Analysis of the Application Scenarios of Different Sensors in Automated Guided Vehicles

Abstract

This study offers a detailed examination of diverse sensor technologies employed in Automated Guided Vehicles (AGVs) across various settings, including warehouses, hospitals, and outdoor environments. The paper investigates the use of a wide range of sensors— Light Detection and Ranging (LiDAR), inertial, vision-based, and magnetic sensors—in AGVs to improve navigation accuracy, reliability, and flexibility. LiDAR generates precise 3D maps and identifies obstacles; inertial sensors, such as accelerometers and gyroscopes, deliver essential data for movement and orientation; vision-based sensors identify landmarks and facilitate predefined path navigation; magnetic sensors guarantee dependable indoor positioning in areas without GPS. Additional focus is given to the increasing trend of sensor fusion, which combines various sensor types to enhance localization, obstacle detection, and path planning. Sensor fusion improves accuracy, safety, and operational efficiency in AGVs. Furthermore, the paper explores the development of more autonomous navigation systems that utilize Artificial Intelligence-based (AI-based) models and advanced algorithms for dynamic decision-making and real-time adaptability. Such advancements propel innovation in robotics, automation, and intelligent transportation systems. As sensor technologies advance, AGVs and other autonomous systems are expected to exhibit enhanced capabilities, wider applications, and more efficient operational processes. This analysis not only underscores the current state of sensor technology in navigation systems but also paves the way for future research and development in this swiftly progressing field.