International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

The aim of this work is to implement a wireless communication system for MEMS-based sensors within the framework of Internet of Things (IoT) applications, specifically in the context of Predictive Maintenance (PdM). The focus is placed on developing a functional prototype of a wireless sensor node that enables efficient data acquisition and transmission from commercially available MEMS vibration sensors. The solution leverages an ESP32 microcontroller for data handling and Wi-Fi communication, forming the basis of a scalable wireless sensor network (WSN). The project emphasizes their integration into a wireless system architecture suitable for industrial monitoring scenarios. This approach aims to demonstrate how low-cost MEMS sensors, when combined with IoT technologies, can contribute to accessible and modular condition monitoring solutions aligned with Industry 4.0.

This paper presents a comprehensive concept for the control system of a four-wheeled omnidirectional mobile platform equipped with mecanum wheels, intended for industrial applications under the Industry 4.0 paradigm. The platform is modeled both kinematically and dynamically, with a nonlinear rigid-body formulation that incorporates Coriolis effects and rolling resistance. Particular attention is paid to the challenges arising from strong coupling between translational and rotational motion. To overcome the limitations of conventional control methods in complex and dynamic environments, a reinforcement learning strategy based on an actor–critic architecture is proposed. The agent is trained in a virtual warehouse scenario using simulated lidar data as sensory input, allowing it to learn effective policies for collision-free navigation. The continuous action space is mapped to wheel angular velocities through scaled hyperbolic tangent activations, enabling direct and fine-grained control of the platform. The proposed control system is designed for modularity, robustness, and scalability, making it a promising candidate for autonomous logistics and adaptive robotic applications in smart manufacturing environments.