## 3 Results  
The results related to the proximity measurements on the various tissue samples, obtained by experimental steps described in the previous section, are shown in this section. Due to the lack of synchronization between the start signal of the robotic arm and the Arduino board containing the proximity sensor, it was necessary to proceed with a manual evaluation of the first useful point at which the sensor returns a non-zero proximity value. To do this, the tissue sample was positioned on the plate as shown in section III, and the robotic arm was moved manually to detect the first significant data by acting on the coordinates of the individual joints. As shown in Figure 14, the sensor returned a series of null proximity values when it was too close to the sample. Starting from the fixed initial position P1 (position of the robotic arm closest to the sample), the sensor, positioned at the end-effector of the robot, was slowly moved away from the tissue sample by taking steps equal to 0.5 mm. In this way, it was possible to evaluate and display on the real-time graph relating to the sensor the first non-zero proximity value detected, and the corresponding  
Figure 11: Positioning of the sensing device prototype on the PSM tool of the dVRK system. Figure 12: Three frames concerning the phases of the performed transfer task, taking the ball from the workspace and positioning it about 8 cm from the tissue. Figure 13: Positioning of the ball on the object placed on the work surface. distance of the sensor from the sample. The real distance was known to the user who programmed the robotic arm. By repeating this experiment for all three types of tissues, it was possible to precisely define the first useful proximity value for each sample. Regarding the liver, the first useful proximity value was detected at two centimeters from the sample, while for the stomach and intestine, the first non-zero proximity value was shown at 3.5 cm. The characteristic curves for each tissue are shown in Figure 14. In this way, it was possible to relate the real distance due to the movement of the robotic arm with the proximity values derived from the sensor. As previously mentioned, the proximity measurement by the sensor took place thanks to the detection of the IR energy reflected by the tissue on the photodiode. Consequently, since this measurement was influenced by the reflectivity of the sample surface, it was reasonable to think that tissues of different colors could give a different characterization curve. This concept is clearly shown in Figure 14d, where the stomach and intestines with a very similar color returned the first non-zero value at the same distance. The liver characterization curve, on the other hand, had a different trend than that of the stomach and gut. It should be mentioned that for all three types of tissues, it was possible to note that as the robotic arm moved away from the sample, the readings of the proximity sensor were accompanied by a part of noise. To ensure the repeatability of the experiment, four different tests were carried out for each of the three tissues examined. For each organ, as shown in Figures 15-17, the sensor characterization curves were practically the same for the four tests. This allows us to conclude that it is possible to extend this argument to any tissue sample, and therefore, to be able to consider the curves obtained as being representative for the tissue to which they refer in relation to the sensor used. Figure 14: Characteristic curves relating to the proximity of the three tissues analyzed. (**a**) Stomach. (**b**) Gut. (**c**) Liver. (**d**) Superposition of the curves of the intestine and stomach. During the following analysis, the stomach curve was taken as a reference, but similar results can also be easily extended to the other two tissues. By analyzing the curve, an initial flat area that describes the locations where the sample was too close to the sensor, and therefore, was unable to return proximity values, was pointed out.