Search Results (147)

To enable autonomous route planning for UAV swarms in dynamic air–terrestrial cooperative navigation scenarios within GNSS-denied environments, this paper proposes a lightweight framework based on the Artificial Potential Field (APF) method. In the considered architecture, UAVs act as mobile transit navigation nodes that relay positioning information from sparse ground anchors to terrestrial users. For TOA-based cooperative positioning, the instantaneous geometric configuration of the UAV swarm significantly affects the overall system accuracy. Therefore, the impact of UAV positions on the end-to-end navigation performance is rigorously analyzed, yielding a comprehensive Dilution of Precision (DOP) matrix for the entire air–terrestrial system. By applying the Schur complement, the global performance metric is decomposed, resulting in a scalar evaluation function that directly reflects the geometric quality of the configuration. In practical scenarios involving dynamic and heterogeneous users, real-time trajectory adaptation of the UAV swarm is essential to continuously optimize user positioning accuracy. To this end, an APF-based autonomous joint route planning approach is developed. The potential field is constructed directly from the derived geometric evaluation model, where its negative gradient generates virtual forces that autonomously guide the UAV swarm. This elegantly bridges high-level navigation performance optimization with low-level motion control of the swarm. The simulation results show a 76.1% improvement in the average comprehensive GDOP for users compared to the baseline of hovering UAVs, validating the effectiveness and real-time capability of the proposed lightweight framework. Full article

Control techniques for neural-network-based charging stations (CSs) are attracting attention worldwide. This popularity is due to the emergent need for alternative intelligent and adaptive control solutions for attaining a CS with stabilized power transfer and voltage control at the point of common coupling. This paper demonstrates novel neural-network-based improved virtual synchronous motor (NN-i-VSM) control through the mechanism of the charging voltage feedback in conjunction with a trained neural network model to adaptively produce field excitation (MN) that mimics a virtual flux model. The MN adaptively generates an electromotive force based on the trained NN output to control the rectifying converter response of the CS for power quality enhancement during multiple-CS operation. Simulation results in the scenario of multiple CSs at 750 kW (5 × 150 kW) with varying capacities showed significant improvement in voltage variable tracking capacity of up to 500 V as well as power response overshot reduction and grid voltage response tracking improvement compared with an i-VSM-based CS model. A comprehensive CS efficiency assessment and plant stability analysis, including Bode plot evaluation, further confirmed the superior dynamic response performance and robustness of the NN-i-VSM model over the i-VSM model. The proposed model offers scalable applicability in smart mobility and wireless CS integration, signifying a new control advancement for future generations of multiple-grid-friendly charging infrastructure for penetration of batteries at varying capacities. Full article

Magnetorheological (MR) fluids are composite materials composed of ferromagnetic particles, medium oils, and several types of additives. MR fluids are particularly suitable for haptic applications, because their rheological properties can be rapidly, stably, and reversibly controlled using an applied magnetic field, MR fluids are particularly suitable for haptic applications. Moreover, with recent advances in virtual reality technologies, handheld haptic interfaces that offer high portability and operability, owing to their lightweight and compact design, have become increasingly important for enhancing immersion in teleoperation systems. In this study, we design and develop a miniature MR fluid device for handheld haptic interfaces using a cylindrical rotor. The proposed device is compact and light, and exhibits a high output. We analyzed the magnetic field distribution inside the device using an analytical model and confirmed that the serpentine magnetic flux path effectively increased the magnetic flux density in the MR fluid working region. According to the experimental characterization, the device generated a maximum torque of 0.3 Nm. The resulting interface had a total mass of 122 g and provided a maximum force of 4.5 N to the user, demonstrating its suitability for teleoperation and virtual reality applications. Full article

Introductory environmental science courses offer non-STEM students an entry point to address global challenges such as climate change and cryosphere preservation. Aligned with the International Year of Glacier Preservation and the Decade of Action for Cryospheric Sciences, this mixed-method, IRB-exempt study applied the Curriculum Redesign and Artificial Intelligence-Facilitated Transformation (CRAFT) model for course redesign. The project leveraged a human-centered AI approach to create anthropomorphized, place-based narratives for online learning. Generative AI is used to amend immersive virtual learning environments (VLEs) that animate glacial forces (water, rock, and elemental cycles) through narrative-driven virtual reality (VR) experiences. Students explored Iceland’s Jökulsárlón Glacier Lagoon via self-guided field simulations led by an imaginary water droplet, designed to foster environmental awareness and a sense of place. Data collection included a five-point Likert-scale survey and thematic coding of student comments. Findings revealed strong positive sentiment: 87.1% enjoyment of the imaginaries, 82.5% agreement on supporting connection to places, and 82.0% endorsement of their role in reinforcing spatial and systems thinking. Thematic analysis confirmed that anthropomorphic imaginaries enhanced emotional engagement and conceptual understanding of glacial processes, situating glacier preservation within geographic and global contexts. This AI-enhanced, multimodal approach demonstrates how narrative-based VR can make complex cryospheric concepts accessible for non-STEM learners, promoting early engagement with climate science and environmental stewardship. Full article

Recent projections by the Intergovernmental Panel on Climate Change indicate that global warming will turn permanent and further intensify the severity and frequency of extreme weather events (heat waves, rain, and intense droughts), with coastal regions being the most vulnerable to extreme events. Therefore, the risk of natural disasters and the associated regional impacts on water, food, energy, social, and health security represents one of the world’s greatest challenges of this century. However, conventional methodologies for monitoring these regions during extreme events are usually not available to managers and decision-makers with the necessary urgency. The aim of this study was to present a framework concept for assessing extreme flood event impacts in coastal zones using a suite of field data combined with numerical (hydrological, meteorological, and hydrodynamic) and computational (flooding) models in a virtual environment that provides a replica of a natural environment—the Patos Lagoon Digital Twin. The study case was the extreme flood event that occurred in the southernmost region of Brazil in May 2024, considered the largest flooding event in 125 years of data. The hydrodynamic model calculated the water levels around Rio Grande City (MAE ± 0.18 m). These results fed the flooding model, which projected the water over the digital elevation model of the city and produced predictions of flooding conditions on every street (ranging from a few centimeters up to 1.5 m) days before the flooding happened. The results were further customized to attend specific demands from the security forces and municipal civil defense, who evaluated the best alternatives for evacuation strategies and infrastructure safety during the May 2024 extreme flood event. Flood Safety Maps were also generated for all the terminals in the Port of Rio Grande, indicating that the terminals were 0.05 to 2.5 m above the flood level. Overall, this study contributes to a better understanding of the strengths of digital twin models in simulating the impacts of extreme flood events in coastal areas and provides valuable insights into the potential impacts of future climate change in coastal regions, particularly in southern Brazil. This knowledge is crucial for developing targeted strategies to increase regional resilience and sustainability, ensuring that adaptation measures are effectively tailored to anticipated climate impacts. Full article

This paper aims to address the problem of the low success rate of seedling picking and throwing, and the high damage rate of pot seedling, caused by the unclear interaction and parameter mismatch between the seedling picking end effector and the pot seedling during the seedling picking and throwing process of automatic transplanters. An EDEM–RecurDyn coupled simulation was conducted, through which the disturbance of substrate particles in the bowl body during the inserting, extracting, and transporting processes by the seedling picking end effector was visualized and analyzed. The force and motion responses of the particles during their interaction with the seedling picking end effector were explored, and the working parameters of the seedling picking end effector were optimized. A seedling picking end effector using two fingers and four needles is taken as the research object, a kinematic mathematical model of the seedling picking end effector is established, and the dimensional parameters of each component of the end effector are determined. Physical characteristic tests are conducted on Shanghai bok choy pot seedlings to obtain relevant parameters. A discrete element model of the pot seedling is established in EDEM 2022 software, and a virtual prototype model of the seedling picking end effector is established in Recurdyn 2024 software. Through EDEM-Recurdyn coupled simulation, the force and movement of the substrate particles in the bowl body during the inserting, extracting, and transporting processes of the seedling picking end effector under different operating parameters were explored, providing a theoretical basis for optimizing the working parameters of the end effector. The inserting and extracting velocity, transporting velocity, and inserting depth of the seedling picking end effector were used as experimental factors, and the success rate of seedling picking and throwing, and the loss rate of substrate, were used as evaluation indicators; single-factor tests and three-factor, three-level Box–Behnken bench tests were conducted. Variance analysis, response surface methodology, and multi-objective optimization were performed using Design-Expert 13 software to obtain the optimal parameter combination: when the inserting and extracting velocity was 228 mm/s, the transporting velocity was 264 mm/s, the inserting depth was 37 mm, the success rate of seedling picking and throwing was 97.48%, and the loss rate of substrate was 2.12%. A verification experiment was conducted on the bench, and the success rate of seedling picking and throwing was 97.35%, and the loss rate of substrate was 2.34%, which was largely consistent with the optimized results, thereby confirming the rationality of the established model and optimized parameters. Field trial showed the success rate of seedling picking and throwing was 97.04%, and the loss rate of substrate was 2.41%. The error between the success rate of seedling picking and throwing and the optimized result was 0.45%, indicating that the seedling picking end effector has strong anti-interference ability, and verifying the feasibility and practicality of the established model and optimized parameters. Full article

Cabbage stubble left in fields after harvest forms a mechanically complex stubble–soil composite that hinders subsequent tillage and crop establishment. Although the Discrete Element Method (DEM) is widely used to model soil-root systems, calibrated contact parameters for taproot-dominated vegetables like cabbage remain unreported. This study addresses this gap by calibrating a novel DEM framework that couples the JKR model and the Bonding V2 model to represent adhesion and mechanical interlocking at the stubble–soil interface. Soil intrinsic properties and contact parameters were determined through triaxial tests and angle-of-repose experiments. Physical pullout tests on ‘Zhonggan 21’ cabbage stubble yielded a mean peak force of 165.5 N, used as the calibration target. A three-stage strategy—factor screening, steepest ascent, and Box–Behnken design (BBD)—identified optimal interfacial parameters: shear stiffness per unit area = 4.40 × 10⁸ N·m⁻³, normal strength = 6.26 × 10⁴ Pa, and shear strength = 6.38 × 10⁴ Pa. Simulation predicted a peak pullout force of 162.0 N, showing only a 2.1% deviation from experiments and accurately replicating the force-time trend. This work establishes the first validated DEM framework for cabbage stubble–soil interaction, enabling reliable virtual prototyping of residue management implements and supporting low-resistance, energy-efficient tillage tool development for vegetable production. Full article

Low-disturbance liquid manure injection is increasingly important for sustainable soil management because it reduces residue burial, minimizes surface disruption, and lowers energy demand during application. However, the performance of low-disturbance shanks has not been systematically optimized, and their interaction with soil remains poorly quantified. This study developed an integrated discrete element method (DEM)–experimental framework to evaluate and optimize the performance of a purpose-built injector shank featuring a 45° rake angle, 25 mm thickness, and 110 mm width. The framework aimed to identify operating conditions that balance soil disturbance and energy efficiency. A DEM soil model was constructed using mechanical properties obtained from laboratory characterization tests and validated against soil bin experiments measuring draft force and soil rupture area across five working depths (100–250 mm) and three travel speeds (350–450 mm/s). The calibrated model showed strong agreement with experimental observations, yielding mean absolute relative errors of 1.7% for draft force and 6.2% for rupture area. Following validation, a multi-objective optimization was performed to minimize draft force while maximizing soil rupture, two key indicators of energy demand and injection effectiveness. Optimization results identified the most favorable operating parameters at a forward speed of 450 mm/s and an injection depth of 150 mm, achieving a desirability score of 0.884. The integrated DEM–experimental framework demonstrated reliable predictive capability and enables virtual testing of soil–tool interactions prior to field implementation. This study provides a scientifically grounded approach for improving injector shank operation and supports sustainable manure management by identifying settings that achieve adequate soil disruption while reducing energy consumption. Full article

This paper addresses the issues of target unreachability and local optima in traditional artificial potential field (APF) methods for UAV swarm path planning by proposing an improved collaborative obstacle avoidance algorithm. By introducing a virtual target position function to reconstruct the repulsive field model, the repulsive force exponentially decays as the UAV approaches the target, effectively resolving the problem where excessive obstacle repulsion prevents UAVs from reaching the goal. Additionally, we design a dynamic virtual target point generation mechanism based on mechanical state detection to automatically create temporary target points when UAVs are trapped in local optima, thereby breaking force equilibrium. For multi-UAV collaboration, intra-formation UAVs are treated as dynamic obstacles, and a 3D repulsive field model is established to avoid local optima in planar scenarios. Combined with a leader–follower control strategy, a hybrid potential field position controller is designed to enable rapid formation reconfiguration post-obstacle avoidance. Simulation results demonstrate that the proposed improved APF method ensures safe obstacle avoidance and formation maintenance for UAV swarms in complex environments, significantly enhancing path planning reliability and effectiveness. Full article

Based on an enhanced artificial potential field approach, this paper presents a control method for obstacle avoidance in vehicle platoons within Intelligent Vehicle-Infrastructure Cooperative Systems (I-VICS). To enhance safety during maneuvers, an inter-vehicle obstacle avoidance potential field model is established. By integrating virtual forces and a consistency control strategy into the control law, the proposed method effectively handles obstacle avoidance for vehicles operating at large inter-vehicle distances (80–110 m). Experimental validation using real-world trajectory data shows a 34% improvement in trajectory smoothness, as quantified by a proposed Vehicle Trajectory Stability (VTS) metric, leading to significantly safer avoidance maneuvers. A coordinated multi-vehicle obstacle avoidance strategy is further devised using a rotating potential field method, enabling collaborative and safe overall motion planning. Moreover, a path tracking strategy based on virtual force design is introduced to enhance platoon stability and reliability. Future work will focus on collision avoidance for vehicle platoons with varying inter-vehicle distances and will extend the consistency control and cooperative avoidance strategies to longer vehicle platoon to further improve overall traffic safety. Full article

The study of cotton stalk extraction resistance provides important parameters for the design of cotton stalk harvesting machinery. To investigate the effects of soil moisture content, cotton stalk diameter, and extraction angle on the extraction force of densely planted cotton stalks, this paper designs a real-time measurement system based on virtual instrument technology and conducts field tests. The tests were carried out in cotton fields at the First Farm in Aral City, Xinjiang, using the cotton variety “Xiulu Zhong 70”. Single-factor experiments were conducted with extraction angle and stalk diameter as influencing factors. A combined three-factor experiment was performed under the following conditions: soil moisture contents of 21.87% and 26.32%; extraction angles of 25°, 30°, and 35°; and cotton stalk diameters of 8.50–9.00 mm, 10.00–10.50 mm, and 11.50–12.00 mm. The results show that the minimum extraction force is required when the extraction angle is 30°. Soil moisture content significantly affects the extraction force, which increases with stalk diameter. The combined test results indicate that the order of significance of the three factors is as follows: cotton stalk diameter (A), extraction angle (B), and soil moisture content (C). The optimal combination is A₁B₁C₂, corresponding to a diameter of 8.50–9.00 mm, an extraction angle of 35°, and a soil moisture content of 26.32%. Based on comprehensive analysis, the recommended extraction angle range is 30–35°. The proposed system can efficiently complete cotton stalk extraction force tests, and the collected data provide valuable references for the design of cotton stalk harvesting machinery. By appropriately selecting the extraction angle and conducting harvesting under suitable soil moisture conditions, it is possible to reduce power consumption and improve production efficiency. Full article

As the environmental regulations of the International Maritime Organization (IMO) become more stringent, the accurate prediction of ship propulsion performance has become essential. Under ballast conditions where the draft is shallow, the propeller approaches the free surface, causing complex phenomena such as ventilation and surface piercing, which reduce propulsion efficiency. The conventional virtual disk (VD) method cannot adequately capture these free-surface effects, leading to deviations from model propeller results. To resolve this, a correction formula that accounts for the advance ratio (J) and submergence ratio ($h/D$) has been proposed in previous studies. In this study, the correction formula was simplified and implemented in a CFD environment using a field function, enabling dynamic adjustment of body force based on time-varying submergence depth. A comparative analysis was conducted between the conventional VD, modified VD, and model propeller using POW and self-propulsion simulations for an MR tanker and SP598M propeller. The improved method was validated in calm and regular wave conditions. The results showed that the modified VD method closely matched the performance trends of the model propeller, especially in free surface-interference conditions (e.g., $h/D$ < 0.5). Furthermore, additional validations in wave-induced self-propulsion confirmed that the modified VD method accurately reproduced the reductions in wake fraction and thrust deduction coefficient, unlike the overestimations observed with the conventional VD. These results demonstrate that the modified VD method can reliably predict propulsion performance under real sea states and serve as a practical tool in the early design stage. Full article

This study introduces a novel numerical approach to analyze the axisymmetric bending behavior of functionally graded (FG) graphene platelet (GPL)-reinforced annular sandwich nanoplates featuring a porous core. The nanostructures are exposed to coupled magnetic and hygrothermal environments. The porosity distribution and GPL weight fraction are modeled as nonlinear functions through the thickness, capturing realistic gradation effects. The governing equations are derived using the virtual displacement principle, taking into account the Lorentz force and the interaction with an elastic foundation. To address the size-dependent behavior and thickness-stretching effects, the model employs the nonlocal strain gradient theory (NSGT) integrated with a modified version of Shimpi’s quasi-3D higher-order shear deformation theory (Q3HSDT). The differential quadrature method (DQM) is applied to obtain numerical solutions for the displacement and stress fields. A detailed parametric study is conducted to investigate the influence of various physical and geometric parameters, including the nonlocal parameter, strain gradient length scale, magnetic field strength, thermal effects, foundation stiffness, core thickness, and radius-to-thickness ratio. The findings support the development of smart, lightweight, and thermally adaptive nano-electromechanical systems (NEMS) and provide valuable insights into the mechanical performance of FG-GPL sandwich nanoplates. These findings have potential applications in transducers, nanosensors, and stealth technologies designed for ultrasound and radar detection. Full article

Three-dimensional (3D) reconstruction technology is not only a core and key technology in computer vision and graphics, but also a key force driving the flourishing development of many cutting-edge applications such as virtual reality (VR), augmented reality (AR), autonomous driving, and digital earth. With the rise in novel view synthesis technologies such as Neural Radiation Field (NeRF) and 3D Gaussian Splatting (3DGS), 3D reconstruction is facing unprecedented development opportunities. This article introduces the basic principles of traditional 3D reconstruction methods, including Structure from Motion (SfM) and Multi View Stereo (MVS) techniques, and analyzes the limitations of these methods in dealing with complex scenes and dynamic environments. Focusing on implicit 3D scene reconstruction techniques related to NeRF, this paper explores the advantages and challenges of using deep neural networks to learn and generate high-quality 3D scene rendering from limited perspectives. Based on the principles and characteristics of 3DGS-related technologies that have emerged in recent years, the latest progress and innovations in rendering quality, rendering efficiency, sparse view input support, and dynamic 3D reconstruction are analyzed. Finally, the main challenges and opportunities faced by current 3D reconstruction technology and novel view synthesis technology were discussed in depth, and possible technological breakthroughs and development directions in the future were discussed. This article aims to provide a comprehensive perspective for researchers in 3D reconstruction technology in fields such as digital twins and smart cities, while opening up new ideas and paths for future technological innovation and widespread application. Full article

Virtual environment simulations have gained great importance in the field of robotics by enabling the validation and optimization of control algorithms before their implementation on real platforms. However, the construction of accurate digital models is limited not only by the lack of detailed characterization of the components but also by the uncertainty introduced by the physics engine and the plugins used in the simulation. Unlike other works, which attempted to model each element of the robot in detail and rely on the physics engine to reproduce its behavior, this paper proposes a methodology based on model following. The proposed architecture forces the simulated robot to replicate the dynamics of the real robot without requiring exactly the same physical parameters. The experimental validation was carried out on two unmanned surface vehicle (USV) platforms with different dynamic parameters and, therefore, different responses to excitation signals, demonstrating that the proposed approach enables a drastic reduction in error. In particular, RMSE and MAE were reduced by more than 98%, with $R^2$ values close to 1.0, demonstrating an almost perfect correspondence between the real and simulated dynamics. Full article