The increasing use of robotics in industrial automation has led to the need for systems that ensure safety and efficiency in monitoring autonomous guided vehicles (AGVs). This research proposes a visual monitoring system for monitoring the trajectory and behavior of AGVs in industrial environments.

The system utilizes a network of cameras mounted on towers to detect, identify, and track AGVs. The visual data is transmitted to a central server, where the robots’ trajectories are evaluated and compared against predefined ideal paths. The system operates independently of specific hardware or software configurations, offering flexibility in its deployment.

Duckietown was used as the test environment for this system, allowing for controlled experiments with simulated robotic fleets. A prototype of the system demonstrated its capability to track AGVs using Aruco tags and evaluate rectilinear trajectories.

Key aspects and concepts:

• Use of camera towers for visual control of AGVs;
• Transmission of visual data to a central server for trajectory evaluation;
• Compatibility with multiple robot types and operating systems;
• Integration of Aruco tags for robot identification;
• Modular architecture enabling future expansions;
• Testing in Duckietown for controlled evaluation.

This research demonstrates a modular approach to monitoring AGVs using a visual control system tested in the Duckietown platform. Future work will extend the system’s capability to handle more complex trajectories such as turns and arcs, further leveraging Duckietown as a scalable research and testing environment.