Research Summary of Intelligent Optimization Algorithm for Warehouse AGV Path Planning

Ye Liu (), Yanping Du (), Shuihai Dou (), Lizhi Peng () and Xianyang Su ()
Additional contact information
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

A chapter in LISS 2021, 2022, pp 96-110 from Springer

Abstract: 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.

Keywords: Warehouse; AGV; Path planning; Intelligent optimization algorithm (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-16-8656-6_9

Ordering information: This item can be ordered from
http://www.springer.com/9789811686566

DOI: 10.1007/978-981-16-8656-6_9

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().