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Title:Improving AGV path planning efficiency using Genetic Algorithms with Hamming distance-based initialization
Authors:ID Breznikar, Žiga (Author)
ID Gotlih, Janez (Author)
ID Artič, Ž. (Author)
ID Brezočnik, Miran (Author)
Files:.pdf APEM20-3_299-308.pdf (677,22 KB)
MD5: E405F25849B90E7AF8AB4A6A00386B22
 
URL https://apem-journal.org/Archives/2025/Abstract-APEM20-3_299-308.html
 
Language:English
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Abstract:This paper presents a Genetic Algorithm (GA) framework for warehouse navigation as a Travelling Salesman Problem (TSP) variant for Automated Guided Vehicles (AGVs). The warehouse layout is represented as a graph, where pick-up locations serve as terminal nodes. A distance matrix, computed via Breadth-First Search (BFS) enables efficient route evaluation. To promote diversity in the initial population, a Hamming distance-based vectorized initialization strategy is employed, ensuring that the chromosomes are maximally distinct. The GA balances exploration and exploitation by dynamically adjusting the fitness function. Early generations emphasize diversity, while later ones focus on solution refinement, improving convergence and avoiding premature stagnation. Our key contribution demonstrates that the Hamming distance-based approach achieves comparable or better results with significantly fewer chromosomes. This reduces computational cost and runtime, making the method well-suited for real-time AGV routing in warehouses. The framework is adaptable to structured environments and shows strong potential for integration into real-world logistics and robotics applications. Future work will focus on optimizing the algorithm and integrating it into the ROS 2 environment.
Keywords:automated guided vehicles (AGV), warehouse routing, combinatorial optimization, Hamming distance initialization, Robot operating system 2 (ROS 2)
Publication status:Published
Publication version:Version of Record
Submitted for review:06.07.2025
Article acceptance date:24.10.2025
Publication date:28.10.2025
Publisher:University of Maribor
Year of publishing:2025
Number of pages:str. 299-308
Numbering:Vol. 20, no. 3
PID:20.500.12556/DKUM-96064 New window
UDC:007.52:519.6
ISSN on article:1855-6531
COBISS.SI-ID:257509123 New window
DOI:10.14743/apem2025.3.541 New window
Publication date in DKUM:28.11.2025
Views:242
Downloads:15
Metadata:XML DC-XML DC-RDF
Categories:Misc.
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Record is a part of a journal

Title:Advances in production engineering & management
Publisher:Fakulteta za strojništvo, Inštitut za proizvodno strojništvo
ISSN:1855-6531
COBISS.SI-ID:244943360 New window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

Language:Slovenian
Keywords:avtomatsko vodena vozila, usmerjanje v skladišču, genetski algoritmi, kombinatorična optimizacija, inicializacija Hammingove razdalje, Robotski operacijski sistem 2 (ROS 2), genetsko programiranje


Collection

This document is a collection and includes these documents:
  1. Advances in production engineering & management

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