## 5 Experimental Results
### Obstacle Avoidance Flight Experiments  
Finally, the experimental drone equipped with the developed system was tested to react to various objects within the experimental laboratory to evaluate its performance in responding to different obstacles. Figure 27 illustrates the experimental drone navigating a complex object setup. As the drone approached a shelf with complex object arrangements (Figure 27a,b), the distance between the drone and the objects decreased below a safety threshold (Figure 27c). In response, the drone began to reduce its speed and moved away from the obstacles (Figure 27d). Ultimately, it maintained a safe distance, preventing collisions by moving away from the obstacles, as shown in Figure 27e,f. Similarly, Figure 28a-c illustrates a case in which the drone was manually operated within the experimental laboratory environment. During its flight, the drone detected an obstacle, and the collision avoidance system continuously measured the distance and generated a virtual repulsive force. As the drone approached the obstacle, the repulsive force gradually increased, causing the drone's speed to reduce to zero. However, due to inertia, the drone continued moving slightly in its previous direction. Once the repulsive force became strong enough to overcome this inertia, it reversed the drone's motion, prompting it to move away from the obstacle. This dynamic response effectively demonstrated the system's ability to prevent a potential collision by automatically adjusting the drone's motion based on real-time distance feedback. Figure 27: Example of drone reacting to a complex obstacle setup: (**a**) the drone starts from a safe zone and begins moving toward the nearby shelf; (**b**) the developed module detects the shelf and identifies it as a potential obstacle, resulting in a speed reduction; (**c**) upon reaching a critical point, the drone begins to move in the opposite direction of the obstacle; (**d**) the drone continues its motion under a relatively high repulsive force; (**e**) it keeps moving in the opposite direction until the safe zone is detected again, although the repulsive force is relatively low at this position, causing the drone to move slowly; (**f**) after re-entering the safe zone, the drone resumes motion based on the manual command. Although real-time control signal visualization was implemented using RViz (as described in Section 5.3), the drone system developed in this study does not include an onboard localization or trajectory visualization module. In other words, the drone lacks autonomous position estimation capability (such as SLAM) and cannot internally generate or display its flight path during operation. While the RViz visualization system [35] supports real-time display of user commands of the drone, repulsive forces, and modified control signals, it requires the embedded microcontroller to be connected to a computer. During flight, no wireless transmitter was used to send data from the drone to a base station for an RViz display in this study. Therefore, the RViz demonstration was conducted by either pushing the drone on a cart or holding it by hand, mimicking realistic flight behavior while maintaining a stable data connection for visualization. To compensate for this limitation and to enable accurate post-flight analysis, an external motion capture system (OptiTrack) was introduced to track and record the drone's position and orientation trajectory in real time [36]. The OptiTrack system uses a network of high-speed infrared cameras to detect retroreflective markers attached to tracked objects. To capture precise motion data,  
* Eight reflective markers were mounted on the drone frame in symmetrical positions to accurately measure its position and orientation in 3D space. * Additionally, eight to ten reflective markers were attached to each obstacle in every experimental setup. This allowed both drone and obstacle positions to be tracked in the same global coordinate frame.