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<p>Automated Guided Vehicles (AGVs) are crucial for enhancing efficiency in logistics automation. To address path planning inefficiencies in complex warehouse environments, an improved A-star algorithm is proposed. A three-neighborhood search strategy is introduced, incorporating obstacle detection and dynamic direction adjustment to eliminate redundant node traversal. Additionally, an optimized evaluation function is developed by integrating a predictive cost weighting coefficient based on OPEN list. Comparative simulations on large-scale maps with varying obstacle densities demonstrate the algorithm’s superiority and robustness. Results indicate that compared to Dijkstra and traditional A-star, the proposed method reduces search node traversal by up to&#xa0;95.9% and 37.6%, respectively, and computation time by&#xa0;94.8%&#xa0;while maintaining optimal path length. The algorithm exhibits consistent performance advantages across different environmental complexities, validating its scalability and reliability for real-time logistics applications. Furthermore, comparative analysis with state-of-the-art planners (e.g., JPS, Theta*) highlights its practical advantage of balancing high performance with implementation simplicity within the standard A-star framework, ensuring easy integration for real-world AGV systems.</p>

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Research on AGV based on improved A-star algorithm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月4日

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  • Yifeng Guo,
  • Xubin Tong,
  • Limin Huang,
  • Kai Deng,
  • Tao Hu,
  • Jiaxin Ding,
  • Hao Yang,
  • Bin Zhong

摘要

Automated Guided Vehicles (AGVs) are crucial for enhancing efficiency in logistics automation. To address path planning inefficiencies in complex warehouse environments, an improved A-star algorithm is proposed. A three-neighborhood search strategy is introduced, incorporating obstacle detection and dynamic direction adjustment to eliminate redundant node traversal. Additionally, an optimized evaluation function is developed by integrating a predictive cost weighting coefficient based on OPEN list. Comparative simulations on large-scale maps with varying obstacle densities demonstrate the algorithm’s superiority and robustness. Results indicate that compared to Dijkstra and traditional A-star, the proposed method reduces search node traversal by up to 95.9% and 37.6%, respectively, and computation time by 94.8% while maintaining optimal path length. The algorithm exhibits consistent performance advantages across different environmental complexities, validating its scalability and reliability for real-time logistics applications. Furthermore, comparative analysis with state-of-the-art planners (e.g., JPS, Theta*) highlights its practical advantage of balancing high performance with implementation simplicity within the standard A-star framework, ensuring easy integration for real-world AGV systems.