Research on the Path Planning and Scheduling for Automated Guided Vehicle

DOI

10.2991/978-94-6463-823-3_44How to use a DOI?

Keywords

Automated Guided Vehicle (AGV); Path Planning; A* Algorithm; Q-learning Algorithm; Ant Colony Algorithm; Deep Reinforcement Learning (DRL)

Abstract

Under the background of the rapid development of intelligent logistics and manufacturing, Automated Guided Vehicle (AGV) systems have been widely applied in intelligent warehousing, intelligent logistics, intelligent manufacturing and other fields. AGV has become an important tool for improving production efficiency and reducing costs. Despite their advantages, AGVs still encounter significant challenges in path planning and scheduling, particularly in complex operating environments. AGV's path planning and scheduling are still plagued by issues such as environmental uncertainty, multi-vehicle collaborative conflicts, and real-time requirements. Efficient path planning and scheduling play a crucial role in improving the overall efficiency of AGV systems. This paper reviews the current mainstream AGV path planning methods, and classifies the A* algorithm, Q-learning algorithm, ant colony optimization algorithm and deep reinforcement learning method as four typical algorithms for inductive analysis. It analyzes their principles, advantages and disadvantages, applicable scenarios, etc., and makes comparisons. At the same time, it discusses the application of AGV in actual scenarios and analyzes the challenges it faces in different scenarios. Finally, the paper discusses potential future trends in AGV path planning and scheduling.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Wenxi Liu
PY  - 2025
DA  - 2025/08/31
TI  - Research on the Path Planning and Scheduling for Automated Guided Vehicle
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 445
EP  - 457
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_44
DO  - 10.2991/978-94-6463-823-3_44
ID  - Liu2025
ER  -