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Research on AGV Path Planning Integrating an Improved A* Algorithm and DWA Algorithm

Authors :
Wenpeng Sang
Yaoshun Yue
Kaiwei Zhai
Maohai Lin
Source :
Applied Sciences, Vol 14, Iss 17, p 7551 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

With the rapid development of the economy and the continuous improvement of people’s living standards, the printing and packaging industry plays an increasingly important role in people’s lives. The traditional printing industry is a discrete manufacturing industry, relying on a large amount of manpower and manual operation, low production efficiency, higher labor costs, wasting of resources, and other issues, so the realization of printing factory intelligence to improve the competitiveness of the industry is an important initiative. Automatic guided vehicles (AGVs) are an important part of an intelligent factory, serving the function of automatic transportation of materials and products. To optimize the movement paths of AGVs, enhance safety, and improve transportation efficiency and productivity, this paper proposes an alternative implementation of the A* algorithm. The proposed algorithm improves search efficiency and path smoothness by incorporating the grid obstacle rate and enhancing the heuristic function within the A* algorithm’s evaluation function. This introduces the evaluation subfunction of the nearest distance between the AGV, the known obstacle, and the unknown obstacle in the global path in the dynamic window approach (DWA algorithm), and reduces the interference of obstacles with the AGV in global path planning. Finally, the two improved algorithms are combined into a new fusion algorithm. The experimental results show that the search efficiency of the fusion algorithm significantly improved and the transportation time shortened. The path smoothness significantly improved, and the closest distance to obstacles increased, reducing the risk of collision. It can thus effectively improve the productivity of an intelligent printing factory and enhance its flexibility.

Details

Language :
English
ISSN :
20763417
Volume :
14
Issue :
17
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.1730f64cc3cd42c8bc6a25e0e2b4b7ec
Document Type :
article
Full Text :
https://doi.org/10.3390/app14177551