Path Planning Algorithm for Heavy-duty AGV Based on A* Algorithm Fused with DWA

In a complex working environment, it is particularly important to automatically plan a safe and real-time path when heavy-duty AGV are used for automated transportation. The traditional A* algorithm has shortcomings in terms of low search efficiency, many redundant points and obstacle avoidance. Meanwhile, the traditional dynamic window method (DWA) is prone to falling into a local optimal solution. Therefore, this paper proposes a fusion algorithm. First, the A* algorithm is improved by using a two-way search, introducing dynamic weights in the heuristic function, and deleting redundant points. Then the dynamic obstacle avoidance strategy is introduced in the evaluation function of the DWA algorithm, and the improved A* algorithm is fused with the improved DWA algorithm. Finally, the superiority of the improved fusion algorithm is verified through simulation experiment comparison and real experiment.

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Publication Details

Published
2024-09-20
DOI
https://doi.org/10.1109/icmra62519.2024.10809041
Citations
1
Primary Topic
Advanced Manufacturing and Logistics Optimization
Type
article
Field-Weighted Citation Impact
0.35

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Path Planning Algorithm for Heavy-duty AGV Based on A* Algorithm Fused with DWA

Lei Zhai, Hai He, Wenxia Xu, Shengdong Zhu et al.
1 citations
Advanced Manufacturing and Logistics Optimization
0.35
article

Path Planning Algorithm for Heavy-duty AGV Based on A* Algorithm Fused with DWA

Lei Zhai, Hai He, Wenxia Xu, Shengdong Zhu, Baocheng Yu, Zhiqiang Tang
article en
1 citations

Abstract

In a complex working environment, it is particularly important to automatically plan a safe and real-time path when heavy-duty AGV are used for automated transportation. The traditional A* algorithm has shortcomings in terms of low search efficiency, many redundant points and obstacle avoidance. Meanwhile, the traditional dynamic window method (DWA) is prone to falling into a local optimal solution. Therefore, this paper proposes a fusion algorithm. First, the A* algorithm is improved by using a two-way search, introducing dynamic weights in the heuristic function, and deleting redundant points. Then the dynamic obstacle avoidance strategy is introduced in the evaluation function of the DWA algorithm, and the improved A* algorithm is fused with the improved DWA algorithm. Finally, the superiority of the improved fusion algorithm is verified through simulation experiment comparison and real experiment.

Wuhan Institute of Technology (CN)
Research and Development
Openalex Percentile: Top 32%
Advanced Manufacturing and Logistics Optimization
0.35
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