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Global path planning of mobile robots using a memetic algorithm

Author

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  • Zexuan Zhu
  • Fangxiao Wang
  • Shan He
  • Yiwen Sun

Abstract

In this paper, a memetic algorithm for global path planning (MAGPP) of mobile robots is proposed. MAGPP is a synergy of genetic algorithm (GA) based global path planning and a local path refinement. Particularly, candidate path solutions are represented as GA individuals and evolved with evolutionary operators. In each GA generation, the local path refinement is applied to the GA individuals to rectify and improve the paths encoded. MAGPP is characterised by a flexible path encoding scheme, which is introduced to encode the obstacles bypassed by a path. Both path length and smoothness are considered as fitness evaluation criteria. MAGPP is tested on simulated maps and compared with other counterpart algorithms. The experimental results demonstrate the efficiency of MAGPP and it is shown to obtain better solutions than the other compared algorithms.

Suggested Citation

  • Zexuan Zhu & Fangxiao Wang & Shan He & Yiwen Sun, 2015. "Global path planning of mobile robots using a memetic algorithm," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(11), pages 1982-1993, August.
    • Handle: RePEc:taf:tsysxx:v:46:y:2015:i:11:p:1982-1993
    DOI: 10.1080/00207721.2013.843735
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    Cited by:

    1. Hyunsoo Lee, 2019. "Development of Sustainable Recycling Investment Framework Considering Uncertain Demand and Nonlinear Recycling Cost," Sustainability, MDPI, vol. 11(14), pages 1-18, July.
    2. Sara Abdallaoui & El-Hassane Aglzim & Ahmed Chaibet & Ali Kribèche, 2022. "Thorough Review Analysis of Safe Control of Autonomous Vehicles: Path Planning and Navigation Techniques," Energies, MDPI, vol. 15(4), pages 1-19, February.

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