## 2 Establishing the Monitoring Scenario and Imposed Restrictions  
In the new paradigm of large-area geographic and interior space monitoring, using autonomous mobile robots amid current security challenges presents an area where current studies do not necessarily offer a clear solution [25]. A critical aspect to consider is ensuring cybersecurity for these systems. Autonomous robots rely on wireless communications and sensors, making them vulnerable to cyber attacks such as data interception, spoofing, and malware [26; 27]. Successful attacks could compromise robot functionality, leading to system failures or even physical harm. Security assurance requires encryption, secure communication protocols, and continuous monitoring against threats. Additionally, AI-driven robots must be trained to autonomously recognize and mitigate potential cybersecurity risks [28; 29; 30]. As robotics evolves, proactive cybersecurity measures will be essential to maintain trust and safety in these systems. Beyond cybersecurity, it is crucial to recognize that the diverse configurations of geographical areas or building interiors navigated by these robots may require custom configurations for each scenario. Additionally, special attention must be given to the coexistence of humans and robots, ensuring that each operates without disrupting the other. Effective communication, trust, and clear safety protocols are vital for seamless interaction. Indoor surveillance with robots is an evolving technology that uses autonomous robots equipped with cameras, sensors, and advanced algorithms to monitor and secure indoor spaces. These robots are designed to autonomously navigate environments such as offices, warehouses, hospitals, or homes, providing real-time surveillance without requiring human operators. In this system, a team of autonomous robots will patrol an indoor building area. Robot trajectories will be both random and predefined based on specific rules developed for each situation. The system will follow key stages, obtaining relevant data on events within the robots' operational area. The algorithm will allow a variable number of mobile robots to patrol an area, equipped with a minimal set of sensors necessary only for movement, obstacle avoidance, and basic detection of disturbances in the area of interest. In this case, the robots follow either a predetermined or random trajectory, while a flying robot, designed to serve the entire system and equipped with high-performance, costly sensors, will intervene only in the event of a detected incident. Robots patrol according to a predefined schedule until one detects a potential threat, at which point it sends an alarm signal to the drone to initiate an intervention, as depicted schematically in Figure 1. By initiating an automated intervention sequence, the drone enters an alert state, powers up its engines, and prepares to take off and inspect the area of interest. It quickly heads to the location indicated by the mobile robot, covering the distance in a much shorter time than another mobile robot or human intervention agent could. Simultaneously, all its equipment (sensors and camera) is activated. The drone begins capturing images and videos from the air using its high-resolution cameras. Additionally, modern drones can utilize thermal cameras to identify heat sources at night or in low-visibility conditions, an option that could be implemented in future projects or viewed as an enhancement to the current project. The data captured by the drone are transmitted in real time to the control center, where human operators or AI algorithms assess whether a genuine threat exists. The drone maintains aerial surveillance, providing additional information for mobile robots or other units available for response. Based on observation results, the drone will either continue to monitor the area or collaborate with the mobile robot to track the  
Figure 1: Basic algorithm—steps. target.