Coverage path planning (CPP) and path motion stabilization (PMS) play crucial roles in a variety of robotics applications, with a particular focus on their significance in precision agriculture. CPP determines optimal paths for covering specified target space, while PMS ensures precise movement along these paths. The paper proposes the advancement of CPP and PMS methodologies through the application of modern metaheuristic algorithms and symbolic regression methods. This study addresses a novel problem where the utilization of these methodologies is pursued to develop a universal stabilization system for moving an object along a trajectory, ensuring complete coverage of target space. Metaheuristic algorithms, such as the Grey Wolf Optimizer used in this study, can effectively solve CPP problems by decomposing the target space into subregions and searching for the optimal trajectory to efficiently explore these subregions. PMS utilizes symbolic regression methods and machine learning control techniques to develop stabilization systems. The control function of the stabilization system is considered as a function of the deviation from the equilibrium state. Consequently, it becomes feasible to explicitly derive a universal stabilization system for subsequent integration into the control object. The proposed CPP and PMS methodologies are evaluated through a computational experiment centered on crop monitoring using a quadcopter, thereby validating their effectiveness in practical applications.

In order to obtain optimal control of a real object, it is necessary to know the precise mathematical model of this control object. In the present study an artificial neural network is used for building a mathematical model of the control object. First, some forms of control are defined, and with the help of these controls, the control object is modeled. The obtained values of the controls and the space state vector are stored to create a training sample. The artificial neural network is then trained on this training set. For a trained neural network, a set of optimal control problems is solved. The optimal control obtained by the trained artificial neural network is applied to a real control object. The accuracy of the approximation of the mathematical model by an artificial neural network can be estimated based on the proximity of the functional values of the control object and the trained neural network.

Commercial competence to satisfy customer demands requires companies to provide the necessary skilled engineering staff and fight against time to achieve that. Learning Factories (LF) aim to provide training and education so that the manufacturing facility can respond to its production aims. However, the implementation of learning factories concept is adopting different styles especially with the rise of Industry 4.0. On the other hand, sustainability concerns are becoming more serious and need to be fulfilled due to the recent climate changes. In this paper, a literature survey of the recent developments in this field is conducted with regards to energy sustainability. In particular, an analysis of the pedagogical aspect in terms of the applied learning theories, curriculum design and learning environment is explored. Further, some criticisms based on the analysis are put forward, and some research topics are suggested in relation to both the pedagogical and technical aspects.

Nowadays industrial companies need more agile and adaptable production systems to manufacture customized products faster and with a high level of process optimization. This agility in production requires flexible automation systems. One key capability such systems need to exhibit is effective connectivity and interoperability with different vendor-specific software and hardware tools, which often presents difficulties. This paper shares results and research experience in applying OPC UA technology, to establish communication between Programmable Logic Controller (PLC) based control systems and virtual engineering tool (Onevue) to enable interactive support and data collection during production. This paper provides an overview of the OPC UA client development for the Onevue. Based on the case study provided in the paper, results of the OPC UA client-server communication is reported.