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Path-Planning for Mobile Robots Using a Novel Variable-Length Differential Evolution Variant

Author

Listed:
  • Alejandro Rodríguez-Molina

    (Tecnológico Nacional de México/IT de Tlalnepantla, Research and Postgraduate Division, Estado de México 54070, Mexico)

  • José Solís-Romero

    (Tecnológico Nacional de México/IT de Tlalnepantla, Research and Postgraduate Division, Estado de México 54070, Mexico)

  • Miguel Gabriel Villarreal-Cervantes

    (Postgraduate Department, Instituto Politécnico Nacional, CIDETEC, Mexico City 07700, Mexico)

  • Omar Serrano-Pérez

    (Postgraduate Department, Instituto Politécnico Nacional, CIDETEC, Mexico City 07700, Mexico)

  • Geovanni Flores-Caballero

    (Postgraduate Department, Instituto Politécnico Nacional, CIDETEC, Mexico City 07700, Mexico)

Abstract

Mobile robots are currently exploited in various applications to enhance efficiency and reduce risks in hard activities for humans. The high autonomy in those systems is strongly related to the path-planning task. The path-planning problem is complex and requires in its formulation the adjustment of path elements that take the mobile robot from a start point to a target one at the lowest cost. Nevertheless, the identity or the number of the path elements to be adjusted is unknown; therefore, the human decision is necessary to determine this information reducing autonomy. Due to the above, this work conceives the path-planning as a Variable-Length-Vector optimization problem (VLV-OP) where both the number of variables (path elements) and their values must be determined. For this, a novel variant of Differential Evolution for Variable-Length-Vector optimization named VLV-DE is proposed to handle the path-planning VLV-OP for mobile robots. VLV-DE uses a population with solution vectors of different sizes adapted through a normalization procedure to allow interactions and determine the alternatives that better fit the problem. The effectiveness of this proposal is shown through the solution of the path-planning problem in complex scenarios. The results are contrasted with the well-known A* and the RRT*-Smart path-planning methods.

Suggested Citation

  • Alejandro Rodríguez-Molina & José Solís-Romero & Miguel Gabriel Villarreal-Cervantes & Omar Serrano-Pérez & Geovanni Flores-Caballero, 2021. "Path-Planning for Mobile Robots Using a Novel Variable-Length Differential Evolution Variant," Mathematics, MDPI, vol. 9(4), pages 1-20, February.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:4:p:357-:d:497446
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    References listed on IDEAS

    as
    1. Jones, D. F. & Mirrazavi, S. K. & Tamiz, M., 2002. "Multi-objective meta-heuristics: An overview of the current state-of-the-art," European Journal of Operational Research, Elsevier, vol. 137(1), pages 1-9, February.
    2. Mahalec, Vladimir & Chen, Yingwu & Liu, Xiaolu & He, Renjie & Sun, Kai, 2015. "Reconfiguration of satellite orbit for cooperative observation using variable-size multi-objective differential evolutionAuthor-Name: Chen, Yingguo," European Journal of Operational Research, Elsevier, vol. 242(1), pages 10-20.
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    Cited by:

    1. Alejandro Rodríguez-Molina & Axel Herroz-Herrera & Mario Aldape-Pérez & Geovanni Flores-Caballero & Jarvin Alberto Antón-Vargas, 2022. "Dynamic Path Planning for the Differential Drive Mobile Robot Based on Online Metaheuristic Optimization," Mathematics, MDPI, vol. 10(21), pages 1-28, October.

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