Search Results (233)

To address the problems of closed-loop task organization, strong corridor constraints, and path failure after local disturbances in unmanned aerial vehicle (UAV) inspection of gas-insulated switchgear (GIS) rooms, this paper proposes a topology-and-corridor-guided bias-suppressed D* (TCG-BS-D*) method for closed-loop three-dimensional (3D) path planning and local replanning. The proposed method constructs a structured guidance model based on the inspection-corridor topology, generates local 3D path segments according to a predetermined inspection sequence, and forms a nominal closed-loop inspection path through bias suppression and path regularization. Meanwhile, for local maintenance blockage and dynamic disturbance scenarios, an alternative local replanning strategy is applied to the affected path segments. Simulation results show that, under the static closed-loop inspection condition, the proposed method achieves a total path length of 700.22 m, a total inspection time of 269.32 s, an average safety clearance of 8.18 m, 37 large-angle turns, a corridor adherence rate of 80.73%, and a task completion rate of 100%, showing superior performance in inspection efficiency, safety margin, trajectory regularity, and corridor consistency. Under the local blockage condition, the replanned path introduces path-length and time increments of 71.29 m and 25.88 s, respectively, while maintaining the minimum safety clearance at 1.52 m and increasing the corridor adherence rate to 83.91%. Under dynamic disturbance conditions, the minimum dynamic safety clearance is improved from −2.71 m to 17.84 m, effectively eliminating the local dynamic collision risk. The results demonstrate that the proposed method can balance closed-loop path-generation efficiency, corridor-structure consistency, safety margin, and adaptability to local disturbances, providing an effective solution for UAV inspection path planning in GIS rooms. Full article

Diffusion models for 3D genome structures make inference an ensemble-generation and tiling problem. In the released ChromoGen workflow, millions of independent denoising trajectories are executed through a single-GPU path, while overlapping genomic windows are sampled without enforcing consistency of their shared physical interval. We introduce ParaChromo, a parallel inference framework for conditioned, tiled 3D genome diffusion workloads built around the trained diffusion U-Net and distance-map interface. ParaChromo organizes the workload into three inference-layer modules: a workload-dispatch module schedules region, guidance, and sample chunks across worker groups; an encoder-aware sharded-conditioning module scales and shards the EPCOT front end with FSDP while keeping the inner-loop U-Net replicated; and a seam-coherent tiled-synchronization module projects the shared 12-bead overlap of adjacent reverse chains in distance-map space. On eight A6000 GPUs, the combined reduced-step and task-parallel systems path raises throughput from $2.356\pm 0.003$ to $235.71\pm 1.120$ samples/s, a $100.04\pm 0.486$-fold gain over the released single-GPU baseline. The reduced-step setting is supported by a sweep from 50 to 1000 DDIM steps, where distance-distribution and Hi-C-based metrics remain stable across four chromosomes. For the synchronization module, the chr22 seam discrepancy falls from 150.9 pm to 7.9 pm, while matched internal and Hi-C-based quality metrics are preserved. The synchronized chr22 run also gives a chromosome-scale coordinate rendering over 32 paper-aligned tiles. Together, these results show that conditioned, tiled 3D genome diffusion can be executed as a scalable workload when throughput parallelism, sampler length, encoder placement, and spatial consistency are treated as separate but compatible constraints. Full article

The transition toward renewable-dominated power systems introduces significant complexity and nonlinearity, rendering traditional mechanism-based modeling computationally prohibitive for real-time security assessment. While data-driven approaches offer computational efficiency, they fundamentally lack physical interpretability and often exhibit generalization failures under rare, large-signal disturbances due to the absence of intrinsic physical constraints. To bridge this gap, this paper proposes a Physics-Guided Symbolic Regression (PGSR) framework for constructing interpretable and robust dynamic equivalent models. The methodology embeds domain knowledge via topological masks and dimensional consistency rules to restrict the evolutionary search space to physically admissible manifolds. A multi-resolution extraction strategy based on the Pareto frontier is developed to autonomously identify both linear small-signal models and nonlinear large-signal formulations adaptable to varying analytical requirements. Furthermore, a post hoc verification stage based on Lyapunov stability theory ensures the dynamic validity and energy dissipation properties of the generated equations. A case study on the WSCC 9-bus system demonstrates that the proposed method accurately recovers the underlying Taylor-series structure of swing equations and significantly outperforms four data-driven baselines—including polynomial, kernel, and neural network models—in out-of-distribution generalization, achieving 12–42× lower trajectory error under unseen large perturbations. Full article

Planning a low-detectability route for a fixed-wing UAV in mountainous environments with radar-based sensing constraints remains highly challenging. Conventional approaches struggle to simultaneously ensure both path quality and operational safety. To address this problem, this paper proposes a two-layer planning framework in which a Risk-A* algorithm provides a global reference route, while a model predictive control (MPC) scheme performs online receding-horizon trajectory optimization. The proposed method combines prior radar-platform information with time-varying detection-risk cues to generate terrain-masked and detection-feasible trajectories. In this study, the framework is instantiated and evaluated on a representative fixed-wing medium-altitude long-endurance (MALE) UAV, where “medium-altitude” denotes the platform class rather than the flight altitude maintained during the low-altitude flight segment. As a result, the UAV can complete the entire flight while reducing the detection-risk metric and overall planning cost. Simulation results on two DEM-based mountainous terrain zones, with one nominal start-goal pair specified in each terrain zone and 50 repeated executions conducted for each scenario, demonstrate that the Risk-A*-MPC framework may yield slightly longer paths and flight times; however, it consistently satisfies the no detection-threshold-exceedance requirement under the tested conditions. In the two main terrain-zone scenarios, the recorded maximum MPC solve time was 0.812 s, which remained below the 3 s control update period and supports the real-time executability of the online MPC layer on the tested computational platform. Full article

Informal waste picking and scrap collection constitute critical yet highly precarious livelihood strategies among economically marginalised women in rural South Africa. This article presents a cross-sectional mixed-methods study, guided by Sen’s Capability Approach as its analytical framework, examining the lived experiences, motivations, and livelihood outcomes of 126 Black African women engaged in scrap collection along the N2 Highway in the Eastern Cape, specifically in Mthatha, Xhora, and Qumbu. The study integrates quantitative descriptive statistics with qualitative thematic analysis derived from structured interviewer-administered questionnaires. The findings indicate that participation in scrap collection is overwhelmingly driven by structural economic constraints, including chronic unemployment, household poverty, and extensive caregiving responsibilities, rather than autonomous occupational choice. The sample is characterised by limited educational attainment, frequently disrupted by poverty, bereavement, early marriage, and early caregiving roles, which collectively constrain access to formal employment opportunities. Participants consistently described scrap collection as physically hazardous, economically insecure, and detrimental to both physical health and psychosocial wellbeing, while remaining indispensable for household survival. Through the lens of the Capability Approach, these conditions reflect severe restrictions in substantive freedoms, particularly in relation to economic security, bodily health and human dignity. Expressions of acceptance are interpreted as manifestations of adaptive preferences formed under conditions of prolonged structural deprivation rather than indicators of genuine agency. The study contributes to informal economy scholarship by demonstrating how intersecting structural inequalities constrain capability sets and limit livelihood trajectories and calls for targeted policy interventions to enhance occupational safety, income security and access to sustainable livelihood alternatives. Full article

In characterizing unconventional reservoirs, conventional Digital Rock Physics (DRP) has long been constrained by three fundamental bottlenecks: the trade-off between imaging resolution and field of view, challenges in reconstructing multiscale pore topology, and the prohibitive computational cost of direct numerical simulation (DNS) at the pore scale. The deep integration of artificial intelligence and rock physics has given rise to a new paradigm—Intelligent Digital Rock Physics (IDRP). This paper provides a systematic review of the evolutionary trajectory of IDRP, with a focus on how machine learning is reshaping the end-to-end workflow from imaging and segmentation to reconstruction and simulation. First, we survey image super-resolution and 3D pore structure generation techniques based on convolutional neural networks (CNNs), generative adversarial networks (GANs), and diffusion models, elucidating their mechanisms for surpassing optical diffraction limits and incorporating macroscopic petrophysical constraints. Second, we outline algorithmic strategies for fusing multi-source heterogeneous data (e.g., Micro-CT and SEM) and representing dual-porosity or multi-continuum systems. Third, we critically examine the application of machine learning surrogates in single- and multiphase flow prediction, highlighting how physics-informed machine learning (PIML) and reinforcement learning (RL)—by embedding governing equations such as Navier–Stokes or Muskat–Leverett into loss functions—achieve both computational acceleration and physical consistency. We further identify key limitations of current IDRP approaches, including insufficient validation of generated topological realism, narrow generalization across lithologies, inadequate representation of dynamic wettability, and limited model interpretability. Finally, we propose a forward-looking roadmap centered on multimodal foundation models for rocks, coupled with neural operators and uncertainty quantification frameworks, emphasizing the critical pathways for translating IDRP into engineering digital twins for unconventional hydrocarbon development, coalbed methane production enhancement, Enhanced Geothermal Systems, and geological CO₂ storage. This review offers a comprehensive reference for researchers at the intersection of geophysics, rock mechanics, and artificial intelligence. Full article

To autonomously dock LNG unloading arms under adverse sea states, this study formulates a dynamic docking process as a trajectory forecasting task. By integrating visual-perception-based spatial localization with trajectory acquisition and forecasting, a comprehensive operational pipeline is established. To predict the dynamic trajectory of the vessel flange, an improved KSE-GRU model is proposed. By extracting implicit kinematic features, the model effectively enhances trajectory characterization under extreme sea states, thereby significantly improving forecasting accuracy and worst-case error constraints. To ensure the operational feasibility of autonomous docking, a robust control strategy is introduced to complement the trajectory predictions. The experimental results demonstrate that the proposed model outperforms traditional time-series forecasting models across all evaluation metrics. Compared with the baseline neural network models, the Mean-3D error is reduced by 19.14%, and the Max-3D error is capped at 348.77 mm, representing an 8.8% improvement over the baseline. Furthermore, the model demonstrates clear advantages in maintaining trajectory consistency and forecasting reliability. In summary, in this study, a robust trajectory forecasting model is developed for vessel target flanges integrated with a comprehensive control framework, thereby offering a practical approach to autonomous docking under dynamic oceanic conditions. Full article

With growing interest in automated dismantling operations for hazardous environments, automatically planning safe and efficient disassembly sequences is becoming increasingly important. When a large structure is segmented into parts, the removal order must ensure that each part can be extracted safely without destabilising the remaining structure. This paper presents a comparative study of four algorithms for solving the disassembly sequencing problem in two dimensions: First Feasible Random Search (FFRS), Greedy Search (GS), Height-Decreasing Search (HDS), and Stochastic Tree Search (STS). The present study focuses specifically on sequencing feasibility under geometric and physical constraints, namely connectivity, accessibility, and structural stability. The 2D formulation provides a simplified yet computationally efficient testbed for analysing algorithmic behaviour under varying cutting complexities, with the objective of minimising the total removal trajectory length. Results show that while STS consistently finds optimal or near-optimal solutions, its factorial runtime limits scalability. GS produces high-quality solutions efficiently but can become trapped in infeasible configurations, whereas HDS offers strong reliability and speed at the expense of solution quality. Based on these findings, a hybrid height-based backtracking algorithm is proposed as a promising future direction, combining the efficiency of greedy search with the robustness of stochastic exploration. The results provide insight into the relative strengths and limitations of different sequencing strategies and establish a foundation for future extension to more realistic dismantling scenarios, including 3D and radiologically constrained applications. Full article

Stable three-dimensional hand landmark reconstruction using low-cost RGB-D sensors is important for human–computer interaction, robot teleoperation, and vision-based motion analysis. RGB-based hand landmark detectors provide stable semantic 2D landmarks, but their depth output is not a metric measurement in the physical camera coordinate system. Stereo cameras can provide metric depth, but direct landmark-level back-projection is sensitive to invalid pixels, local depth holes, boundary noise, and partial occlusion. To address these problems, this paper presents a lightweight RGB-D sensing front-end that combines MediaPipe semantic hand landmarks with ZED2 stereo depth. The proposed pipeline detects 21 semantic hand landmarks in the RGB image, obtains landmark-level metric depth from the aligned ZED2 depth map using local median sampling, reconstructs 3D landmarks by camera back-projection, and further applies exponential moving average filtering and a bone-length consistency constraint. Experiments were conducted on a self-collected SVO dataset containing 13 hand actions and 26 recorded sequences, and an additional checkerboard-based reference-distance validation was performed to evaluate the metric depth sampling and 3D back-projection component. Compared with single-pixel sampling, the $5\times 5$ local median strategy slightly increased the valid-depth ratio from 0.9731 to 0.9738 and reduced the temporal smoothness metric from 1.7163 mm to 1.6902 mm. To further justify the temporal filtering choice, an additional comparison with the 1 Euro Filter was conducted using the reconstructed win5 trajectories. The 1 Euro Filter produced stronger smoothing, reducing the temporal smoothness metric to 0.196 mm, but also reduced the path-length ratio to 0.484, indicating substantial motion attenuation. EMA0.7 was therefore retained as a more balanced setting, reducing the temporal smoothness metric to 0.826 mm while maintaining a path-length ratio of 0.803. The BL0.5 bone-length constraint reduced the bone-length standard deviation from 2.0727 mm to 1.1995 mm with limited trajectory modification. The final configuration provides a practical low-cost RGB-D front-end for stable 3D hand landmark reconstruction under controlled indoor conditions. Full article

This study investigates offline trajectory planning strategies for multi-UAV missions in complex 3D environments, with the aim of systematically comparing classical, hybrid, and reinforcement learning-based approaches under unified evaluation conditions. Two simulation scenarios were considered: an uneven terrain environment with elevation-induced constraints and a planar obstacle-rich environment. The evaluated planners include graph-based (A*), sampling-based (RRT, RRT*), gradient-based (APF), a hybrid APF B-RRT* method, and a DQN-based reinforcement learning planner with spatial attention and reward shaping. Performance was assessed using geometric, safety, energetic, and computational metrics. The results show that A* consistently produces the shortest and most stable trajectories with low energy consumption but at increased computational cost in high-resolution environments. Sampling-based planners exhibit higher variability and planning time, while APF achieves computational efficiency but may violate safety margins. The hybrid planner provides improved robustness across scenarios. The reinforcement learning planner demonstrates consistent safety compliance and strong inter-UAV separation in both environments, also with longer trajectories and higher energy usage. Overall, the study highlights trade-offs between determinism, scalability, safety, and adaptability across planning paradigms. Full article

While Unmanned Aerial Systems (UAS) are increasingly used for infrastructure inspection, a critical gap exists between optimized path planning and reliable real-world execution. Fully autonomous flights face regulatory constraints and environmental risks, whereas manual piloting introduces inconsistencies that compromise data quality. To address this gap, this study proposes a human-in-the-loop assistive navigation platform that enables pilots to follow preplanned inspection trajectories while maintaining manual control. The proposed system integrates an Augmented Reality (AR)-based guidance module that provides real-time viewpoint localization with a mesh-coupled quality monitoring module that continuously evaluates view redundancy and triangulation uncertainty. A proof-of-concept field demonstration through an on-site façade inspection example indicates that the proposed platform has the potential to improve the consistency of viewpoint distribution, achieving closer adherence to planned spacing and stand-off distance. This results in more uniform spatial sampling, enhanced view redundancy, and reduced variability in theoretical uncertainty, leading to improved geometric conditions for Structure-from-Motion (SfM) reconstruction. Overall, the field demonstration highlights the potential of combining computational guidance with human decision-making to support reliable and high-quality UAS-based infrastructure inspection. Full article

Under the trend of intelligent transportation and connected vehicles, real-time control plays a vital role in improving bus system efficiency. Existing bus control strategies typically treat buses as homogeneous points and achieve system equilibrium by maintaining consistent headways. However, this simplification overlooks differences in dynamic responses and the evolution of powertrain lifespan arising from vehicle heterogeneity. It converts the sparse constraint problem, which is intended to ensure timely arrival, into a hard constraint on the vehicle trajectory over the entire time horizon, thereby excessively restricting individual optimal evolutionary paths and causing the optimization process to become trapped in a local optimum. To this end, this paper proposes SMATD3, a multi-agent cooperative control algorithm that accounts for vehicle heterogeneity. By adopting a centralized training and decentralized execution paradigm and avoiding the specification of a fixed inter-vehicle spacing target, the algorithm enables each vehicle to adaptively adjust its speed control strategy according to its own dynamic characteristics, thereby achieving the coordinated optimization of system equilibrium and individual objectives. The simulation results indicate that the proposed method can effectively suppress bus tailgating and achieve the coordinated multi-objective optimization of operational stability, passenger travel efficiency, energy consumption, and battery health. From a sustainability perspective, improved headway regularity and service reliability can enhance public transit attractiveness and support mode shift, while smoother energy use and reduced battery degradation lower lifecycle impacts. Full article

Cooperative localization for multi-Unmanned Aerial Vehicle (UAV) systems in GPS-degraded environments is often compromised by ideal-communication or uniform-quality assumptions. This paper proposes Quality-Aware Distributed State Estimation (QA-DSE), which combines three operational quality factors—freshness (Age of Information), accuracy (covariance trace), and link reliability (packet loss and channel noise)—into a single multiplicative score $q_{ij}$, modulated by a bounded history-consistency factor based on velocity-propagated self-trajectory continuity. A dual-constraint AND-gate on AoI and covariance trace excludes jointly degraded neighbors, while admitted neighbors are fused through a quality-squared information-matrix update under a stated bounded residual cross-correlation assumption, with an adaptive Covariance-Intersection fallback when the assumption is stressed. Under explicit observability, bounded-noise, bounded-quality, joint-connectivity, and bounded residual cross-correlation assumptions, we establish mean-square bounded error, exponential convergence at a rate inherited from the Kalman update operator, $O\left(n^3+nm\right)$ per-step complexity, Bounded-Input Bounded-Output (BIBO) stability, soft attenuation of single-axis faults (Theorem 4), and hard exclusion under joint AoI–covariance violation (Theorem 5). Under a Ultra-Wideband (UWB)-style cooperative-observation model, Monte Carlo experiments across five scenarios show 74.08–74.24% position- Root Mean Square Error (RMSE) reductions over Covariance Intersection, with the relative advantage held within 73.04–74.24% as the fleet scales from 3 to 50 UAVs; QA-DSE remains within 8.1% of an idealized no-cooperation single-vehicle Kalman filter, demonstrating graceful degradation rather than improvement above that floor. Per-step Central Processing Unit (CPU) time scales from 0.09 ms (5 UAVs) to 0.31 ms (50 UAVs); embedded validation is left to future work. Full article

This paper presents a robust dual-loop control strategy for the lateral motion and heading-angle regulation of an autonomous vehicle equipped with a Steer-By-Wire (SBW) system under unknown time-varying disturbances. The proposed framework comprises a fuzzy PID controller in the inner loop to generate the motor torque and track the front-wheel steering angle, and an optimal backstepping controller in the outer loop—integrated with a finite-time disturbance observer—to ensure lateral trajectory tracking and wind-disturbance rejection. The PID gains are tuned online by a Mamdani-type fuzzy inference system, while the backstepping parameters are optimized offline via a genetic algorithm. Beyond the bicycle-model-based design, the controller is evaluated through supplementary simulations using a 6-degree-of-freedom (6-DOF) vehicle model, as well as through a detailed robustness analysis that includes measurement noise and increasing lateral disturbance forces. The results demonstrate that the closed-loop system achieves precise path tracking, finite-time convergence of both tracking and estimation errors, and effective compensation of road vibrations and wind disturbances. Furthermore, the controller maintains stable performance under significant measurement noise and tolerates lateral disturbance forces up to at least 10,000 N without violating safety constraints. The effectiveness of the proposed method is consistently confirmed across both the reduced-order bicycle model and the higher-fidelity 6-DOF validation environment. Full article

The rapid expansion of Unmanned Aircraft System (UAS) operations has created an urgent need for scalable strategic conflict resolution methods within the U-space framework. When requested 4D flight plans overlap with previously authorised ones, the Flight Authorisation Service (FAS) denies the request, and can provide the UAS operator with an alternative route, free of conflict. This work introduces a Machine Learning-based tool designed to support this process, which consists of three sequential phases. First, an Octree spatial partitioning technique is proposed, discretising the airspace, further identifying the previously occupied cells and visualising the occupied airspace, so that the UAS operator can manually find an alternative route. Then, the widely known A* pathfinding algorithm is implemented in this discretized airspace, allowing the shortest or most optimal conflict-free alternative route. Finally, the methodology integrates a Machine Learning (Reinforcement Learning) model, created from scratch and trained with realistic flight trajectories from a PX4 Simulator, to further optimise flight paths, explicitly accounting for operational constraints such as distance and battery consumption. In this work, both methods are compared, addressing traditional algorithms limitations with Machine Learning (ML) techniques, showing that a near-optimal behaviour can be achieved with the ML approach, at a fraction of the computation time needed. Full article