Incorporating hand gesture recognition in human–robot interaction has the potential to provide a natural way of communication, thus contributing to a more fluid collaboration toward optimizing the efficiency of the application at hand and overcoming possible challenges. A very promising field of interest is agriculture, owing to its complex and dynamic environments. The aim of this study was twofold: (a) to develop a real-time skeleton-based recognition system for five hand gestures using a depth camera and machine learning, and (b) to enable a real-time human–robot interaction framework and test it in different scenarios. For this purpose, six machine learning classifiers were tested, while the Robot Operating System (ROS) software was utilized for “translating” the gestures into five commands to be executed by the robot. Furthermore, the developed system was successfully tested in outdoor experimental sessions that included either one or two persons. In the last case, the robot, based on the recognized gesture, could distinguish which of the two workers required help, follow the “locked” person, stop, return to a target location, or “unlock” them. For the sake of safety, the robot navigated with a preset socially accepted speed while keeping a safe distance in all interactions.

Automated agricultural operations must be planned and organized to reduce risk and failure potential while optimizing productivity and efficiency. However, the diversity of natural outdoor environments and the varied data types and volumes required to represent an agricultural setting comprise critical challenges for the deployment of fully automated agricultural operations. In this regard, this study develops an integrated system for enabling an unmanned aerial vehicle (UAV) supported route planning system for unmanned ground vehicles (UGVs) in the semi-structured environment of orchards. The research focus is on the underpinning planning system components (i.e., world representation or map generation or perception and path planning). In particular, the system comprises a digital platform that receives as input a geotagged depiction of an orchard, which is obtained by a UAV. The pre-processed data define the agri-field’s tracks that are transformed into a grid-based map capturing accessible areas. The grid map is then used to generate a topological path planning solution. Subsequently, the solution is translated into a sequence of coordinates that define the calculated optimal path for the UGV to traverse. The applicability of the developed system was validated in routing scenarios in a walnuts’ orchard using a UGV. The contribution of the proposed system entails noise reduction techniques for the accurate representation of a semi-deterministic agricultural environment for enabling accuracy in the route planning of utilized automated machinery

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