The technological evolution of Automated Guided Vehicles (AGVs) clearly outlines a developmental trajectory from “mechanical path-following” to “autonomous intelligence.” The core driving force behind this lies in the fundamental transformation of perception, decision-making, and control systems.

First Generation: The “Railway Train” Dependent on Physical Paths
Early AGVs operated strictly along pre-set physical paths. Magnetic tape-guided AGVs functioned like trains running on rails—cost-effective but extremely cumbersome to re-route. QR code navigation improved flexibility somewhat by deploying markers at key nodes for discrete point localization, yet still required extensive floor modifications. The common limitation of these technologies was their lack of flexibility: any reorganization of the production process incurred high costs associated with downtime and path reconfiguration. They served more as an extension of automated conveyor belts, accomplishing the initial shift from “manual carrying” to “vehicle transport.”

Second Generation: The “Intelligent Car” Based on Natural Features
A paradigm shift arrived with Autonomous Mobile Robots (AMRs) centered on laser SLAM (Simultaneous Localization and Mapping) and visual SLAM. By using sensors like LiDAR or cameras to perceive inherent environmental features (e.g., walls, columns), these robots construct maps and localize themselves in real-time, completely liberating themselves from reliance on floor markers. This granted robots basic environmental understanding and obstacle avoidance capabilities. However, perception at this stage largely remained at the level of “identifying obstacles,” with limited deep adaptability to complex commands (e.g., “go to the third rack and fetch the blue box”) or highly dynamic environments (e.g., complex flows with mixed human and vehicle traffic).

Third Generation: Toward Collaborative and Cognitive “Intelligent Agents”
The current technological frontier is focused on endowing AGVs with higher-level “cognitive” and “collaborative” abilities. On one hand, through the integration of 3D vision and depth cameras, robots can now not only avoid obstacles but also recognize shelves, pallets, specific goods, and even human posture, enabling more precise interactive operations. On the other hand, 5G and edge computing make efficient, large-scale fleet coordination possible. Dispatch systems are evolving from centralized command distribution toward distributed optimization based on real-time data, allowing fleets to respond dynamically to global changes like a swarm.

From strict path-following to autonomous map-building, and onward to environmental cognition, the technological core of AGVs has upgraded from simple motion control to a complex system integrating perception, decision-making, and collaboration. This evolution represents not just a liberation from fixed paths but is key to transforming their role from an “isolated tool” to an “intelligent, interconnected node,” laying the cornerstone for building truly flexible and adaptive smart factories.