## 6 Discussion  
After testing the indoor positioning system, a small team of mobile robots was put together to better understand how the system might behave in certain scenarios. Figure 1(a) shows a structure of a tracked robot. For this type of robot, a commercially available chassis model was used. The chosen chassis is equipped with two DC motors with gearboxes, one for each track. In order to navigate the environment, robots have been equipped with four ultrasonic sensors, as depicted in Figure 1(b). The tracked robots are programed using a very simple algorithm that allows them to switch between random movement or heading straight along the longest distance, as indicated by the ultrasonic sensors. Certainly, 3D printing and autonomous robots transform industries by enabling rapid, customizable manufacturing and precise automation [31]. All inter-robot communication is performed by using Wi-Fi communication, using the model included in either the microcontroller or SBCs in their construction. This is also true for the drone included in the system's construction. Figure 21: Error of mobile beacon against linear axis. Figure 22: _Cont._In order to diversify the team, two four-legged robots were included. A commercially available variant is a robotic dog equipped with visual artificial intelligence, featuring 12 degrees of freedom. It is built using six servomotors, aluminum alloy brackets, and a video camera. The robot can perform a variety of human-like actions and move in any direction, controlling its attitude across six dimensions/six degrees of freedom--the robot's position along the X, Y, and Z axes, as well as orientation control, including pitch, roll, and yaw. Equipped with IMU sensors and angle sensors for the servomotors, DOGZILLA provides real-time feedback on its position and joint angles. Using inverse kinematics algorithms, the robot can execute various types of movements. A Raspberry Pi serves as the main controller, complemented by additional configurations such as Lidar and a voice module. By programming in Python, the robot can perform diverse functions, including AI-based visual recognition, navigation using Lidar maps, and voice control. An image of the robot is shown in Figure 23.  
For enhanced efficiency and remote monitoring, the surveillance system has been upgraded by introducing a drone equipped with the same type of beacon for indoor testing. This will assist with positioning the drone in the environment. In this stage, only 2D positioning of the device is needed. Figure 24 shows a view of all the robots to be used. Figure 23: Robotic dogs. Figure 22: (a) Tracked robot. (b) Ultrasonic sensors mounting. Following this stage, after the system is installed and activated, it operates through a series of coordinated steps to ensure efficient monitoring and response. First, the Marvel-mind measurement systems continuously track and read the position of each team member in real time, ensuring precise localization at any moment. Next, the system identifies the location of an event or anomaly using data collected by the sensors integrated into the mobile robots. These sensors are designed to detect unusual activities, environmental changes, or critical conditions in the monitored area. If an anomaly is detected, the system immediately dispatches a drone to the exact coordinates of the mobile robot that initially reported the event. The drone, equipped with advanced sensors and real-time monitoring capabilities, positions itself close to the critical location to gather supplementary information. This allows the system to refine its understanding of the situation, ensuring more accurate assessments. The drone transmits detailed data back to the command systems, including visual, positional, or environmental feedback. With this additional input, the command system analyzes the event and determines the most appropriate course of action based on the nature and severity of the anomaly.