the proposed approach quickly provides an admissible solution, which may be further improved by additional samples of the configuration space.

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Acknowledgments

The presented work is supported by the Czech Science Foundation (GAČR) under research project No. 13-18316P. Computational resources were provided by the MetaCentrum under the program LM2010005 and the CERIT-SC under the program Centre CERIT Scientific Cloud, part of the Operational Program Research and Development for Innovations, Reg. No. CZ.1.05/3.2.00/08.0144.

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Authors and Affiliations

  1. Czech Technical University in Prague, Department of Computer Science, Technická 2, 166 27, Prague, Czech Republic

    Jan Faigl

Authors
  1. Jan Faigl
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Correspondence to Jan Faigl .

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Editors and Affiliations

  1. Department of Statistics, Rice University, Houston, Texas, USA

    Erzsébet Merényi

  2. Department of Electrical and Computer En, Air Force Institute of Technology, Wright-Patterson AFB, Ohio, USA

    Michael J. Mendenhall

  3. Applied Physics, Rice University, Houston, Texas, USA

    Patrick O'Driscoll

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Faigl, J. (2016). On Self-Organizing Map and Rapidly-Exploring Random Graph in Multi-Goal Planning. In: Merényi, E., Mendenhall, M., O'Driscoll, P. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 428. Springer, Cham. https://doi.org/10.1007/978-3-319-28518-4_12

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  • DOI: https://doi.org/10.1007/978-3-319-28518-4_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28517-7

  • Online ISBN: 978-3-319-28518-4

  • eBook Packages: EngineeringEngineering (R0)

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