## 1 Introduction
Their system uses a combination of sensors, such as ultrasonic and infrared, along with mapping and obstacle-avoidance algorithms to enable robots to patrol predefined indoor spaces. The paper highlights the importance of efficient navigation and real-time environmental awareness. Paper [10] proposes a hybrid vision-based system that integrates optical cameras and depth sensors. Their approach improves target detection and environmental monitoring accuracy, enabling robots to adapt to complex indoor settings. The hybrid system enhances surveillance capabilities by combining the strengths of multiple sensing technologies. Further on, paper [11] focuses on a multi-sensor fusion approach for autonomous surveillance. The system integrates data from LiDAR, ultrasonic sensors, and cameras to improve environmental perception and obstacle detection. By combining sensor inputs, the robots achieve higher navigation precision, ensuring reliable surveillance in dynamic and cluttered environments. Paper [12] introduces a multi-agent system for indoor surveillance, where multiple robots collaborate to optimize task allocation and area coverage. Their study highlights cooperative strategies and communication protocols that allow robots to patrol efficiently while minimizing overlap and maximizing coverage. Collectively, these papers demonstrate innovative approaches to indoor surveillance using autonomous robots. They emphasize the importance of integrating sensors, advanced algorithms, and multi-robot cooperation to address challenges such as navigation, obstacle avoidance, and real-time monitoring in dynamic environments, being a topic of great interest among researchers for different approaches of indoor positioning ssystems [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24]. The present study proposes an autonomous monitoring system using mobile robots and drones to secure strategic objectives. This system integrates advanced sensors and software algorithms to ensure efficient and secure surveillance of predefined areas, meeting modern security requirements. It offers benefits such as reduced operational costs, elimination of repetitive human tasks, and an eco-friendly, energy-efficient infrastructure. Unmanned terrestrial and aerial systems are explored, highlighting their applications in security, emergency response, environmental monitoring, agriculture, and infrastructure protection. These systems leverage artificial intelligence and advanced technologies for monitoring, rapid response, and safeguarding assets against evolving threats. Mobile robots are equipped with technologies such as AI and sensors, enabling autonomous navigation, route planning, mapping, and obstacle avoidance. Various locomotion mechanisms--wheels, tracks, and legs--are utilized, catering to environments such as industrial inspections, search and rescue, and exploration. Aerial mechatronic systems combine mechanics, electronics, and control engineering, emphasizing automation, miniaturization, safety, and predictive maintenance. The study also classifies drones based on design: fixed-wing for long distances, rotary-wing for stability, hybrids, single-rotor for heavy payloads, ornithopters, nano/micro drones for confined spaces, and submersibles for air and underwater use. Applications range from smart city surveillance to ocean monitoring, addressing disaster detection and wide-area oversight. Collaborative systems of aerial and terrestrial robots are also discussed, capable of executing tasks such as strategic surveillance and disaster response. The challenges of using robots in diverse environments are addressed, emphasizing the need for safe human-robot interaction. The proposed system includes mobile robots patrolling indoor areas. Theoretical and experimental tests validated the design, including a mobile flying robot with calculations for thrust, motion, and battery life. Marvelmind beacons ensured accurate indoor localization (\(\pm\)2 cm), tested through various stages, including mobile robots and quadcopters.