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A hybrid optimization algorithm combining A* and the dynamic window approach for automated guided vehicle control in static and dynamic environments

Published online by Cambridge University Press:  12 December 2025

Ankur Bhargava Open the ORCID record for Ankur Bhargava [Opens in a new window] ,
Mohammad Suhaib  and
Ajay K. S. Singholi
Ankur Bhargava*
Affiliation:
Department of Mechanical Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia, New Delhi, India
Mohammad Suhaib
Affiliation:
Department of Mechanical Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia, New Delhi, India
Ajay K. S. Singholi
Affiliation:
University School of Automation & Robotics, Guru Gobind Singh Indraprastha University, Delhi, India
*
Corresponding author: Ankur Bhargava; Email: ankurgsb21@gmail.com
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Abstract

This study presents the control of an omnidirectional automated guided vehicle (AGV) with mecanum wheels using a hybrid optimization algorithm that combines a modified A* algorithm and the dynamic window approach (ADWA-HO). The method ensures efficient and precise navigation in both static and dynamic environments. The modified A* algorithm generates global paths, removes redundant nodes, and refines trajectories to improve efficiency and smoothness. At the same time, the dynamic window approach (DWA) enables real-time local path planning and obstacle avoidance. By evaluating the AGV’s motion commands in real time, ADWA-HO selects optimal velocity commands within a dynamically updated window, thereby reducing route conflicts and ensuring stable movement. Compared with benchmark methods including dynamic A* (D*), artificial potential field (APF), DWA, probabilistic roadmap (PRM) & rapidly exploring random tree (RRT) fusion, and PRM & DWA fusion, the proposed ADWA-HO achieves improvements in average path length of 28.10%, 22.95%, 21.16%, 17.35%, and 10.71% and in average motion time of 23.48%, 17.85%, 15.47%, 11.86%, and 7.53% on both Map 1 and Map 2, respectively. The difference between simulation and real-world experiments is limited to 5.35% in path length and 4.38% in motion time, confirming the method’s practical reliability. Furthermore, the algorithm achieves lower standard deviation in both metrics, indicating higher consistency of performance. This work also introduces a novel map-building strategy based on geometric and semantic data modules, which enhances the adaptability of real-world AGV deployment.

Keywords

automated guided vehicles (AGVs)modified A* algorithmdynamic window approach (DWA)path planningobstacle avoidance

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Type
Research Article
Information
Robotica , First View , pp. 1 - 63
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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