## 5 Experimental Results
### Obstacle Avoidance Flight Experiments
During each experiment, the drone was manually flown toward static obstacles, and the OptiTrack system recorded the time-synchronized position, heading, and relative movement of both the drone and the obstacles. After each test, the recorded data were exported and visualized to reconstruct the drone's trajectory and assess how it responded to obstacle detection and avoidance. To evaluate the system under various conditions, four representative flight experiments were conducted under different obstacle conditions. In each case, the drone  
Figure 28: Example of flight experiment: (**a**) drone moving towards the object on its left side; (**b**) the developed system detects the object as a potential obstacle and orders the drone to move against it; (**c**) drone moving towards the opposite direction to prevent collision. was manually piloted toward the obstacle, and its response was recorded and analyzed. Figures 29-32 summarize the results of these experiments. In each figure, (a) shows the actual experimental scenario, and (b) presents the drone's trajectory, captured using the OptiTrack motion capture system. In the trajectory plots with blue and red colors, point A indicates the starting position, point B marks the endpoint, and red arrows denote the direction of drone motion. Figure 29: The drone was directed toward a flat whiteboard, representing a generic planar obstacle such as a wall. Upon detecting the obstacle denoted with light green line, the drone reversed its direction. The trajectory plot (b) shows the initial forward motion toward the wall, followed by a retreat in the opposite direction after obstacle detection. Figure 30: The drone approached a tool shelf, representing a set of multiple complex obstacles with irregular shapes and varying depths. Where the obstacle is denoted by purple color lines. After detecting the shelf, the drone adjusted its path and moved away to avoid collision. The trajectory (b) reflects this adaptive behavior, showing a change in direction following detection. Figure 29: Obstacle avoidance test with a flat and smooth surface (white board): (**a**) real flight scenario with the obstacle; (**b**) recorded flight trajectory showing avoidance behavior. Figure 31: The drone flew toward a box, placed along its forward path. Upon detecting the box, it adjusted its trajectory to avoid collision and continued its flight. The corresponding plot (b) demonstrates a smooth directional adjustment after obstacle detection. Where the obstacle denoted by a box with purple dashed lines. Figure 32: The drone was flown between two consecutive obstacles labeled obstacle_1 (light blue) and obstacle_2 (light green). As it approached the first obstacle, it generated a repulsive response, altered its course, and then adjusted again to avoid the second. The resulting zigzag trajectory, shown in (b), confirms the drone's ability to handle sequential obstacle avoidance using the proposed system. These visualizations confirm that as soon as an obstacle entered the sensor's detection range, a virtual repulsive force was generated based on proximity. The drone then deviated smoothly from its original path, validating the system's ability to perform real-time collision avoidance under various environmental conditions. This motion tracking approach provided a reliable and objective method for verifying the system's real-time behavior and the performance of the repulsive force-based control  
Figure 31: Obstacle avoidance test with a single major obstacle (right-side box): (a) real flight scenario with the box obstacle; (b) recorded flight trajectory demonstrating avoidance behavior. Figure 32: Obstacle avoidance test with two consecutive obstacles: (a) real flight scenario showing the obstacle setup; (b) recorded flight trajectory illustrating the drone’s path adjustment in response to both obstacles. modification.