Applications of safety-related control systems demand reliable and credible inputs from physical sensors, therefore there is a need to extend their capabilities to provide a validated input with high availability. Our main idea is to insert virtual sensors between physical sensors and the control system’s logic. The created solution can validate the values of real sensors and with the use of multiple virtual sensors we can achieve high availability in addition, therefore our solution is entitled as a virtual redundant sensor. It works by the digital twin’s concept and uses fusion function to calculate validated results. The fusion function is used to transform the measured values from the physical sensors according to designed numerical models. The selection of a numerical model with assigned fusion functions can be performed via the WEB-based graphical user interface. Proposal of the numerical model is created and validated on the experimental workplace with emulation of physical sensors and MQTT integration (smart IoT sensors). The results of testing have shown that our solution can be applied to validate the values of physical sensors. Proposed fusion functions calculated results according to the selected model in all cases, while non-standard cases were handled according to our definition. In addition, the high availability concept with a group of two virtual sensors has proven fast recovery and availability of results for safety-related applications as well.

This article proposes and completely describes a modification of the Hybrid A* method used for navigation of a non-holonomic mobile wheeled robot. Our modification allows straightforward multi-criterial adjustment of the algorithm according to the desired behavior considering not only traveled distance but also time, changing of direction, stopping, going backwards while avoiding obstacles. The obstacle avoidance algorithm evaluates the danger of collision smoothly (not-binarily) using danger fields. Such behavior reflects human-like sensing of danger—the closer to the obstacle the robot is, the higher is the danger of collision. A modified uniform state expansion method has been used to cover the state space of the robot more uniformly providing the possibility of precise near-target navigation. A greed factor has been introduced to decrease the computational time and improve the real-time performance of the algorithm.

Competitiveness in industry requires smooth, efficient, and high-quality operation. For some industrial applications or process control and monitoring applications, it is necessary to achieve high availability and reliability because, for example, the failure of availability in industrial production can have serious consequences for the operation and profitability of the company, as well as for the safety of employees and the surrounding environment. At present, many new technologies that use data obtained from various sensors for evaluation or decision-making require the minimization of data processing latency to meet the needs of real-time applications. Cloud/Fog and Edge computing technologies have been proposed to overcome latency issues and to increase computing power. However, industrial applications also require the high availability and reliability of devices and systems. The potential malfunction of Edge devices can cause a failure of applications, and the unavailability of Edge computing results can have a significant impact on manufacturing processes. Therefore, our article deals with the creation and validation of an enhanced Edge device model, which in contrast to the current solutions, is aimed not only at the integration of various sensors within manufacturing solutions, but also brings the required redundancy to enable the high availability of Edge devices. In the model, we use Edge computing, which performs the recording of sensed data from various types of sensors, synchronizes them, and makes them available for decision making by applications in the Cloud. We focus on creating a suitable Edge device model that works with the redundancy, by using either mirroring or duplexing via a secondary Edge device. This enables high Edge device availability and rapid system recovery in the event of a failure of the primary Edge device. The created model of high availability is based on the mirroring and duplexing of the Edge devices, which support two protocols: OPC UA and MQTT. The models were implemented in the Node-Red software, tested, and subsequently validated and compared to confirm the required recovery time and 100% redundancy of the Edge device. In the contrast to the currently available Edge solutions, our proposed extended model based on Edge mirroring is able to address most of the critical cases, where fast recovery is required, and no adjustments are needed for critical applications. The maturity level of Edge high availability can be further extended by applying Edge duplexing for process control.