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Dynamic Path Planning for the Differential Drive Mobile Robot Based on Online Metaheuristic Optimization

Author

Listed:
  • Alejandro Rodríguez-Molina

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

  • Axel Herroz-Herrera

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

  • Mario Aldape-Pérez

    (Instituto Politécnico Nacional, CIDETEC, Computational Intelligence Laboratory (CIL), Ciudad de México 07700, Mexico)

  • Geovanni Flores-Caballero

    (División Tecnológica de Diseño, Universidad Aeronáutica en Querétaro, Querétaro 76278, Mexico)

  • Jarvin Alberto Antón-Vargas

    (Department of Computer Science, Universidad de Sancti Spíritus “José Martí Pérez”, Sancti Spíritus 60100, Provincia de Sancti Spíritus, Cuba)

Abstract

Mobile robots are relevant dynamic systems in recent applications. Path planning is an essential task for these robots since it allows them to move from one location to another safely and at an affordable cost. Path planning has been studied extensively for static scenarios. However, when the scenarios are dynamic, research is limited due to the complexity and high cost of continuously re-planning the robot’s movements to ensure its safety. This paper proposes a new, simple, reliable, and affordable method to plan safe and optimized paths for differential mobile robots in dynamic scenarios. The method is based on the online re-optimization of the static parameters in the state-of-the-art deterministic path planner Bug0. Due to the complexity of the dynamic path planning problem, a metaheuristic optimization approach is adopted. This approach utilizes metaheuristics from evolutionary computation and swarm intelligence to find the Bug0 parameters when the mobile robot is approaching an obstacle. The proposal is tested in simulation, and well-known metaheuristic methods are compared, including Differential Evolution (DE), the Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The dynamic planner based on PSO generates paths with the best performances. In addition, the results of the PSO-based planner are compared with different Bug0 configurations, and the former is shown to be significantly better.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:21:p:3990-:d:955298
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    References listed on IDEAS

    as
    1. Alejandro Rodríguez-Molina & Miguel Gabriel Villarreal-Cervantes & Omar Serrano-Pérez & José Solís-Romero & Ramón Silva-Ortigoza, 2022. "Optimal Tuning of the Speed Control for Brushless DC Motor Based on Chaotic Online Differential Evolution," Mathematics, MDPI, vol. 10(12), pages 1-32, June.
    2. 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.
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    Cited by:

    1. Chia-Hung Wang & Shumeng Chen & Qigen Zhao & Yifan Suo, 2023. "An Efficient End-to-End Obstacle Avoidance Path Planning Algorithm for Intelligent Vehicles Based on Improved Whale Optimization Algorithm," Mathematics, MDPI, vol. 11(8), pages 1-31, April.

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