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A self-adaptive safe A* algorithm for AGV in large-scale storage environment

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Abstract

This paper presents a safe A* algorithm for the path planning of automated guided vehicles (AGVs) operating in storage environments. Firstly, to overcome the problems of great collision risk and low search efficiency in the path produced by traditional A* algorithm, a new evaluation function is designed by introducing repulsive term and assigning dynamic adjustment weights to heuristic items. Secondly, a Floyd deletion algorithm based on the safe distance is proposed to remove redundant path points for reducing the path length. Moreover, the algorithm replaces the broken line segments at the turns with a cubic B-spline to ensure the smoothness of turning points. The simulation applied to different scenarios and different specifications showed that, compared with other three typical path planning algorithms, the path planned by the proposed safe A* algorithm always keeps a safe distance from the obstacle and the path length is reduced by 1.95\(\%\), while the planning time is reduced by 25.03\(\%\) and the number of turning point is reduced by 78.07\(\%\) on average.

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Funding

This research mainly supported by Natural Science Foundation of Shaanxi Province of China (NO. 2021JM-363). In addition, this paper is partly supported by Local Projects Guided by the Central Government (NO. XZ202301YD0003C) and Key Laboratory Project of Shaanxi Provincial Department of Education (NO.20JS065).

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Author notes

  1. Qiyu Zhang, Zhifeng Bai and Guifang Guo have contributed equally to this work.

Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xian, 710311, Shanxi, China

    Xiaolan Wu, Qiyu Zhang & Zhifeng Bai

  2. School of Information Engineering, Xizang Minzu University, Xianyang, 712082, Shanxi, China

    Guifang Guo

Authors
  1. Xiaolan Wu
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  2. Qiyu Zhang
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  3. Zhifeng Bai
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  4. Guifang Guo
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Contributions

All authors contributed to the study conception and design. Zhifeng Bai contributed to the conception of the study; Xiaolan Wu contributed significantly to analysis and manuscript preparation; Qiyu Zhang performed the data analyses and wrote the manuscript; Guifang Guo helped perform the analysis with constructive discussions.

Corresponding author

Correspondence to Zhifeng Bai.

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Wu, X., Zhang, Q., Bai, Z. et al. A self-adaptive safe A* algorithm for AGV in large-scale storage environment. Intel Serv Robotics 17, 221–235 (2024). https://doi.org/10.1007/s11370-023-00494-2

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  • DOI: https://doi.org/10.1007/s11370-023-00494-2

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