## 4 Experiments and Results  
Several experiments are conducted with a test bench in order to verify the feasibility of the proposed scheme. The system was tested under different test scenarios depending on the drone landing position on the charging station. The receiving coil attached with load was placed at different positions on the charging station and the response of the system was observed. In one test scenario, the system was tested for the misalignment of \(x=100\) mm and \(y=50\) mm, whereas in other test scenarios, the misalignment was \(x=75\) mm, \(y=30\) mm and \(x=50\) mm, \(y=10\) mm. Regardless of the position of the receiving coil, the charging station was able to perfectly align the centroid of the transmitting and receiving coil with an accuracy of 98.8%. Figure 13 shows one of the test scenarios. Initially, we assumed that the receiving coil and four transmitting coils were positioned as shown in Figure 13a. At the start, the \(XY\) table was moved randomly until the receiving coil got closer to one of the four transmitting coils. During this process, the microcontroller kept measuring the four terminal voltages simultaneously. By detecting the voltage drop between all four transmitting coils, the microcontroller activated the hill climbing algorithm and the \(XY\) table was moved to align the nearest transmitting coil with a receiving coil. The distance and position of the nearest transmitting coil were judged on the basis of the voltage drop. The closer the transmitting and receiving coil, the larger the voltage drop would be due to power transmission between the two coils. As shown in Figure 13b,c, after the random movement of the \(XY\) table, transmitting coil 1 was found as the nearest coil to the receiving coil. Then, the hill climbing algorithm was activated by the microcontroller and the \(XY\) table moved to align transmitting coil 1 with the receiving coil. Figure 14 shows the recorded voltage waveforms for the scenario shown in Figure 13. It can be seen clearly that the voltage of transmitting coil 1 decreased, red in the case of Figure 13a, green in the case of Figure 13b, and blue in in the case of Figure 13c. Figure 15 shows the voltage of other transmitting coils under load (wireless power transmission) and no load (normal) condition. Figure 13: (**a**) Initial position of the coils. (**b**) \(XY\) table movement toward nearest transmitting coil. (**c**) Transmitting and receiving coil aligned for wireless power transmission. The system was designed for the transmission power of \(P_{tx}=60\) W. Figure 16 shows the output power at the load resistance \(R_{L}\) under different test scenarios. The misalignment distance between the coils was changed and the load power \(P_{L}\) response was observed. The maximum load power \(P_{L}\) was measured to be 52 W, which gives an efficiency \(\eta\) of 85%. It can be observed in Figure 16 that, as the distance of misalignment between the transmitting and receiving coils increased, the time taken by the charging station (\(XY\) table) to properly align to the nearest possible coil also increased. In the case of a misalignment of \(x=100\) mm, \(y=50\) mm, it took almost 1.85 s to properly align the coils and transfer the power with designed efficiency. For misalignments of \(x=75\) mm, \(y=30\) mm and \(x=50\) mm, \(y=10\) mm, the time to align the coils was almost 1.58 s and 1.5 s, respectively. In a time ranging between 1.5 s and 1.9 s, the misalignment caused by the imperfect drone landing was eliminated and the power transmission was increased to the maximum power. In order to validate the hill climbing algorithm, the data obtained from the practical test bench was used in MATLAB and simulations were carried out to assure the feasibility of the algorithm. Figure 17 shows the trajectory of the \(XY\) position for one transmitting coil in a three-dimensional space. It can be seen that, at the start, there is no voltage drop and the \(XY\) table is moved to a random position.