## 6 Conclusions and Future Work  
In conclusion, this work focused on the development of the simulation subcomponent of the robotic network through the implementation of ROS2 and Gazebo technologies. This subcomponent is part of a much more complex system that will be further developed using Kubernetes orchestration technologies. Emergency scenario simulation is essential, as it enables foresight and anticipation of the complex events typically encountered in such situations. For this reason, it is of significant interest to leverage simulation as much as possible, both for training the robotic system and for preparing the human personnel involved. This approach helps reduce the risk of injury to personnel assisting in field missions. Moreover, simulation environments allow for the generation of accurate maps using SLAM techniques, which serve as a foundational layer for navigation, mission planning, and environment-aware decision-making in real deployments. Based on this perspective, the development of such networked robotic systems, equipped with integrated simulation capabilities, is both justified and of great relevance. The scientific literature reflects this trend and presents various configurations that integrate and simulate mobile robots within distributed networks. These configurations require continuous research and optimization, as emergency situations are highly dynamic and subject to constant change. The proposed activities for the further development of the simulation environment focus on two key stages related to the integration of this component into networked architecture. The first stage involves expanding the virtual machine by allocating additional resources, enabling the integration of multiple mobile robots within the simulation environment. Additionally, the authors aim to develop ROS2 microservices to manage the data exchanged via nodes and topics in an automated manner, ensuring its transmission to the Minikube cluster, specifically to the server pod of the robotic network. For this information to be processed by the network server, a secure and encrypted connection is required. To this end, a ROS Bridge will be used to connect the Hyper-V virtual machine to the network server. The authors propose a WSS model secured with TLS encryption and the use of HTTPS requests for data exchange. Furthermore, an alternative communication node based on the gRPC protocol is proposed, intended for mobile robots with higher processing capabilities that can support such libraries. The second stage of development involves designing emergency scenarios using specialized simulation environments and proposing communication strategies. Based on previously acquired maps through SLAM techniques, this stage will focus on the development of Nav2-based obstacle avoidance algorithms and the training of mobile robots in various scenarios involving obstacle circumvention or traversal. Furthermore, this phase will incorporate noise analysis to evaluate the robustness and reliability of navigation strategies under sensor uncertainty and environmental disturbances. An initial network operation model is presented in Table 2, simulating the phases of a search and rescue mission involving three mobile robots, two ground-based and one aerial. Building upon this operational paradigm, a comprehensive suite of microservices will be systematically engineered to encapsulate the core functional components essential to search and rescue missions in emergency contexts. These microservices will be designed to operate in a distributed and modular manner, each responsible for specific capabilities such as sensor data acquisition, environment perception, path planning, obstacle avoidance, inter-robot communication, and decision-making under uncertainty. Writing the original manuscript's draft, M.M. and M.O.T. Software development and simulation, M.M. All authors have read and agreed to the published version of the manuscript. The authors declare that this research was conducted without any external financial support.