## 3 Results
### Demonstration of the Proposed System in Different Scenarios  
For safety purposes, a step-by-step approach was performed. We initially tested the efficacy of the proposed framework in a case where only one person existed in the same working environment with the [PERSON]. The latter was still in a specific farm location waiting to lock its \"collaborator\", in case the \"fist\" gesture was detected. Taking into account the above results concerning the performance of the investigated ML algorithms in predicting the five hand gestures, only the KNN algorithm was used for brevity. The UGV was able to respond to all the commands given via the hand gestures (Table 1), recorded by the RGB-D camera and recognized by the proposed framework. The fact that the UGV aborted its current task whenever the \"flat\" gesture was recognized is very important considering that a possible failure to comply with this command can provoke harmful consequences [10]. Lastly, a maximum speed of 0.2 m/s and a maximum safe distance of 0.9 m [46], which were programmed in the developed model, were necessary prerequisites in all occasions.  
In the next experimental set, a second person was added in the working region to investigate how the proposed framework could cope with this challenge. It can be concluded that, again, the robot was able to accurately \"lock\" onto the selected participant in spite of the presence of the second person while also obeying the other four commands. During this scenario, although the UGV could obey only the authorized person, it was able to recognize the second person and avoid them similarly to the other existing obstacles of the orchard. Figure 8 illustrates the case where the UGV was in the \"following\" mode for the locked person in the case of a single person (Figure 8a) or when both persons were in the field of view of the RGB-D camera (Figure 8b). Figure 8 was generated by RVIZ [74], which is a 3D visualization tool for the ROS software.  
Figure 7: (**a**) Box plot distribution of the accuracy of the examined machine learning algorithms; (**b**) illustration of the main set of data in a typical box plot.  
Lastly, the proposed synergistic HRI system was tested in a more complex scenario. In this case study, two persons were present in the same working space with the UGV. The first person asked to be locked onto by the UGV (\"fist\" gesture) (Figure 9a), and then requested to be followed (\"victory\" gesture) (Figure 9b) before performing a stop command (\"flat\" gesture) (Figure 9c). Afterward, the locked person asked to be unlocked (\"okay\" gesture) (Figure 9d). A second person in the field of view of the RGB-D camera asked to be locked onto (Figure 9e). By locking onto the second person, the UGV followed the participant (Figure 9f), and was then ordered to go to the target site of the farm (Figure 9g), where it arrived after navigating a few meters (Figure 9h) following the set of gestures presented in Table 1. Some indicative images taken from the pilot study are shown in Figure 9a-h, while a video is also provided in the Supplementary Materials (Video S1).  
Figure 8: Visualization of the real-time field experiments in ROS RVIZ environment showing the local orthogonal coordinate system of some parts of the UGV, as well as the “following” mode, in case of (**a**) a single person and (**b**) a second person appearing in the field of view of the RGB-D camera.  
Figure 9: _Cont._