Advancements and Challenges in Automated Guided Vehicles for Smart Industrial Automation - Mayur Divate*; Siddhi Dhulgand; Aditya Lokhande; Niranjan Labhade; Daisy Mudiar; R.A. Kapgate (International Journal of Advanced Robotics and Automation Technology, 2026)
Automated Guided Vehicles (AGVs) are increasingly central to modern industrial automation, enhancing operational efficiency in manufacturing, warehousing, and logistics. Traditionally reliant on fixed paths using magnetic tapes or wired tracks, AGVs were limited in flexibility. However, recent technological advances have enabled the development of autonomous AGVs equipped with sensor fusion, LiDAR, computer vision, and artificial intelligence (AI). These features support real-time obstacle detection, dynamic path planning, and robust performance in complex environments. Integration with Industry 4.0 technologies—particularly the Internet of Things (IoT) and cloud computing has further expanded AGV capabilities, enabling predictive maintenance and remote monitoring through real-time data. Current research is exploring the use of reinforcement learning and swarm intelligence to enhance multi-AGV coordination, energy efficiency, and adaptive task allocation. Despite these advancements, widespread adoption still faces barriers. High implementation and maintenance costs remain a hurdle for small and medium enterprises (SMEs). AGVs also face challenges in adapting to dynamic environments and ensuring secure, reliable connectivity with cloud-based and legacy systems. Additionally, the shortage of skilled personnel to operate and maintain AGVs continues to limit deployment in various industries. Furthermore, ongoing developments focus on improving navigation accuracy through advanced simultaneous localization and mapping (SLAM) techniques and enhanced sensor integration frameworks. The incorporation of edge computing is reducing latency in decision-making processes, enabling faster response times and improved operational safety. Energy optimization strategies, including intelligent battery management systems and automated charging solutions, are also being investigated to extend operational uptime and reduce overall lifecycle costs. In addition, cybersecurity frameworks are being strengthened to protect AGV networks from potential threats and unauthorized access. As industries move toward fully autonomous smart factories, AGVs are expected to play a pivotal role in enabling flexible manufacturing systems, just-in-time delivery, and seamless human–machine collaboration. Continued research and strategic investment will be essential to overcome existing limitations and unlock the full potential of AGVs in next-generation industrial ecosystems.
Keywords: Automated guided vehicles (AGVs); Industry 4.0; Internet of things (IoT); LIDAR; Predictive Maintenance; artificial intelligence; autonomous navigation; deep reinforcement learning; industrial automation; sensor fusion; swarm intelligence