Search Results (97)

Reliable spatial georeferencing of mobile mapping platforms is a fundamental prerequisite for high-fidelity urban remote sensing products such as 3D point clouds and digital twins. However, in deep urban canyons, severe signal occlusion and multipath effects reduce visible GNSS satellites, causing ambiguity resolution (AR) failure and degraded observation geometry for UGV-borne systems. Conventional Vehicle-to-Vehicle (V2V) cooperation offers limited improvement due to symmetric ground-level occlusion. To overcome this, we propose an asymmetric GNSS/UWB fusion method that introduces Unmanned Aerial Vehicles (UAVs) as high-altitude dynamic spatial anchors to reconstruct the 3D observation geometry. Two contributions are presented: (i) an asymmetric heterogeneous stochastic model coupling carrier-to-noise ratio (C/N0) and elevation angle to handle the quality disparity between air and ground sensor links, preventing multipath contamination of high-fidelity UAV observations; and (ii) a dynamic baseline constrained least-squares algorithm integrating Ultra-Wideband (UWB) ranging to stabilize GNSS positioning under high-dynamic relative motion. Validated through high-fidelity simulations and field experiments, the method achieves a 98.2% AR success rate and sub-decimeter 3D accuracy under extreme occlusion (≤3 visible satellites), while urban-canyon tests demonstrate 100% positioning availability across all evaluated epochs and reduce the 95th-percentile 3D error from 7.25 m to 0.19 m under the tested single-UAV/single-UGV configuration. The framework supports smart city modeling, 3D reconstruction, and infrastructure monitoring. Full article

This study presents a probabilistic seismic fragility assessment of a jointed rock slope by integrating field characterization, incremental dynamic analysis (IDA), and numerical modeling. Dominant joint sets are identified through field mapping, and key discontinuity parameters are estimated for the Barton–Bandis non-linear shear strength criterion. Dynamic simulations are performed using the distinct element method with the continuously yielding (C-Y) joint model to capture progressive shear degradation. Twenty real earthquake ground-motion records are scaled incrementally to perform IDA, with critical block displacement and cumulative joint slip adopted as engineering demand parameters (EDPs). A probabilistic seismic demand model (PSDM) is developed to correlate peak ground acceleration (PGA) with EDPs. Kinematic analysis indicates that planar failure along joint set 1 is the most likely failure mechanism (90% probability), followed by wedge failure along the intersection of joint sets 1 and 2 (52%). Fragility curves are derived for three displacement-based damage states: minor (1 cm), moderate (5 cm), and severe (15 cm). The results demonstrate that seismic deformation is strongly controlled by discontinuity geometry and progressive joint slip, with the slope exceeding the severe damage state at PGA levels as low as 0.4 g, indicating high seismic vulnerability. This highlights the importance of integrating field characterization with dynamic numerical modeling for reliable seismic stability assessment of such discontinuous rock mass. Future work should incorporate larger datasets, in situ testing, and 3D modeling to enhance assessment reliability. Full article

Seismic excitations induce floor accelerations that can damage non-structural components and, in extreme cases, contribute to global structural failure. Although floor acceleration demands have been widely studied, their integration into probabilistic seismic performance and reliability frameworks remains limited within Performance-Based Seismic Design (PBSD). This study addresses this gap by proposing a reliability-based framework that incorporates the stochastic nature of floor accelerations through their probability density functions. Five-story steel and reinforced concrete (RC) buildings, designed according to Mexican codes, were analyzed using nonlinear dynamic simulations in PERFORM 3D under 33 ground motions corresponding to immediate occupancy (IO), life safety (LS), and collapse prevention (CP) levels. Structural reliability was quantified using the probability of failure ($p_f$) and the reliability index (β). Results show that peak accelerations occur at the roof level, with higher demands in the steel structure. For the IO level, β ranged from approximately 2.29 to values above 4.0 in steel buildings, while RC structures reached up to β ≈ 4.97. At LS and CP levels, RC buildings maintained β values generally above 3.0, whereas steel structures showed values as low as β ≈ 1.32. The Kernel distribution best captured response variability, reflecting high dispersion ($C.V.$ > 30%). The proposed framework enhances PBSD by linking acceleration demands with reliability-based decision-making. Full article

The utilization of unmanned aerial vehicle (UAV) relays has significantly improved the availability and reliability of free-space optical (FSO) communication links within airborne communication backhaul networks. This paper proposes an FSO/RF dual-hop backhaul network employing multiple UAV relays and investigates a joint optimization scheme for three-dimensional (3D) trajectories and resource allocation of multiple UAVs. In this scheme, network throughput is maximized by jointly optimizing three variables: the association between the UAVs and the ground stations (GSs), power allocation, and the UAVs’ trajectories. Moreover, to enhance the engineering applicability of this research, we systematically incorporate multi-dimensional practical constraints—including the motion of the AWACS, platform dynamics, information causality, co-channel interference, the influence of weather variations, and multi-UAV collision avoidance. Furthermore, to address this challenging mixed-integer non-convex optimization problem, an iterative algorithm is developed. This algorithm integrates the principles of block coordinate descent with successive convex approximation, thereby alternately optimizing the three variable blocks within each iterative cycle. Numerical simulations confirm that the proposed scheme achieves a substantial throughput improvement in the multi-UAV-assisted FSO/RF hybrid backhaul network in comparison with other benchmark schemes. Full article

Liquefaction-induced damages related to excess pore water pressure generation in soils and stiffness degradation significantly influence infrastructure and seismic ground response, requiring reliable experimental testing setups and validated numerical models for accurate assessment. This study investigates the free-field liquefaction behavior of saturated sands using the newly proposed ETILam (Enhanced Transparent Impermeable Laminar) soil container under 1 g shaking table conditions. Specimens composed of loose and dense saturated sands overlain by a dry sand layer were prepared and tested under two harmonic motions (0.1 g–2 Hz and 0.2 g–2 Hz), the second motion being two consecutive 6 s excitations. Dynamic response was evaluated through acceleration time histories, shear strains obtained through displacement measurements, excess pore water ratio (r_u), response spectra, transfer functions, and Fourier amplitude computations. Fully coupled effective stress analyses were performed in 2D and 3D using calibrated PM4Sand and P2PSand constitutive models. Experimental results showed limited liquefaction for the lower-amplitude motion, whereas the higher-amplitude motion triggered significant shear strains (up to 10%) and r_u values approaching 0.8, with depth-dependent dissipation patterns between sequential shakings. Numerical simulations reproduced acceleration amplitudes and general pore-pressure trends, with the 2D model providing closer agreement in both generation and dissipation behavior. The findings validate the ETILam container’s capability to simulate free-field liquefaction response and demonstrate that a well-calibrated 2D approach can reliably capture the essential features of the observed behavior. Full article

In today’s energy landscape, defined by the growing demand for sustainable energy generation technologies and the parallel need to advance internal combustion engine (ICE) architectures toward cleaner and more efficient solutions, the adoption of Free-Piston Linear Generator (FPLG) engines emerges as a highly promising approach. This innovative system enables the direct conversion of combustion-induced piston motion into electrical energy, eliminating the need for traditional crankshaft and connecting rod mechanisms. The FPLG concept facilitates efficient utilization of a broad spectrum of fuels—including methane, ethanol, LPG, gasoline, biodiesel, and hydrogen—by supporting variable compression ratio operation. This feature enhances operational flexibility and fuel adaptability, positioning the technology as a viable candidate for future energy transition scenarios. The absence of rotating mechanical components significantly reduces frictional losses, contributing to an overall increase in system efficiency. To accurately characterize and optimize engine performance, an extensive series of one-dimensional (1D) numerical simulations was performed under both free and controlled operating conditions. The resulting data enabled the development of semi-empirical models capable of predicting the dynamic behavior of the engine across a wide range of working scenarios. Finally, through a detailed parametric analysis, the optimal operating conditions were identified to maximize both net electric efficiency and electrical power output. These findings provide a solid ground for the design and implementation of FPLG engine systems in advanced power generation applications. Full article

This paper proposes a 3D terminal-constraint guidance framework for a UAV, modeled here as a missile-like attacker vehicle, that improves survivability against an incoming anti-air missile (AAM) while enforcing a prescribed terminal approach direction to a stationary ground target. The UAV uses Generalized Vector Explicit Guidance (GENEX) augmented by a rotating lateral sinusoidal bias that generates a barrel-roll-like evasive motion. The AAM employs classical proportional navigation (PNG). Both vehicles include a fifth-order binomial acceleration-command realization with explicit lateral saturation. Parametric simulations show that the proposed bias can increase survivability while maintaining terminal accuracy. Performance is primarily governed by the evasive frequency and amplitude, the guidance time constants, and the available lateral acceleration budget. Full article

Modern-day urban warehouses face exploding large inventory and tight spaces requiring fast, accurate, and safe stocktaking in a narrow aisle in a GPS-denied environment. This paper proposes a complete UAV-enabled framework performing real-time QR code scanning with inventory synchronization through a safety-aware trajectory generation for obtaining collision-free motion. A novel hybrid workflow integrating MATLAB/Simulink R2024b and Unreal Engine is used for dynamics and photorealistic rendering, alongside a real-time warehouse setup using drone cameras and 3D LiDAR coupled with a ground control station and live dashboard. The system in this paper was evaluated by testing with single and multi-UAV models across high-fidelity simulations and experiments. Results demonstrate simulated QR accuracy of approximately 95 to 96%, with experimental validation achieving between 86 and 90.5% due to real-world environmental factors. In experimental and simulation analysis, mean end-to-end latency remained under half a second, trajectory error range between 8 and 10 cm, and safety margins were consistently maintained throughout the test. It was further observed that multi-UAV coordination halved mission time compared to single-drone tests while keeping duplicate reads negligible, indicating a scalable and safe pipeline for industry application. Full article

This paper investigates the stability of telehandlers operating on inclined terrain through a sequential methodological approach. In a first stage, stability is assessed using quasi-static methods based on force and moment equilibrium, including the load transfer matrix and the stability pyramid. These approaches account for gravitational and inertial effects through equivalent external forces and moments applied at the global centre of gravity, enabling efficient evaluation of load redistribution and proximity to rollover thresholds under generalized quasi-static conditions. The application of these methods highlights intrinsic limitations when addressing structurally complex machines such as telehandlers equipped with a pivoting rear axle and evolving mass distribution due to boom motion. In particular, quasi-static approaches require a priori assumptions regarding the effective rollover axis and cannot fully capture the coupled geometric and contact interactions between rear axle articulation limits, centre of gravity migration, tyre–ground interface behaviour, and support polygon evolution. To overcome these limitations, a nonlinear dynamic multibody model based on the three-dimensional Bond Graph (3D Bond Graph) methodology is introduced. The model is implemented within a virtual tilt–rotation test platform and validated against experimental results obtained from ISO 22915-14 stability tests. The comparison confirms compliance with normative requirements and demonstrates that the dynamic framework captures condition-dependent rollover mechanisms and transitions between distinct virtual rollover axes that cannot be fully explained by quasi-static formulations. Unlike most previous studies, which focus on fixed configurations or forward-driving scenarios, the proposed framework analyzes stability evolution under spatial inclination while accounting for structural articulation constraints. The explicit identification of rollover axis transitions induced by rear axle articulation provides a deeper mechanistic interpretation of telehandler stability and supports the use of high-fidelity dynamic simulation as a complementary tool for test interpretation, experimental planning, and the development of predictive stability and operator assistance systems. Full article

Ground vehicles navigating uneven terrain must simultaneously guarantee motion safety and efficiency. Safety requires that the planned waypoints lie in highly traversable terrain, while ensuring vehicle reachability to these waypoints, which must be kinematically feasible. Efficiency demands fewer detours and smoother paths that avoid excessive vehicle acceleration and steering. However, existing path planning research for uneven terrain fails to comprehensively integrate vehicle kinematic constraints, terrain factors, path smoothness, rollover risk, and total path length. To address this problem, this paper proposes a novel navigation framework. It first integrates terrain slope, flatness, elevation variation, and sparsity to generate a 2D global terrain traversability cost map. Subsequently, a three-phase path planning algorithm integrates A*, guided Rapidly-exploring Random Tree (RRT), and our proposed Kinematic and Terrain-Aware Probabilistic Roadmap (KT-PRM) local re-planning algorithm, which jointly considers multiple factors including ground vehicle kinematic constraints, terrain factors, path smoothness, rollover risk, and path length. This three-phase combination delivers safe, smooth, and short global paths over uneven terrain within a relatively short planning time. Finally, Nonlinear Model Predictive Control (NMPC) is employed for path tracking in the framework. Experiments were conducted in both simulated and real-world uneven terrain environments. The results demonstrated that the three-phase path planning algorithm integrated with our proposed KT-PRM algorithm achieves comprehensive performance in generating safer, smoother, and shorter paths. Our proposed navigation framework achieves safer and more efficient navigation compared with existing navigation frameworks. Full article

Autonomous navigation in mobile robots operating in dynamic and partially known environments demands the coordinated integration of perception, decision-making, and control while ensuring stability, safety, and energy efficiency. This paper presents an integrated navigation framework for differential-drive mobile robots that combines deep learning-based visual perception, reinforcement learning (RL) for high-level decision-making, and a Lyapunov-based trajectory reference generator for low-level motion execution. A convolutional neural network processes RGB-D images to classify obstacle configurations in real time, enabling navigation without prior map information. Based on this perception layer, an RL policy generates adaptive navigation subgoals in response to environmental changes. To ensure stable motion execution, a Lyapunov-based control strategy is formulated at the kinematic level to generate smooth velocity references, which are subsequently tracked by embedded PID controllers, explicitly decoupling learning-based decision-making from stability-critical control tasks. The local stability of the trajectory-tracking error is analyzed using a quadratic Lyapunov candidate function, ensuring asymptotic convergence under ideal kinematic assumptions. Experimental results demonstrate that while higher control gains provide faster convergence in simulation, an intermediate gain value (K = 0.5I) achieves a favorable trade-off between responsiveness and robustness in real-world conditions, mitigating oscillations caused by actuator dynamics, delays, and sensor noise. Validation across multiple navigation scenarios shows average tracking errors below 1.2 cm, obstacle detection accuracies above 95% for human obstacles, and a significant reduction in energy consumption compared to classical A* planners, highlighting the effectiveness of integrating learning-based navigation with analytically grounded control. Full article

The integration of eye-tracking into consumer-grade virtual reality (VR) headsets presents a transformative opportunity for assessing user mental states within simulated, immersive environments. However, the validity of this built-in technology must be established against gold-standard real-world eye-tracking systems. This study employs a novel paradigm using a physically moving object to evaluate the accuracy of dynamic smooth pursuit, a key oculomotor function in mental state assessment. We rigorously validated the performance of the HTC Vive Pro Eye’s integrated eye-tracker against the Tobii Pro Glasses 3 using a high-precision OptiTrack motion capture system as ground-truth for object position. Eight participants completed both 2D and 3D gaze-tracking tasks. In the 2D condition, they tracked a dot on a screen, while in the 3D condition, they tracked a physically moving object. The real-world object trajectories captured by OptiTrack were replicated within a VR environment. Gaze data from both the VR headset and the Tobii glasses were recorded simultaneously and compared to the OptiTrack baseline using Dynamic Time Warping (DTW) to quantify accuracy. Results revealed a task-dependent performance. In the 2D task, the Tobii glasses demonstrated significantly lower DTW distances, indicating superior accuracy. Conversely, in the 3D task, the VR headset significantly outperformed the glasses, showing a closer match to the real object trajectory. This suggests that while traditional eye-trackers excel in constrained 2D contexts, integrated VR eye-tracking is more accurate for naturalistic 3D gaze pursuit. We conclude that VR headset eye-tracking is not only a reliable but also a cost-effective tool for research, particularly offering enhanced performance for studies conducted within immersive 3D simulations. Full article

The behavior of shield tunnel lining structures is known to be influenced by segmental joints. Most studies conducted in this area use simplified models, which may not properly simulate the behavior of the segmental joints. This study utilizes a full-reinforced concrete segment model to rigorously investigate the seismic behavior of joints in a segmental tunnel lining, explicitly accounting for segment–segment contact, interaction, and joint bolts. Specifically, a comprehensive full dynamic analysis of a two-dimensional (2D) lining–soil model, incorporating nonlinear constitutive models for both concrete (CDPM) and soil (Mohr–Coulomb), was conducted to investigate the effects of joint bolt type, seismic intensity, and vertical excitation component on the seismic response. The lining–soil model was excited using three ground motions. The results indicate that the joint rotation is significantly influenced by the amplitude and frequency content of ground motions, which has implications for the watertightness of the gasketed joint. In particular, including the vertical component of the excitations was found to increase the diametral deformation by at least 150% and tended to increase other structural responses. Moreover, the bolt tension increased significantly by over 400% with only a 150% increase in seismic intensity, highlighting the strong nonlinear sensitivity. However, due to the inherent constraints of the 2D plane-strain assumption, the influence of the bolt type remains inconclusive. Full article

On 7 January 2025, an Mw7.1 earthquake struck Dingri County, Tibet, causing severe damage in a high-altitude, sparsely instrumented region where traditional damage assessment methods are limited. To address this, we developed an integrated "source simulation–nighttime light validation" framework. First, a kinematic source model (constrained by InSAR and teleseismic data) and the Unified Seismic Tomography models for continental China lithosphere 2.0 (USTClitho2.0) velocity model were used with the curved-grid finite difference method to simulate high-resolution ground motion and intensity fields. Second, NASA Black Marble (VNP46A2) nighttime light data, processed with the Block-Matching and 3D filtering (BM3D) algorithm, were analyzed to compute pixel-level radiance changes and township-level total nighttime light loss rates (TNLR). The results reveal a high spatial consistency between simulated high-intensity zones and areas of significant light loss. For instance, Mangpu Township, within a simulated high-intensity zone, exhibited a TNLR of 44.7%. This demonstrates that nighttime light remote sensing can effectively validate physical simulations in areas lacking dense seismic networks. Our framework provides a novel, complementary methodology for rapid and reliable post-earthquake damage assessment in high-mountain, data-sparse regions. Full article

Unmanned aerial vehicles (UAVs) are vital for surveillance missions requiring the geolocation of moving ground targets, yet small, resource-constrained platforms often lack integrated, robust systems that can handle disturbances such as wind, occlusions, and noise. This paper presents an integrated, end-to-end vision-based geolocation pipeline specifically designed for embedded deployment on resource-constrained UAVs with gimbal cameras. Starting from a rough initial position estimate, pan/tilt angles are computed to orient the gimbal, and then a visual tracking module combining object detection (via Tiny-YOLO) and feedback control (using CSRT) centers the target in the frame. The target’s absolute position is derived from UAV inertial data and gimbal angles. To mitigate noisy or unavailable direct geolocation due to disturbances or visual lock loss, Kalman filtering is integrated with a unicycle-based motion model. Both an extended Kalman filter (EKF) and unscented Kalman filter (UKF) are evaluated and tuned in high-fidelity simulations, with the UKF demonstrating superior performance by reducing the 2D position RMSE by 33% compared to the EKF in occlusion scenarios. The system is implemented on embedded hardware and validated through real flight tests, establishing the operational capability of vision-based surveillance on small UAV platforms. Full article