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Improved Genetic Algorithm for Solving Robot Path Planning Based on Grid Maps

Author

Listed:
  • Jie Zhu

    (College of Mathematic and Information, China West Normal University, Nanchong 637009, China)

  • Dazhi Pan

    (College of Mathematic and Information, China West Normal University, Nanchong 637009, China
    Sichuan Colleges and Universities Key Laboratory of Optimization Theory and Applications, Nanchong 637009, China)

Abstract

Aiming at some shortcomings of the genetic algorithm to solve the path planning in a global static environment, such as a low efficiency of population initialization, slow convergence speed, and easy-to-fall-into the local optimum, an improved genetic algorithm is proposed to solve the path planning problem. Firstly, the environment model is established by using the grid method; secondly, in order to overcome the difficulty of a low efficiency of population initialization, a population initialization method with directional guidance is proposed; finally, in order to balance the global and local optimization searching and to speed up the solution speed, the proposed non-common point crossover operator, range mutation operator, and simplification operator are used in combination with the one-point crossover operator and one-point mutation operator in the traditional genetic algorithm to obtain an improved genetic algorithm. In the simulation experiment, Experiment 1 verifies the effectiveness of the population initialization method proposed in this paper. The success rates in Map 1, Map 2, Map 3, and Map 4 were 56.3854%, 55.851%, 34.1%, and 24.1514%, respectively, which were higher than the two initialization methods compared. Experiment 2 verifies the effectiveness of the genetic algorithm (IGA) improved in this paper for path planning. In four maps, the path planning is compared with the five algorithms and the shortest distance is achieved in all of them. The two experiments show that the improved genetic algorithm in this paper has advantages in path planning.

Suggested Citation

  • Jie Zhu & Dazhi Pan, 2024. "Improved Genetic Algorithm for Solving Robot Path Planning Based on Grid Maps," Mathematics, MDPI, vol. 12(24), pages 1-17, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:24:p:4017-:d:1549324
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    References listed on IDEAS

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    1. Imen Hassani & Imen Maalej & Chokri Rekik, 2018. "Robot Path Planning with Avoiding Obstacles in Known Environment Using Free Segments and Turning Points Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-13, June.
    2. Daniel Sanin-Villa & Miguel Angel Rodriguez-Cabal & Luis Fernando Grisales-Noreña & Mario Ramirez-Neria & Juan C. Tejada, 2024. "A Comparative Analysis of Metaheuristic Algorithms for Enhanced Parameter Estimation on Inverted Pendulum System Dynamics," Mathematics, MDPI, vol. 12(11), pages 1-17, May.
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