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Research Summary of Intelligent Optimization Algorithm for Warehouse AGV Path Planning

In: Liss 2021

Author

Listed:
  • Ye Liu

    (Beijing Institute of Graphic Communication)

  • Yanping Du

    (Beijing Institute of Graphic Communication)

  • Shuihai Dou

    (Beijing Institute of Graphic Communication)

  • Lizhi Peng

    (Beijing Institute of Graphic Communication)

  • Xianyang Su

    (Beijing Institute of Graphic Communication)

Abstract

Automated Guided Vehicle (AGV) path planning is the core technology of warehouse AGV. Reasonable path planning is helpful to maximize the benefits of warehouse space and time. Scholars at home and abroad have already made extensive and in-depth research on warehouse AGV path planning, and have achieved fruitful research results. In this paper, the models and environmental modeling methods of warehouse AGV path planning are summarized. It turned out that the cell method is intuitive and easy to model, the geometric method is safe, but difficult to update, and the artificial potential field method is easy to solve, but easy to fall into local optimum. The optimization methods of genetic algorithm, ant colony algorithm and particle swarm optimization algorithm in AGV path planning are emphatically summarized. It is found that genetic algorithm is suitable for complex and highly nonlinear path planning problems, ant colony algorithm is suitable for discrete path planning problems, and particle swarm algorithm is suitable for real number path planning problems. The research summary of this paper provides reference value for the research of intelligent optimization algorithm of AGV path planning and new ideas for broadening the application field of AGV path planning.

Suggested Citation

  • Ye Liu & Yanping Du & Shuihai Dou & Lizhi Peng & Xianyang Su, 2022. "Research Summary of Intelligent Optimization Algorithm for Warehouse AGV Path Planning," Lecture Notes in Operations Research, in: Xianliang Shi & Gábor Bohács & Yixuan Ma & Daqing Gong & Xiaopu Shang (ed.), Liss 2021, pages 96-110, Springer.
    • Handle: RePEc:spr:lnopch:978-981-16-8656-6_9
    DOI: 10.1007/978-981-16-8656-6_9
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