Ensuring reliable communication in remote or disaster-affected areas is a technical challenge due to unplanned deployment and mobilization, meaning placement difficulties and high operation costs of conventional telecommunications infrastructures. To address this problem, unmanned aerial vehicles (UAVs) have emerged as an excellent alternative to provide quick connectivity in remote or disaster-affected regions at a reasonable cost. However, the limited battery autonomy of UAVs restricts their flight service time. This paper proposes a communication system based on a tethered UAV (T-UAV) capable of continuous operation through a wired power network connected to a ground station. The communications system is based on low-cost devices, such as Raspberry Pi platforms, and offers wireless IP telephony services, providing high-quality and reliable communication. Experimental tests assessed power consumption, UAV stability, and data transmission performance. Our results prove that the T-UAV, based on a quadcopter drone, operates stably at 16 V and 20 A, ensuring consistent VoIP communications at a height of 10 m with low latency. These experimental findings underscore the potential of T-UAVs as cost-effective alternatives for extending or providing communication networks in remote regions, emergency scenarios, or underserved areas.

This paper presents an experimental comparison between model-based and model-free trajectory-tracking control strategies applied to an omnidirectional mobile robot platform. Classical model-based controllers, including Proportional-Integral-Derivative (PID), Generic Model Control (GMC), and Sliding Mode Control (SMC), are evaluated against a Model-Free Control (MFC) strategy based on an intelligent Proportional–Integral–Derivative (iPID) regulator. An empirical integrator-with-delay model is identified from experimental data and used to design model-based controllers. All strategies were implemented on the Festo Robotino platform under comparable operating conditions and evaluated using circular, lemniscate, and square trajectories. Controller performance is assessed using the Integral of Squared Error (ISE), the settling time ($T_s$), and the control effort quantified by the Integral of Squared Control Output (ISCO). Experimental results show that the model-free controller provides the best overall tracking performance, achieving tracking error reductions of approximately $25\%$ to $90\%$ compared with the evaluated model-based controllers, while maintaining a competitive control effort. The study provides a unified experimental benchmark comparing model-based and model-free control paradigms on the Robotino platform, highlighting the practical advantages of model-free control for robotic systems affected by uncertainties and nonlinearities.

One of the critical challenges in mobile robotics is obstacle avoidance, ensuring safe navigation in dynamic environments. In this sense, this work presents a comparative study of two intelligent control approaches for mobile robot obstacle avoidance based on a fuzzy architecture. The first approach is a neuro-fuzzy interface that combines neural networks’ learning capabilities with fuzzy logic’s rule-based reasoning, offering a flexible and adaptable control strategy. The second is a classic Mamdani fuzzy system that relies on human-defined fuzzy rules, providing an intuitive approach to control. A key contribution of this work is the development of a fast comprehensive, model-based dataset for neural network training generated without the need for real sensor data. The results show the evaluation of these two systems’ performance, robustness, and computational efficiency using low-cost ultrasonic sensors on a Pioneer 3DX robot within the Coppelia Sim environment.

This study presents the design, implementation, and field validation of a low-cost Internet of Things (IoT)-enabled embedded platform for real-time energy efficiency assessment of heat pump systems. The platform integrates an ESP32-based microcontroller with calibrated temperature, flow rate, and electrical current sensors to automate the determination of the coefficient of performance (COP). Unlike conventional studies that rely on high-cost instrumentation or laboratory-only trials, this work emphasizes a portable and scalable solution deployable in real environments. A three-month continuous deployment demonstrated high measurement accuracy against certified reference instruments, achieving mean absolute error (MAE) values of 0.29 °C (temperature), 0.0027 L/min (flow rate), and 0.110 A (current). Under steady-state conditions, the daily COP ranged from 1.43 to 4.43, diverging from the nominal manufacturer value of 3.83 and confirming the influence of operational and environmental factors reported by field studies. These results indicate that the platform serves not only as a diagnostic and monitoring tool but also as a replicable framework for predictive maintenance, operational optimization, and sustainable energy management in both grid-connected and off-grid applications, bridging the gap between engineering implementation and scientific assessment of heat pump performance.

This study presents the design and validation of an AI-enhanced embedded IoT system for real-time industrial sensor calibration. The proposed platform integrates a PT100 temperature sensor and a 4–20 mA pressure transmitter with an ESP32 microcontroller, enabling on-device data acquisition, processing, and wireless transmission. A lightweight multilayer perceptron (MLP) neural network, trained in Python with a hybrid dataset (synthetic and experimental) and deployed on the ESP32 via JSON weight files, performs local inference to estimate ideal sensor outputs and compute key performance metrics. Experimental tests under controlled laboratory conditions confirmed high accuracy, with efficiency above 98.6%, RMSE below 0.005 V, and absolute uncertainty margins of ±0.5 °C and ±0.07 bar. Additionally, 95% confidence intervals for RMSE and standard deviation demonstrated statistical reliability across all operating points. The prototype also addresses practical constraints, including ESP32 ADC nonlinearity, energy consumption, and multi-sensor scalability, while remaining portable and low-cost. The integration of edge AI capabilities demonstrates the feasibility of executing accurate neural network models directly on embedded microcontrollers, eliminating reliance on cloud-based processing. The proposed solution provides a robust proof-of-concept that is scalable, cost effective, and suitable for industrial IoT applications, predictive maintenance, and Industry 4.0 environments, with future work focusing on long-term drift evaluation and validation under real industrial conditions.