Intra-row weeding is a time-consuming and technically demanding operation in orchard and vineyard management, requiring high precision to avoid damage to crop trunks while ensuring effective weed removal. To address this challenge, a rotary weeder implement integrated with an autonomous electric robot was developed and evaluated. The conventional hydraulic drive system was replaced with an electric motor, and selected mechanical components were redesigned to reduce the overall weight of the implement. Side-shift, height, and tilt adjustments were achieved using linear electric actuators to enable precise positioning during operation. For trunk detection, two sensing approaches were investigated, namely a conventional electromechanical feeler sensor and a sonar-based sensor. Autonomous row following was accomplished using data acquired from a two-dimensional laser scanner. The robotic prototype was experimentally evaluated at a forward speed of 0.16 m s⁻¹ and a working depth of 40 mm. The overall performance of both trunk detection systems was assessed in terms of weeding quality and power consumption. The experimental results demonstrated that the autonomous intra-row weeding robot could serve as a viable alternative to conventional machinery. Among the tested sensing systems, the sonar sensor exhibited superior performance compared to the adjusted feeler mechanism. The integration of autonomous navigation with electric weeding technology showed potential to improve weeding quality while reducing power requirements in future field applications.

This paper describes the systematic development of a low-cost robotic mobility platform tailored for assistive tasks, specifically addressing the critical mobility needs of two vulnerable user groups: visually impaired individuals and the elderly. To lay a solid foundation for the design, this work first conducts an in-depth analysis of the prevalent challenges plaguing existing assistive mobility devices, such as prohibitive manufacturing costs that limit accessibility and functional fragmentation that fails to meet diverse daily needs. Corresponding core design criteria are then derived from the specific requirements of the target users, including robust terrain adaptability for complex indoor and outdoor environments and a strict cost ceiling of under €500. In response to these demands, a hybrid wheeled-legged locomotion structure is proposed, featuring a five-wheeled base integrated with adjustable front legs; the conceptual mechanical design of this structure is described to illustrate how it enables seamless transition between efficient flat-ground operation and reliable stair-climbing capability. For prototype fabrication, cost-effective commercial components are prioritized, including an Arduino Nano microcontroller as the control core and MG996R servos for actuation. Comprehensive assembly and performance testing are carried out to validate the platform’s efficacy. The experimental results demonstrate that the fabricated prototype weighs merely 1.9 kg with a total cost of approximately €400, and it achieves stable navigation on typical urban surfaces such as asphalt and interlock paving, as well as safe stair climbing—all performance metrics are fully aligned with the predefined design specifications, verifying the feasibility and practical value of this proposed low-cost assistive mobility solution.

With industrial and residential electricity demands on the increase, electrification plays a fundamental role in providing the necessary infrastructure for this constant evolution, which fuels several sectors that are important to society. To distribute electricity on a large scale and over long distances, high-voltage transmission systems, which operate at very high voltages with multiple cables, are commonly used worldwide. These cables must be spaced apart to ensure that they do not come into contact. Thus, in this paper, we develop a robotic module coupled with a drone that is used to install spacers. Spacers are elements that are used between various cables within a power distribution system to keep them apart. The drone transports the robotic module to the cable, controls the robotic module using radio signals, and installs/uninstalls the spacer. After completing the procedure, the drone searches for the robotic module, returning it to the ground station to repeat the operation if necessary. This paper describes the development of the mathematical model followed by the CAD/CAE design. Computer simulations verified the feasibility of using this robotic module for installing spacers. It is noteworthy that this module can be placed on cables using a drone or a hot stick, eliminating the need for technicians to come into direct contact with the cables or to move along them. This drone–robot aims to reduce risks for technicians who carry out these operations by climbing the towers and moving on cables or in some cases may be suspended from a platform fixed to a helicopter.

Upper extremity injuries caused by trauma and repetitive movements are common among elderly individuals, manual labor workers, and athletes. A traditional rehabilitation program to restore range of motion and strength consists of massage, physiotherapy, and mechanotherapy. Although a great variety of commercial devices is available, the majority lack sensorization for performance monitoring. This paper presents a sensor-integrated rotating crank mechanism for upper limb rehabilitation by comparing the performance of three motor configurations and by validating the system's capability through quantitative motion parameters during exercise. Three prototype configurations were developed and tested. The first prototype (V0) was tested in passive mode without motor activation, then the stepper motor in (V1) was activated, and testing was carried out in semi-active mode. The third prototype (V3) was designed and tested in motorized mode. A 6-axis IMU sensor was integrated onto the crank to capture acceleration and orientation data. Testing was conducted with six volunteers (3 male, 3 female, aged 21-26 years) performing exercises in both horizontal and sagittal planes. The V0 configuration demonstrated predictable motion patterns with an acceleration magnitude ranging from approximately 9 to 13 m/s². Sagittal plane rotation exhibited higher variability with the range 5-20 m/s² due to gravitational effects. The V2 configuration introduced vibration and irregularities in motion smoothness with a magnitude acceleration range of 12 to 13.5 m/s². The most consistent performance was demonstrated by the brushless DC motor system (V3) with stable acceleration profiles (7-18 m/s² horizontal, 4-20 m/s² vertical). Acquired data revealed gender-related differences in peak acceleration, where male volunteers exhibited higher acceleration peaks, especially in the sagittal plane, where the Az component reached up to 25 m/s², whereas female volunteers showed lower and smoother acceleration profiles. The sensor-integrated system provided a reliable method for acquiring quantitative performance metrics, establishing a viable foundation for monitoring rehabilitation progress.

The continuous monitoring of patients affected by Alzheimer’s disease requires autonomous and reliable machine-based systems capable of operating under strict energy, size, and safety constraints. This work proposes an intelligent mechatronic architecture for an implantable monitoring device integrating embedded biomedical sensors, low-power processing units, secure wireless communication, and artificial intelligence for real-time data analysis. To address the limited availability of clinical datasets, a digital twin-based synthetic data generation framework is developed. The proposed system is evaluated on a multidimensional dataset composed of 10, 235 records, including physiological, behavioral, and cognitive parameters, with an 80/20 train-test split. Random Forest, Support Vector Machine, and Deep Neural Network models are implemented and compared using standardized classification and regression metrics, including accuracy, precision, recall, F1-score, confusion matrices, and error-based indicators. The experimental results show that the Deep Neural Network consistently outperforms classical machine learning models, achieving higher classification accuracy, reduced misclassification rates, and more stable convergence behavior, as confirmed by learning and loss curves. From a mechatronics perspective, the proposed solution emphasizes modular system integration, computational efficiency, and compatibility with implantable hardware constraints. The results demonstrate the feasibility of embedding intelligent decision-making capabilities into compact mechatronic systems, highlighting their relevance for intelligent machines and continuous monitoring applications.

Introduction: Severe weather events such as floods are increasing in frequency and pose significant risks to homes, infrastructure, and human safety. Traditional domestic flood protection systems often require manual setup or user intervention, limiting their effectiveness during sudden flood events. Addressing this gap, we propose an integrated automated barrier system that is designed to reduce internal flooding and associated damage for residences located on sloped streets.

Methods: The developed system combines mechanical components with sensor-driven automation to detect rising floodwater and react in real time. The key elements include an array of hinged diverting plates (lintels) and a vertical sealing barrier installed at the entrance threshold. When sensors register imminent water ingress, the system autonomously deploys the lintels to redirect surface flow away from the doorway, creating a localized dry area. Simultaneously, the sealing barrier engages to prevent water from penetrating the the building's interior. The design process incorporated standard door dimensions and complied with relevant safety and automation regulations.

Results: Prototype testing under simulated flood conditions demonstrated consistent activation without human intervention, effective water diversion away from the entryway, and reduced internal water penetration compared to conventional static barriers. The automated control logic reliably interpreted sensor inputs, triggering timely deployment and retraction of barrier elements.

Conclusions: The intelligent automated barrier shows promise as a proactive residential flood mitigation solution that enhances response times and protects property with minimal human input. Future work will refine sensor calibration and assess long-term field performance across diverse flood scenarios.

While bipedal locomotion for humanoid robots has improved significantly over the past 50 years of dedicated research, energy efficiency and load capacity remain challenging considerations in locomotion tasks. The LARMbot V.3 humanoid addresses these issues with custom parallel architectures in a low-cost and compact platform (850 mm in height, 462 mm in leg length, and 3.6 kg in weight) mainly for research and education purposes.

This work presents a performance evaluation of the LARMbot V.3 modular bipedal locomotion system, which is based on a parallel-serial (3-UPR)R mechanism for each leg. This structure offers proper precision, rigidity, and load capacity while maintaining the desired walking performance and movement stability. After we introduce the architecture, a prototype is presented and we describe the design, manufacturing, and assembly. Walking performance tests were conducted in two modes—free motion in the air and with ground contact—to measure power consumption, speed, and movement repeatability.

The tests demonstrated a stable and repeatable gait cycle with a step length of 80 mm, a step height of 20 mm, and a speed of 5 seconds per step. The acquired IMU data confirmed synchronized leg motion and hip orientation deviations within ±15°. Power measurements showed consistent and low values during the tests, with an average power consumption of 5.11 W. These results confirm the efficiency and reliability of the proposed LARMbot V.3 parallel biped locomotion system. This design enables stable and precise movement while maintaining low power consumption, and it is easy to manufacture. The module can serve as a foundation for further improvements and development of parallel locomotion for humanoid robots.

Collaborative manipulators are often deployed as robotic systems that are intended to operate safely and intuitively within shared workspaces alongside human users. Their effectiveness depends not only on mechanical design, but also on the reliability of the underlying control architecture, the quality of sensor feedback, and the system’s ability to adapt to human interaction in real time. This study provides a structured review of modern control strategies used in collaborative manipulators, examining the theoretical principles and practical implementation of impedance, admittance, hybrid, and sensor-based approaches. An analysis of the existing literature shows that impedance and admittance control techniques are particularly well suited for human–robot collaboration, as they help to maintain stability during contact while enabling compliant and responsive motion that aligns with human intent.

This paper introduces a conceptual multi-layer control architecture that integrates safety supervision, trajectory planning, and sensor fusion. Within this architecture, a hybrid control scheme that combines force and motion sensing is highlighted as a promising direction for achieving adaptive behavior. Such an approach supports real-time adjustment of dynamic parameters and aligns with the safety limits defined in ISO/TS 15066, ensuring controlled contact forces and safe motion near human operators.

Overall, the presented framework offers a comprehensive theoretical foundation for further development of adaptive and sensor-rich control systems. These insights are expected to contribute to subsequent simulation studies, prototype testing, and the broader implementation of collaborative robots in industrial, medical, and human-assistive applications.