Application of Hybrid Ant Colony-Genetic Algorithm in Warehouse Robot Path Planning

Yue Li, Huiqi Zhu, Shuihai Dou (), Yanping Du (), Zhaohua Wang () and Yuxia Huang
Additional contact information
Yue Li: Beijing Institute of Graphic Communication
Huiqi Zhu: LuDong University
Shuihai Dou: Beijing Institute of Graphic Communication
Yanping Du: Beijing Institute of Graphic Communication
Zhaohua Wang: Beijing Institute of Graphic Communication
Yuxia Huang: Beijing Institute of Graphic Communication

A chapter in LISS 2024, 2025, pp 996-1008 from Springer

Abstract: Abstract As the key intelligent equipment in the warehouse operation system, warehouse robots are indispensable in reducing logistics costs and improving logistics efficiency. Path planning is the core technology of warehouse robots, which directly affects the distance traveled by the robot during operation and is crucial for improving the efficiency of warehouse operation. Aiming at the problem of inefficient robot path planning in warehousing scenarios, this paper proposes a path planning model based on multi-robot paths and shortest as the objective function, with the number of robots and robot energy as the constraints. The model optimizes the robot path through an ant colony algorithm and subsequently uses the optimized path as the initial path for the genetic algorithm to improve the quality of the path. In this paper, the proposed algorithm is analyzed in comparison with a single ant colony algorithm and a single genetic algorithm in a warehousing scenario. Experimental results show that the proposed algorithm possesses better robustness and stability, and significantly improves the path planning efficiency of the warehouse robot.

Keywords: Intelligent warehousing; Ant Colony Optimization; Genetic Algorithm; multi-robot systems; path planning (search for similar items in EconPapers)
Date: 2025
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:978-981-96-9697-0_75

Ordering information: This item can be ordered from
http://www.springer.com/9789819696970

DOI: 10.1007/978-981-96-9697-0_75

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 ().