## 3 Related Work
### Fleet Management Systems  
The fleet management of mobile robots has increasingly become a crucial factor in optimizing processes in industrial and logistical environments. With technological advancements, different approaches have been developed to enhance the task distribution, movement coordination, and efficient communication between robots, making systems more effective and adaptable [18].  
Regarding planning and coordination, the use of search and optimization algorithms, combined with machine learning techniques, has enabled a better performance in the dynamic allocation of tasks and collision avoidance. Recent studies show that these approaches lead to a significant increase in the efficiency of multi-robot systems, allowing the devices to respond more quickly and effectively to environmental conditions [19].  
The communication between robots also plays a crucial role in fleet coordination. Instead of relying on complex networks and fixed infrastructures, decentralized systems based on a consensus have been adopted, offering a greater scalability and resilience in fleet management [20]. The integration of mobile robots with smart infrastructures represents another key aspect in the development of these systems. The incorporation of sensors in warehouses and factories enables better environmental awareness, allowing for optimized trajectories and the prevention of congestion. These advancements have proven essential for increasing efficiency and reducing resource consumption in logistical processes [21].  
Regarding the applicability of fleet management systems, the current literature shows that research has been conducted on applying this methodology in different fields. For example, in [22] the authors developed a fleet management system (FMS) to manage logistics tasks performed by mobile robots in hospital environments, through an architecture that integrates several tools to support dynamic route planning and fleet management. [PERSON] et al. discuss digital transformation in the management of Automated Guided Vehicle (AGV) fleets using Digital Twins. To this end, they propose upgrading traditional AGV management systems to incorporate Digital Twin capabilities, highlighting the benefits of this change [23]. Finally, in [24] the authors propose a linear programming-based algorithm for task scheduling and charging for fleets of AMRs that operate continuously. The focus is on minimizing the downtime and battery degradation and integrating preventive maintenance into the planning.  
Unlike many existing fleet management systems, both academic and commercial, the proposed solution was specifically developed to integrate AGVs and AMRs from different brands and manufacturers into a unified interface. In this way, the proposed system enables native interoperability through data abstraction layers and protocol translation, allowing for the continuous monitoring of heterogeneous robots. Furthermore, few existing systems combine, in a single real-time interface, SLAM-based localization and RFID detection--functionalities that are fully integrated into the system presented in this article. One of the most distinctive aspects of the proposed approach is the real-time web interface,which allows for the visual fusion of data into a single map, simplifying supervision and facilitating decision-making.