## 4 Discussion and Main Conclusions  
In the present study, a very crucial aspect of HRI was investigated to meet the needs of enabling natural communication. In this context, we proposed a nonverbal interaction via real-time hand gesture recognition captured by a depth camera. This combination has the potential to overcome common problems associated with illumination changes and complex backgrounds, providing reliable results [13]. In the direction of additionally facing the challenges of dynamic agricultural environments, a skeleton-based recognition methodology was designed on the basis of ML. The accuracy of six ML algorithms (LR, LDA, KNN, CART, NB, and SVM) was tested in this study on five certain hand gestures representing different actions that the UGV, equipped with an RGB-D camera and other sensors, could carry out on the basis of the detected commands. Among the examined classifiers, KNN, LDA, and SVM demonstrated the best performance. Firstly, the UGV must detect the assigned hand gesture to \"lock\" onto the person with whom it is going to collaborate in a relatively crowded environment. To that end, a distinct hand gesture is used, which represents the first class of the ML implementation. Once the person is located, the UGV can perform four other actions in order to assist them, namely, following its collaborator by keeping the preset safe distance and speed, returning to the destination site, which may be the cargo vehicle zone similarly to [46] or any other site depending on the application, or stopping its current task at any time, once the corresponding hand gesture is detected. In summary, the present integrated hand gesture recognition framework incorporated and combined several kinds of ICT. In particular, the data acquisition was performed using a ZED 2 depth camera (Stereolabs Inc., San Francisco, CA, USA) mounted on a UGV (Thorvald, SAGA Robotics SA, Oslo, Norway). The open-source MediaPipe framework [56] was used for object detection and tracking in conjunction with an algorithm developed for the present study. For the preprocessing of the data, the SMOTE algorithm [57] was implemented. Furthermore, the UGV used in this study was equipped with a variety of sensors including (i) the depth camera mentioned above, (ii) a 3D laser scanner (Velodyne Lidar Inc., San Jose, CA, USA), (iii) an RTK GPS (S850 GNSS Receiver, Stonex Inc., Concord, NH, USA), and (iv) IMUs (UM7 IMU, RedShift Labs, Studfield, Victoria, Australia). To combine these technologies, an algorithm was developed within the ROS framework using Python, whose operational flow is described in Figure 5. Moreover, different tf publishers were developed within the tf ROS package [70], while the ROS navigation stack package [72] was used for safe navigation. To meet the increased requirements necessary for the detection ML model, a Jetson TX2 Module [64] with CUDA support was embedded in the robotic platform. Overall, the challenge of properly classifying the five hand gestures was successfully fulfilled, while the preliminary results on real conditions are encouraging enough. In fact,  
Figure 9: Snapshots from experimental demonstration session in an orchard depicting (**a**) the locking onto of the first person by the UGV, as well as the UGV (**b**) following the locked participant, (**c**) stopping, and (**d**) unlocking this participant. Next, the UGV (**e**) locked onto the second participant, (**f**) followed him, (**g**) was ordered to go to the target location, and (**h**) reached this location. three different scenarios took place considering either one or two persons. In all cases, the KNN classifier was used for the sake of brevity. It was demonstrated that a fluid HRI in agriculture can be successful in sharing the same working space with the robot being navigated within a predetermined safe range of speed and distance.