A systematic study on advanced intelligent techniques in automated guided vehicles control for Industry 5.0 perspective

Abstract

Automated guided vehicles (AGVs) play a crucial role in streamlining operations within manufacturing plants, warehouses, and distribution centers. As the industrial landscape transitions from Industry 4.0 to Industry 5.0, there is an increasing demand for more advanced, intelligent control systems to support the evolving complexity of these environments. This paper presents a systematic study of the advanced intelligent techniques driving the autonomous behavior of AGVs, with a focus on their application in Industry 5.0. The review categorizes intelligent techniques—such as machine learning, soft computing, game theory, and other intelligent algorithms—used for enhancing AGV functionalities including path planning, task scheduling, and energy-efficient operation. Emphasis is placed on how these approaches enable adaptability and smarter decision-making in dynamic industrial settings. The study concludes with key insights and outlines future research directions to further enhance AGV performance in the context of Industry 5.0.