Current Issue : October-December Volume : 2025 Issue Number : 4 Articles : 5 Articles
Physics-assisted machine learning is a powerful framework that enhances data efficiency by integrating the strengths of conventional machine learning with physical knowledge. This paper applies this concept and focuses on the design of a driver evaluator using physics-assisted unsupervised learning, which serves as a virtual reference generator that provides different driving modes for vehicles equipped with active actuators. A strategy that applies sensitivity analysis regarding the vehicle handling performance, aiming to reduce the computational workload of the clustering algorithms, is proposed. First, a bicycle model with nonlinear Pacejka’s tire models is established for the analysis of lateral dynamics. Next, mathematical interpretations of sensitivity analysis are derived to evaluate the contribution of physical parameters to the system response and build the reduced parameters set. Then, Gaussian mixture models are fitted to a database generated with the full parameters set and another with the reduced set, respectively. Finally, step-steer and constant radius tests are performed to assess the handling performance with respect to the two validated centroids. Comparisons of lateral dynamics and understeer characteristics indicate that the proposed method can accurately distinguish driving modes in a much faster manner compared to traditional machine learning. This methodology has significant potential for practical applications with large databases and more complex systems....
Urban logistics are facing growing sustainability challenges, particularly in last-mile delivery operations, which contribute significantly to traffic congestion, emissions and operational inefficiencies. The COVID-19 pandemic further exposed the vulnerabilities in traditional logistics systems, accelerating interest in innovative solutions such as electric vehicles (EVs) and autonomous vehicles (AVs) for last-mile delivery. This study investigates the potential of EV and AV technologies to enhance sustainable urban logistics by integrating cleaner, smarter transportation into delivery networks. Drawing on survey data from logistics professionals and consumers in Italy, the findings highlight the key benefits of EV and AV adoption, including reduced emissions, improved delivery efficiency and increased resilience during global disruptions. Autonomous delivery robots and EV fleets can reduce labor costs, traffic congestion and carbon footprints while meeting evolving consumer demands. However, barriers such as limited charging infrastructure, range constraints, and technological readiness remain critical challenges. By addressing these issues and aligning EV and AV strategies with urban mobility policies, last-mile delivery systems can play a crucial role in advancing cleaner, more efficient and sustainable urban logistics. This research emphasizes the need for continued investment, policy support and public–private collaboration to fully realize the potential of EVs and AVs in reshaping future urban delivery systems....
The paper offers an overview of the motor vehicle brake pad wear process. Considering the types of wear that occur between the pads and the disc, the study begins by presenting Archard’s fundamental wear law. It explains how the hardness and roughness of materials can influence the wear rate. Furthermore, the analysis describes factors influencing the wear coefficient, including chemical affinity between materials, surface quality, thermo-elastic instability (TEI) of the materials, and environmental effects. The paper also presents detection systems for brake pad wear, such as sensors-based monitoring and artificial neural networks (ANNs). These systems monitor brake pad wear in real time, thereby improving the driving safety by alerting the driver to the condition of the brake pads. The principles and systems analyzed form the basis for predictive maintenance, minimizing the risks of brake failure due to excessive wear....
The underwater unstructured environment poses new challenges for the miniaturization and flexibility of underwater vehicles. This paper proposes a method of using micrometer-scale vibrations of piezoelectric vibrators to drive macroscopic jets. Then, we use two coupled piezoelectric jet driving units to construct a miniature underwater vehicle. Numerical simulation is used to investigate the flow field characteristics of coupled jets. Finally, the impact of the angle between the two piezoelectric jet drive units on the propulsion force is analyzed. The miniature underwater vehicle measures 77.8 mm in length and 87 mm in width. While achieving miniaturization, it maintains high flexibility, maneuverability, and controllability. By adjusting the input signals to the two piezoelectric jet drive units, the miniature underwater vehicle can move in a straight line, turn, and rotate. Its maximum linear velocity reaches 54.23 mm/s. Its outstanding motion ability and environmental adaptability allow it to perform various tasks in unknown and complex environments. It also has broad application prospects....
This study presents the development, simulation, and hardware implementation of a 48 V, 1 kW mid-range wireless power transfer (WPT) platform for autonomous guided vehicle (AGV) charging in industrial applications. The system uses an LCC-S compensation topology, selected for its ability to maintain a constant output voltage and deliver high efficiency even under load variations at a typical coil distance of 15 cm. It can also operate at different distances by adjusting the compensator circuit. A proportional–integral (PI) controller is implemented for current regulation, offering a practical, low-cost solution well suited to industrial embedded systems. Compared to advanced control strategies, the PI controller provides sufficient accuracy with minimal computational demand, enabling reliable operation in real-world environments. Current adjustment can be dynamically carried out in response to real-time changes and continuously monitored based on the AGV battery’s state of charge (SOC). Simulation and experimental results validate the system’s performance, achieving over 80% efficiency and demonstrating its feasibility for scalable, robust AGV charging in Industry 4.0 Manufacturing Settings....
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