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An Improved PBIL Algorithm for Path Planning Problem of Mobile Robots

  • Conference paper
Intelligent Data Engineering and Automated Learning – IDEAL 2013 (IDEAL 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8206))

Abstract

The path planning problem of mobile robots is a NP-Hard problem often solved by evolutionary approaches such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO). However, the algorithm’s performance is often influenced heavily by the determination of the operators and the choice of related parameters. In this paper, a permutation code PBIL is proposed to solve the path planning problem. First, a free space model of the mobile robot is constructed by the MAKLINK graph; second, a sub-optimal path is generated by the Dijkstra algorithm; then global optimal path is constructed by the permutation code PBIL based on the sub-optimal path. Simulation results show that the PBIL can get satisfied solutions more simply and efficiently with fewer operators and parameters.

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Author information

Authors and Affiliations

  1. Center of Green & Intelligent Engineering, Shijiazhuang Institute of Railway Technology, Shijiazhuang, 050061, China

    Qingbin Zhang, Fajun Zhou & Hairong Nie

  2. Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Ministry of Education, Dalian, 116622, China

    Qingbin Zhang

  3. Institute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, China

    Manjun Cai

Authors
  1. Qingbin Zhang
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  2. Manjun Cai
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  3. Fajun Zhou
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  4. Hairong Nie
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  2. University of Science and Technology of China, Hefei, China

    Ke Tang

  3. Nanjing University, Nanjing, China

    Yang Gao

  4. Ostfalia University of Applied Sciences, 38302, Wolfenbüttel, Germany

    Frank Klawonn

  5. Kyungpook National University, 702-701, Buk-Gu, Daegu, Korea

    Minho Lee

  6. Nature Inspired Computational and Applications Laboratory, School of Computer Science and Technology,, University of Science and Technology of China, 230027, Hefei, China

    Thomas Weise

  7. University of Science and Technology of China, 230017, Hefei, China

    Bin Li

  8. CERCIA, School of Computer Science, University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK

    Xin Yao

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© 2013 Springer-Verlag Berlin Heidelberg

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Zhang, Q., Cai, M., Zhou, F., Nie, H. (2013). An Improved PBIL Algorithm for Path Planning Problem of Mobile Robots. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_11

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  • DOI: https://doi.org/10.1007/978-3-642-41278-3_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41277-6

  • Online ISBN: 978-3-642-41278-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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