Search Results (470)

Unmanned aerial vehicles (UAVs) are becoming an important platform for precision agriculture, supporting both high-throughput sensing and active field operations such as spraying, monitoring, and phenotyping. However, unlike general UAV applications, agricultural environments impose distinctive challenges due to heterogeneous field structures, canopy occlusion, terrain variation, dynamic disturbances, and strong coupling between navigation performance and task quality. To address this gap, this review presents a systematic analysis of UAV navigation in agricultural environments from a system-level perspective. The review first summarizes the core technical components of agricultural UAV navigation, including sensing, localization, mapping, planning, and control. It then discusses how navigation requirements vary across representative scenarios such as open fields, orchards, and terraced farmland, and examines their roles in key applications including aerial mapping, field monitoring, precision spraying, and close-range orchard operations. In addition, datasets, simulation platforms, and evaluation protocols relevant to agricultural UAV navigation are reviewed. Finally, major challenges are identified, including scene heterogeneity, perception degradation, insufficient task-semantic integration, limited control robustness, and the lack of standardized benchmarks. Future research should move toward robust, task-aware, and modular navigation architectures that support reliable and scalable agricultural UAV deployment. Full article

Seafloor hydrothermal systems at mid-ocean ridges are focal points for heat and matter exchange between the seawater and lithosphere. While seafloor seismographs (OBS) and pressure recorders (BPR) are standard for regional monitoring, achieving high-precision, vertical sub-surface data in complex hydrothermal terrains remains a significant technical objective. This study presents a novel in situ penetration probe designed for multi-parameter monitoring of marine hydrothermal vent areas. A key innovation of this work is its operational versatility and engineering efficiency: the probe is specifically designed for post-drilling deployment in boreholes, effectively utilizing existing coring sites to achieve direct coupling with the deep-seated crust, or for targeted placement via Remotely Operated Vehicles (ROVs). The device integrates a titanium-alloy conical tip and cylindrical chamber, housing tri-axial accelerometers and dual temperature-pressure sensors. Numerical simulations using the SST k-ω turbulence model and finite element analysis optimized the cone aperture and assessed fluid–structure stability under deep-sea conditions. Laboratory vibration tests and shallow-water sea trials validated the probe’s basic dynamic response, electromechanical integrity, and capability to acquire coupled environmental parameters. This compact, modular design provides a scalable and cost-effective framework for precise three-dimensional observation of sub-surface hydrothermal processes and deep-sea resource exploration. Full article

For vehicle driving scenarios in complex backgrounds, road sign detection faces challenges such as multi-scale targets, long-distances, and low-resolution. To address these challenges, a detection method based on an improved YOLO11n network is proposed. Firstly, to accommodate the multi-scale characteristics of the targets and improve the network’s ability to detect low-resolution objects and details, a Multi-path Gated Aggregation (MGA) Module is proposed, achieving these objectives via multi-dimensional feature extraction. Secondly, the Neck is improved by designing a network structure that incorporates high-resolution information from the Backbone, thereby enhancing the detection capabilities for small and blurry targets. Finally, an enhanced Spatial Pyramid Pooling—Fast (SPPF) module is proposed, wherein a Group Convolution-Layer Normalization-SiLU structure is integrated across various stages of information passing. By fusing adjacent channel information, it effectively suppresses complex background noise across multiple scales and amplifies road marking features, which consequently boosts the model’s discriminability for distant and obscured targets. Experimental results on a multi-type road sign dataset show that the improved model achieves an mAP@0.5 of 96.96%, which is 1.42% higher than the original model. The mAP@0.5–0.95 and Recall rates are 83.94% and 92.94%, respectively, while the inference speed remains at 134 FPS. Research demonstrates that via targeted modular designs, the proposed approach strikes a superior balance between detection accuracy and real-time efficiency. Consequently, it provides robust technical support for the reliable operation of intelligent vehicle perception systems under complex conditions. Full article

Unmanned aerial vehicles (UAVs) have become indispensable tools in precision agriculture and plant phenotyping, enabling the rapid, non-destructive assessment of crop traits across space and time. Equipped with RGB, multispectral, thermal, and other sensors, UAVs provide detailed information on canopy structure, physiology, and stress responses that can guide management decisions and accelerate breeding programs. Despite these advances, the downstream processing of UAV imagery remains technically demanding. Converting orthomosaics into standardized, biologically meaningful data often requires a combination of photogrammetry, geospatial analysis, and custom scripting, which can limit reproducibility and accessibility across research groups. We present drone2report, an open-source python-based software that processes orthomosaics from UAV flights to generate vegetation indices, summary statistics, derived subimages, and text (html) reports, supporting both research and applied crop breeding needs. Alongside the basic structure and functioning of drone2report, we also present five case studies that illustrate practical applications common in UAV-/drone-phenotyping of plants: (i) thresholding to remove background noise and highlight regions of interest; (ii) monitoring plant phenotypes over time; (iii) extracting information on plant height to detect events like lodging or the falling over of spikes; (iv) integrating multiple sensors (cameras) to construct and optimize new synthetic indices; (v) integrate a trained deep learning network to implement a classification task. These examples demonstrate the tool’s ability to automate analysis, integrate heterogeneous data and models, and support reproducible computation of agronomically relevant traits. drone2report streamlines orthorectified UAV-image processing for precision agriculture by linking orthomosaics to standardized, plot-level outputs. Its modular, configuration-driven design allows transparent workflows, easy customization, and integration of multiple sensors within a unified analytical framework. By facilitating reproducible, multi-modal image analysis, drone2report lowers technical barriers to UAV-based phenotyping and opens the way to robust, data-driven crop monitoring and breeding applications. Full article

In this paper, an integrated optimization is carried out to find the optimal hybrid rocket engine design for a modular multistage launch vehicle targeting a 500 km polar circular orbit. A single hybrid rocket engine unit is reused across the whole launch vehicle, with each stage constituted by a cluster of a specified number of units. Only the nozzle exit diameter of the units is allowed to change across each stage. This clustering approach is aimed at reducing the costs of the launch vehicle and at simplifying the optimization procedure. After a brief mission analysis based on Tsiolkovsky’s equation, a three-stage configuration is chosen for the launch vehicle, employing 16, 4, and 1 engine units for, respectively, the first, second, and third stage. A neural network-based surrogate model is employed to approximate the complex hybrid rocket internal ballistics, with the aim to reduce the computational cost of the optimization process. The surrogate model is trained to map a reduced number of design parameters to the performance and mass budget of a single engine unit using data from a 0-D hybrid rocket engine model. The accuracy of the trained network in predicting crucial features is then assessed. Finally, the trained network is integrated into a multidisciplinary optimization process. The aim is to identify the optimal rocket engine design and launch vehicle ascent trajectory that maximize the payload capacity to the target orbit. Full article

The electrification of urban and industrial transport is driving the need for propulsion architectures that combine energy efficiency, operational flexibility and regulatory compliance. However, current electric platforms often lack the adaptability required for customized body configurations and multistage manufacturing, and their approval is hindered by the complexity of meeting electrical safety and electromagnetic compatibility (EMC) requirements at vehicle level. This article presents the conceptual design of a modular electric propulsion module developed within the MODULe project, in which the traction motor, inverter, battery pack, Battery Management System (BMS) and cooling circuits are integrated into a standardized module conceived as an Independent Technical Unit (ITU). The propulsion module dimensioned using a modified WLTP cycle, and the results indicate that the selected components can meet the dynamic demands of light and medium-duty vehicles, achieving an estimated consumption of around 50 kWh/100 km and a driving range above 160 km. By concentrating the critical regulatory requirements within a single module, the proposed architecture facilitates multistage vehicle approval, reduces development effort and supports the scalable electrification of commercial fleets. This approach may contribute to accelerating the deployment of zero-emission vehicles in urban logistics and industrial applications, with potential benefits for both the sector and society. Full article

Air curtain systems have been proposed as a supplementary smoke control strategy for vehicle tunnels, particularly where structural constraints limit the installation or upgrading of conventional ventilation systems. However, most previous studies rely on numerical simulations or fixed experimental facilities, while flexible experimental platforms and the influence of vehicle obstruction on smoke behavior remain less explored. This study experimentally investigates the smoke confinement performance of an air curtain using a 1:18 modular detachable scaled vehicle tunnel model. The modular configuration enables flexible assembly and adjustment of the experimental setup for different test conditions. A series of laboratory experiments was conducted using a liquefied petroleum gas (LPG) burner to simulate a vehicle fire. Temperature measurements and smoke visualization were performed under different air curtain jet velocities and vehicle obstruction conditions to analyze the interaction between the air curtain jet and buoyancy-driven smoke flow. The results show that the air curtain significantly restricts the upstream propagation of hot smoke and modifies the thermal field inside the tunnel. When the jet velocity reached approximately 5 m/s, the temperature in the protected region decreased by about 25–35% compared with the case without an air curtain. In addition, the presence of vehicle models altered the airflow structure and increased heat accumulation in the middle region of the tunnel cross-section. These results demonstrate that the proposed modular tunnel model provides a reliable experimental platform for tunnel fire research and highlights the importance of considering vehicle obstruction effects in tunnel smoke control studies. Full article

This paper presents a distributed V2G-enabled multiport DC charging system with a hierarchical charging management strategy. Unlike conventional architectures based on centralized power converter cabinets, the proposed system distributes bidirectional power converters within individual multiport dispensers, each equipped with a local charging power management device. This architecture improves system scalability, fault tolerance, and operational flexibility while enabling vehicle-level charging and V2G services. A hierarchical control framework is introduced, consisting of high-level optimal charging scheduling, mid-level power coordination among distributed dispensers, and low-level converter control. Key elements include modular power units that can be dynamically configured and expanded, providing a cost-effective and adaptable solution for growing EV markets. Experimental results obtained from a 45 kW modular DC charging prototype demonstrate an efficiency improvement of up to 2% at rated power compared to a non-modular charger. In contrast, the optimized charging strategy achieves an overall charging cost reduction of approximately 11% and a peak load demand reduction of up to 31%. Furthermore, stable bidirectional power flow, effective power sharing, and total harmonic distortion within regulatory limits are experimentally validated during both charging and V2G operation. The prototype is implemented to validate the proposed charging system in the laboratory environment. Full article

This article introduces a new Multi-Source DC-DC converter-based smart energy management system on a common DC bus architecture, utilizing solar PV and wind sources for electric vehicle applications. The common DC bus enables coordinated power flow control among multiple sources while maintaining modularity and flexibility. To promote efficient battery charging and discharging, as well as enhanced protection from faults, an artificial neural network (ANN) approach has been incorporated. The main function of the ANN controller is to detect faults in the EV battery for timely intervention. Compared to existing topologies, its coordinated integration and control can operate effectively under dynamic conditions and improve stability. Additionally, the article presents the operating principle, modes of operation, design analysis, and control strategy. The simulation results of the proposed system are evaluated through MATLAB Simulink software 2024b. Furthermore, a 200 W laboratory prototype was developed to validate the system’s dynamic performance under various operating conditions. Full article

Mini-roundabouts are increasingly implemented as compact, low-cost alternatives to conventional roundabouts and signalized intersections, especially at low-speed, space-constrained urban locations where safety is a concern. Their design emphasizes speed management, reduced conflict severity, and operational simplicity, contributing to safer mobility for all road users. This paper reviews U.S., German, and UK design guidelines and synthesizes empirical safety evidence from before-and-after studies of mini-roundabout conversions. In terms of design, the U.S. practice typically relies on a single large design vehicle and more permissive geometry, whereas the German guidance adopts a multi-vehicle approach with tighter curvature and stronger compactness to enforce lower speeds, affecting crash risk and driver behavior. The UK guidance is distinguished by its flush or slightly domed central marking and flexible application approach. Conversions from two-way stop-controlled (TWSC) or one-way stop-controlled (OWSC) intersections yield substantial reductions in injury and severe crashes, with total crash reductions of 17–42%. Conversions from all-way stop-controlled (AWSC) intersections present more variable outcomes, including increases in total crashes, because drivers are still reacting based on the previous control and may not adjust their expectations quickly. Modular roundabouts are also examined as alternative compact interventions for constrained or high-risk sites, with early evidence indicating reductions in severe crashes and improved speed control while minimizing construction costs and right-of-way impacts. Full article

Nano-aerial vehicles have emerged as pivotal tools in modern robotics research, offering a safe and scalable means to validate complex algorithms in resource-constrained environments. This scoping review synthesizes the extensive body of work on the Crazyflie nano-quadcopter and evaluates its potential for drone application development in research and academia. The Crazyflie quadcopter has emerged as a leading open-source platform for education and research in aerial robotics due to its modularity and low cost. Despite its rapid evolution, there is currently no comprehensive synthesis mapping its diverse applications across hardware configurations and research domains. This evaluation systematically charts existing research on the Crazyflie platform, outlining its development, identifying relevant hardware and software configurations, categorizing major research topics, and identifying knowledge gaps. A systematic search was performed on three major databases, Scopus, Web of Science and Google Scholar, for studies published between 2015 and 2025. The results indicate a rapid growth in scientific production, an involved research community and very diverse thematic approaches. Expansion decks for the Crazyflie have been analyzed together with their relation to specific fields of research. While control systems remain the primary research theme, there is a significant shift toward artificial intelligence and swarm robotics. Full article

This research paper presents Volvo SmartCell, an AC battery technology that integrates modular multilevel converters and battery cells to form a unified system for electric vehicle propulsion and power supply. The research work addresses the broader challenge of reducing driveline cost and complexity by replacing traditional components such as inverters, onboard chargers, centralized DC/DC converters, vehicle control units and many more. SmartCell uses distributed Cluster Boards comprised of H-bridges which are controlled via wireless communication to generate AC voltage, deliver redundant low voltage power, and support cell level protection mechanisms. The prototype testing demonstrates that the system can supply traction power by engaging clusters according to the required voltage depending on motor speed, achieve AC grid charging by synthesizing sinusoidal voltages without a dedicated charger, and provide autonomous DC/DC operation through cluster level voltage regulation. Simulations further indicate that multilevel voltage generation can reduce switching losses and improve electric machine efficiency compared to conventional systems. Additional benefits include active cell balancing, support for mixed cell chemistries, and high redundancy through multiple independent power branches. Challenges remain in wireless bandwidth limitations and cost optimization of Cluster Boards. Ongoing development aims to enhance communication robustness and validate safety for non-isolated grid charging. Full article

Dynamic Wireless Power Transfer (DWPT) represents a promising solution to advance sustainable electric mobility by reducing vehicle downtime, extending driving range, and mitigating the need for battery oversizing. However, the lack of integrated and flexible experimental testbeds still limits the validation of emerging technologies. This paper presents DEXTER (Development of an Enhanced eXperimental proTotype of wirEless chargeR), a 1:2-scale open platform specifically designed for research on DWPT systems. The setup integrates a three-axis motion control for coil misalignments and trajectory emulation, digitally regulated TX/RX converters, a programmable battery emulator, and electromagnetic shielding coils equipped with field probes. A MATLAB-based interface enables automated testing and Hardware-in-the-Loop (HiL) integration. By combining modularity, scalability, and reproducibility, DEXTER provides a comprehensive framework for experimental optimization of power electronics and electromagnetic design while ensuring compliance with international safety standards. The case studies analyzed here demonstrate the capability of such a platform to validate and optimize the DWPT design choices, checking their impact on the overall performance of these systems. The platform constitutes a reference environment for both academia and industry, supporting the development of next-generation wireless charging systems and contributing to the sustainability and reliability of future electric mobility infrastructures. Full article

Background: The increasing volume of waste and stricter environmental regulations necessitate efficient waste transportation. Optimizing the specialized vehicle fleet remains a challenge due to fragmented decision-making approaches. Methods: This study develops a Decision Support System (DSS) integrating a simulation model (developed in AnyLogic) with a vehicle competitiveness assessment module (developed in Python). The simulation reproduces waste generation, collection (schedule-based and event-based), and transport logistics. An optimization experiment was conducted to minimize total logistics costs by varying fleet composition. Results: The findings indicate that the optimal fleet configuration reduced total logistics costs by 40.64% compared to the baseline; this reduction was statistically significant. Conclusions: The proposed DSS enables integrated optimization of fleet composition, demonstrating substantial potential for improving both economic and environmental performance of waste transportation systems. The modular architecture supports adaptation to diverse operational contexts. Full article

Traditional stator-permanent magnet flux-reversal motors have the problems of large cogging torque, limited improvement of power density, and low fault-tolerant performance. Based on the traditional flux-reversal motor, this paper proposes a design scheme of a flux-reversal motor with a stator-segmented double-winding and double-sequence permanent magnet structure. The motor adopts a stator-segmented modular design, each independent stator segment is connected by permanent magnets, and a bipolar permanent magnet array is arranged on the teeth of the stator segment. Meanwhile, a double-winding system composed of independent power windings and fault-tolerant windings is configured to realize the dual characteristics of high power density and high reliability. A two-dimensional finite element model is established to simulate and analyze the motor, which verifies the feasibility of the motor structure design. The simulation results show that the motor has improved operation stability, better fault-tolerant performance, and theoretically lower maintenance cost, as well as being especially suitable for the petroleum and chemical industries, electric vehicles, aerospace, and other application fields with high requirements for motor reliability and power density. Full article