One of the central challenges of today is to increase the degree of automation of the available systems and equipment in a sustainable way. This paper presents the development of an outdoor mobile robot platform navigation method, including an on-board unit and a mobile base station to support the operation of the ZalaZone Automotive Proving Ground in Zalaegerszeg, Hungary, by precise, automated deployment of traffic cones. Currently, traffic cone handling is a time- and labor-intensive job. Its automation saves a significant amount of human resources. As a first step, the technical requirements of the problem have been defined. Then, the navigation system that met the pre-defined requirements has been implemented. This paper presents the detailed development of this navigation system, including a description of the test phases as well. As part of the development process, the operation of a Global Navigation Satellite System (GNSS) receiver corrected by Real-Time Kinematic (RTK) is presented, supported by a small, low-power mobile base station, followed by a description of the applied hardware and software components, alongside with the alignment and further development of these components to achieve centimeter-accurate positioning. The Network Transport of Radio Technical Commission for Maritime Services (RTCM) via Internet Protocol (NTRIP) server facilitates the communication between the onboard unit and the base station via a 5G network. This work is continued by a detailed description of the autopilot system on the robotic platform, including the tools and software used for this purpose. The calibration process of the navigation system is described as well. Finally, the results and observations gained during the test are summarized and analyzed. These results have shown that the addition of an RTK system has highly increased the accuracy of a general GNSS receiver. In addition, these results underline the crucial role of 5G networks in the case of automated mobile applications.

Protecting agricultural fields, like crops, vineyards, and husbandry areas, has been a difficult challenge since historical times. Classical methods to prevent intrusion are often destructive to wild and domestic animals alike. Even more current nondestructive systems, like camera-based systems are attributed to specific problems related to environmental or technological issues. Furthermore, verifying the effectiveness of installed systems is difficult, as the triggering situations are unmanageable and typically occur unsupervised. This paper presents a complex vision-based intrusion detection system to overcome these problems and further proposes more extensive control and flexibility on the development process. The solution provides a workflow integrating Digital Reality methods into the system development by creating a digital twin of the drone and its surrounding environment in a general-purpose robotic simulator. With this simulation, the triggering events and environmental effects can be easily emulated, such as a wild animal entering the area of interest. The solution also focuses on incorporating new 5G info-communication networks on handling communication between the intrusion detection system and the base station in a distributed manner.

Wireless sensor networks (WSNs), IoT-enabled sensing, and mobile platforms are increasingly used in precision agriculture, but fixed stations cannot fully capture within-field or canopy-level variability. This study developed and greenhouse-tested a low-cost modular tracked robot as a wireless environmental-sensing and telemetry research node for future crop-monitoring applications, rather than as a fully validated autonomous field robot. An open-source tracked chassis was extended with Raspberry Pi edge computing, a Cube Orange autopilot, RTK-capable GNSS, 5G/VPN/MAVLink communication, and BME280, BH1750, MLX90614, RGB camera, and LiDAR-ready sensing. The platform measured 35 × 25 × 40 cm, weighed 6.4 kg, operated from a 12 V supply, and provided about 4 h of runtime under favorable conditions. Sensor data were logged locally and could be transmitted remotely, while telemetry was visualized in QGroundControl. The environmental sensing layer was compared with a calibrated Libelium Smart Agriculture Pro station in a greenhouse using 70 synchronized samples per variable across three sessions. Because the two nodes were placed close to one another but were not strictly co-located, the comparison quantifies operational sensing differences under greenhouse microclimatic gradients rather than pure laboratory sensor error. Regression was retained only as a trend-tracking metric, while method-comparison interpretation was added using bias and Bland–Altman limits of agreement. The pressure channel showed strong trend tracking (R² = 0.992, RMSE = 0.024 hPa), whereas air temperature (R² = 0.756, RMSE = 2.537 °C) and relative humidity (R² = 0.817, RMSE = 5.024%) were suitable mainly for exploratory microclimate mapping and relative trend monitoring unless local calibration is applied. The title, claims and conclusions were therefore narrowed to greenhouse sensing-layer validation and future crop-monitoring deployment.