Search Results (65)

Managing vehicle speed is crucial for reducing crash risks and crash severity. South Korea’s ‘Safety Speed 5030’ policy introduced lower urban speed limits to enhance road safety, but speed limit reductions alone may not be sufficient to change driver behavior. This paper investigates how spatial and environmental factors beyond speed limits affect drivers’ speed choice. Using point-level speed data from Jeju Island’s C-ITS dataset combined with GIS information, spatial econometric techniques were employed to capture spatial dependencies in speeding degree. Results show that a spatial lag model (SLM) outperforms ordinary least squares (OLS) and spatial error models (SEMs), providing higher explanatory power and more consistent parameter estimates. Key factors influencing drivers’ speed choice include road geometry (e.g., curvature, number of lanes), node-level features (e.g., intersections, property change points), and the presence of enforcement measures. The findings suggest that the reduction in speed limits alone may not guarantee a corresponding decrease in vehicle speed. This underlines that sustainable traffic safety requires not only regulation but also careful consideration of spatial and environmental contexts. Full article

In this paper, we propose a navigation synthesis method for indoor mobile robots based on the Improved Jumping Point Search (JPS) framework. Although traditional JPS has high search efficiency, it often leads to excessive node expansion and sharp turns in complex environments, which limits its practical application. In order to overcome these problems, we introduced three key strategies. First, we used a density-sensing heuristic function calculated by integrating the image to improve the adaptability of complex areas. Secondly, we extracted structural key points from the path and used third-order B-splines to fit them to enhance smoothness and continuity. Third, a curvature-driven Regulated Pure Pursuit (RPP) controller adjusts the look-ahead distance and speed based on path curvature, improving tracking stability. Simulation results show that the proposed method reduces planning time and node redundancy while generating smoother and more executable paths than the conventional JPS framework. Full article

Due to the complex spatial structure, dense tree distribution, and narrow passages in orchard environments, traditional path planning algorithms often struggle with large path deviations, frequent turning, and reduced navigational safety. In order to overcome these challenges, this paper proposes a hybrid path planning algorithm based on improved D* Lite for narrow forest orchard environments. The proposed approach enhances path feasibility and improves the robustness of the navigation system. The algorithm begins by constructing a 2D grid map reflecting the orchard layout and inflates the tree regions to create safety buffers for reliable path planning. For global path planning, an enhanced D* Lite algorithm is used with a cost function that jointly considers centerline proximity, turning angle smoothness, and directional consistency. This guides the path to remain close to the orchard row centerline, improving structural adaptability and path rationality. Narrow passages along the initial path are detected, and local replanning is performed using a Hybrid A* algorithm that accounts for the kinematic constraints of a differential tracked robot. This generates curvature-continuous and directionally stable segments that replace the original narrow-path portions. Finally, a gradient descent method is applied to smooth the overall path, improving trajectory continuity and execution stability. Field experiments in representative orchard environments demonstrate that the proposed hybrid algorithm significantly outperforms traditional D* Lite and KD* Lite-B methods in terms of path accuracy and navigational safety. The average deviation from the centerline is only 0.06 m, representing reductions of 75.55% and 38.27% compared to traditional D* Lite and KD* Lite-B, respectively, thereby enabling high-precision centerline tracking. Moreover, the number of hazardous nodes, defined as path points near obstacles, was reduced to five, marking decreases of 92.86% and 68.75%, respectively, and substantially enhancing navigation safety. These results confirm the method’s strong applicability in complex, constrained orchard environments and its potential as a foundation for efficient, safe, and fully autonomous agricultural robot operation. Full article

This study investigates three-dimensional two-phase flows in complex geometries found in industrial process equipment design using finite-element numerical simulations. The governing equations are formulated in three-dimensional Cartesian coordinates and solved on unstructured meshes employing the Taylor–Hood “Mini” element, selected for its numerical stability and convergence properties. The convective term in the momentum equation is discretized using a first-order semi-Lagrangian scheme. The two fluid phases are separated by an interface mesh composed of triangular surface elements, which is independent of the primary volumetric fluid mesh. Surface tension effects are incorporated as a source term using the continuum surface force (CSF) model, with the curvature computed via the Laplace–Beltrami operator. At each time step, the positions of the interface mesh nodes are updated according to the local fluid velocity field. The results show that the methodology is stable and can be used to accurately model two-phase flows in complex geometries found in several engineering solutions. Full article

The advancement of mobile robot technology has made path planning a necessary condition for autonomous navigation, but traditional algorithms have issues with efficiency and reliability in dynamic and unstructured environments. This study proposes a Dynamic Hybrid A* (DHA*)–Adaptive Dynamic Window Approach (ADA-DWA) fusion algorithm for efficient and reliable path planning in dynamic unstructured environments. This paper improves the A* algorithm by introducing a dynamic hybrid heuristic function, optimizing the selection of key nodes, and enhancing the neighborhood search strategy, and collaboratively optimizes the search efficiency and path smoothness through curvature optimization. On this basis, the local planning layer introduces a self-adjusting weight-adaptive system in the DWA framework to dynamically optimize the speed, sampling distribution, and trajectory evaluation metrics, achieving a balance between obstacle avoidance and environmental adaptability. The proposed fusion algorithm’s comprehensive advantages over traditional methods in key operational indicators, including path optimality, computational efficiency, and obstacle avoidance capability, have been widely verified through numerical simulations and physical platforms. This method successfully resolves the inherent trade-off between efficiency and reliability in complex robot navigation scenarios, providing enhanced operational robustness for practical applications ranging from industrial logistics to field robots. Full article

This paper addresses the limitations of the A* algorithm in underground roadway path planning, such as proximity to roadway boundaries, intersection with obstacle corners, trajectory smoothness, and timely obstacle avoidance (e.g., fallen rocks, miners, and moving equipment). To overcome these challenges, we propose an improved path planning algorithm integrating an enhanced A* method with an improved Dynamic Window Approach (DWA). First, a diagonal collision detection mechanism is implemented within the A* algorithm to effectively avoid crossing obstacle corners, thus enhancing path safety. Secondly, roadway width is incorporated into the heuristic function to guide paths toward the roadway center, improving stability and feasibility. Subsequently, based on multiple global path characteristics—including path length, average curvature, fluctuation degree, and direction change rate—an adaptive B-spline curve smoothing method generates smoother paths tailored to the robot’s kinematic requirements. Furthermore, the global path is segmented into local reference points for DWA, ensuring seamless integration of global and local path planning. To prevent local optimization traps during obstacle avoidance, a distance-based cost function is introduced into DWA’s evaluation criteria, maintaining alignment with the global path. Experimental results demonstrate that the proposed method significantly reduces node expansions by 43.79%, computation time by 16.28%, and path inflection points by 80.70%. The resultant path is smoother, centered within roadways, and capable of effectively avoiding dynamic and static obstacles, thereby ensuring the safety and efficiency of underground robotic transport operations. Full article

To assess structural sealant damage in hidden frame glass curtain walls (HFGCWs) during service, damage states were simulated by controlled cutting with varying incision lengths. Quantitative identification challenges were investigated through natural frequency and curvature modal difference (CMD) analyses at multiple test points. The results indicate that natural frequency decreases with increasing damage severity, while the first-order curvature mode difference (FCMD) exhibits localized abrupt changes in damaged regions. Boundary modes provide more targeted and accurate damage identification. The peak value of the FCMD mutation region enables precise damage localization. A quantitative damage identification threshold of 0.1205 was derived from FCMD distribution characteristics in boundary regions. By leveraging boundary mode features, modal testing efficiency is optimized, reducing the required acquisition nodes and effectively guiding structural sealant damage detection in engineering applications. Full article

The precise extractions of tree components such as wood (i.e., trunk and branches) and leaves are fundamental prerequisites for obtaining the key attributes of trees, which will provide significant benefits for ecological and physiological studies and forest applications. Terrestrial laser scanning technology offers an efficient means for acquiring three-dimensional information on tree attributes, and has marked potential for extracting the detailed tree attributes of tree components. However, previous studies on wood–leaf separation exhibited limitations in unsupervised adaptability and robustness to complex tree architectures, while demonstrating inadequate performance in fine branch detection. This study proposes a novel unsupervised model (NE-PC) that synergizes geometric features with graph-based path analysis to achieve accurate wood–leaf classification without training samples or empirical parameter tuning. First, the boundary-preserved supervoxel segmentation (BPSS) algorithm was adapted to generate supervoxels for calculating geometric features and representative points for constructing the undirected graph. Second, a node expansion (NE) approach was proposed, with nodes with similar curvature and verticality expanded into wood nodes to avoid the omission of trunk points in path frequency detection. Third, a path concatenation (PC) approach was developed, which involves detecting salient features of nodes along the same path to improve the detection of tiny branches that are often deficient during path retracing. Tested on multi-station TLS point clouds from trees with complex leaf–branch architectures, the NE-PC model achieved a 94.1% mean accuracy and a 86.7% kappa coefficient, outperforming renowned TLSeparation and LeWos (ΔOA = 2.0–29.7%, Δkappa = 6.2–53.5%). Moreover, the NE-PC model was verified in two other study areas (Plot B, Plot C), which exhibited more complex and divergent branch structure types. It achieved classification accuracies exceeding 90% (Plot B: 92.8 ± 2.3%; Plot C: 94.4 ± 0.7%) along with average kappa coefficients above 80% (Plot B: 81.3 ± 4.2%; Plot C: 81.8 ± 3.2%), demonstrating robust performance across various tree structural complexities. Full article

For mobile agent path planning, traditional path planning algorithms frequently induce abrupt variations in path curvature and steering angles, increasing the risk of lateral tire slippage and undermining operational safety. Concurrently, conventional reinforcement learning methods struggle to converge rapidly, leading to an insufficient efficiency in planning to meet the demand for energy economy. This study proposes LSTM Bézier–Double Deep Q-Network (LB-DDQN), an advanced path-planning framework for mobile agents based on deep reinforcement learning. The architecture first enables mapless navigation through a DDQN foundation, subsequently integrates long short-term memory (LSTM) networks for the fusion of environmental features and preservation of training information, and ultimately enhances the path’s quality through redundant node elimination via an obstacle–path relationship analysis, combined with Bézier curve-based trajectory smoothing. A sensor-driven three-dimensional simulation environment featuring static obstacles was constructed using the ROS and Gazebo platforms, where LiDAR-equipped mobile agent models were trained for real-time environmental perception and strategy optimization prior to deployment on experimental vehicles. The simulation and physical implementation results reveal that LB-DDQN achieves effective collision avoidance, while demonstrating marked enhancements in critical metrics: the path’s smoothness, energy efficiency, and motion stability exhibit average improvements exceeding 50%. The framework further maintains superior safety standards and operational efficiency across diverse scenarios. Full article

The efficiency and smoothness of path planning algorithms are critical factors influencing their practical applications. A traditional A* algorithm suffers from limitations in search efficiency, path smoothness, and obstacle avoidance. To address these challenges, this paper introduces an improved A* algorithm that integrates the Salp Swarm Algorithm (SSA) for heuristic function optimization and proposes a refined B-spline interpolation method for path smoothing. The first major improvement involves enhancing the A* algorithm by optimizing its heuristic function through the SSA. The heuristic function combines Chebyshev distance, Euclidean distance, and obstacle density, with the SSA adjusting the weight parameters to maximize efficiency. The simulation experimental results demonstrate that this modification reduces the number of searched nodes by more than 78.2% and decreases planning time by over 48.1% compared to traditional A* algorithms. The second key contribution is an improved B-spline interpolation method incorporating a two-stage optimization strategy for smoother and safer paths. A corner avoidance strategy first adjusts control points near sharp turns to prevent collisions, followed by a path obstacle avoidance strategy that fine-tunes control point positions to ensure safe distances from obstacles. The simulation experimental results show that the optimized path increases the minimum obstacle distance by 0.2–0.5 units, improves the average distance by over 43.0%, and reduces path curvature by approximately 61.8%. Comparative evaluations across diverse environments confirm the superiority of the proposed method in computational efficiency, path smoothness, and safety. This study presents an effective and robust solution for path planning in complex scenarios. Full article

This article presents an alternative variant of motion planning techniques for autonomous vehicles (AVs) centered on an inverse approach that concurrently optimizes both trajectory and speed. This method emphasizes searching for a trajectory and distributing its speed within a single road segment, regarded as a final element. The references for the road lanes are represented by splines that interpolate the path length, derivative, and curvature using Cartesian coordinates. This approach enables the determination of parameters at the final node of the road segment while varying the reference length. Instead of directly modeling the trajectory and velocity, the second derivatives of curvature and speed are modeled to ensure the continuity of all kinematic parameters, including jerk, at the nodes. A specialized inverse numerical integration procedure based on Gaussian quadrature has been adapted to reproduce the trajectory, speed, and other key parameters, which can be referenced during the motion tracking phase. The method emphasizes incorporating kinematic, dynamic, and physical restrictions into a set of nonlinear constraints that are part of the optimization procedure based on sequential quadratic optimization. The objective function allows for variation in multiple parameters, such as speed, longitudinal and lateral jerks, final time, final angular position, final lateral offset, and distances to obstacles. Additionally, several motion planning variants are calculated simultaneously based on the current vehicle position and the number of lanes available. Graphs depicting trajectories, speeds, accelerations, jerks, and other relevant parameters are presented based on the simulation results. Finally, this article evaluates the efficiency, speed, and quality of the predictions generated by the proposed method. The main quantitative assessment of the results may be associated with computing performance, which corresponds to time costs of 0.5–2.4 s for an average power notebook, depending on optimization settings, desired accuracy, and initial conditions. Full article

To enhance the efficiency and accuracy of detecting insulation faults such as discharge carbon traces in large oil-immersed transformers, this study employs an inspection micro-robot to replace manual inspection for image acquisition and fault identification. While the micro-robot exhibits compactness and agility, its limited battery capacity necessitates the critical optimization of its 3D inspection path within the transformer. To address this challenge, we propose a hybrid algorithmic framework. First, the task of visiting inspection points is formulated as a Constrained Traveling Salesman Problem (CTSP) and solved using the Ant Colony Optimization (ACO) algorithm to generate an initial sequence of inspection nodes. Once the optimal node sequence is determined, detailed path planning between adjacent points is executed through a synergistic combination of the A algorithm*, Rapidly exploring Random Tree (RRT), and Particle Swarm Optimization (PSO). This integrated strategy ensures robust circumvention of complex 3D obstacles while maintaining path efficiency. Simulation results demonstrate that the hybrid algorithm achieves a 52.6% reduction in path length compared to the unoptimized A* algorithm, with the A*-ACO combination exhibiting exceptional stability. Additionally, post-processing via B-spline interpolation yields smooth trajectories, limiting path curvature and torsion to <0.033 and <0.026, respectively. These advancements not only enhance planning efficiency but also provide substantial practical value and robust theoretical support for advancing key technologies in micro-robot inspection systems for oil-immersed transformer maintenance. Full article

To explore the nonlinear dynamics of a piezoelectric energy harvesting device, we consider the simultaneous parametric and external excitations. Based on Bernoulli–Euler beam theory, a new dynamic model is proposed taking into account the curvature of the beam, geometric and electro-mechanical coupling nonlinearities, and damping nonlinearity, with inextensible deformation. The system is discretized by using the Galerkin–Bubnov procedure and then is investigated by the optimal auxiliary functions method. Explicit analytical expressions of the approximate solutions are presented for a complex problem near the primary resonance. The main novelty of our approach relies on the presence of different auxiliary functions, the involvement of a few convergence-control parameters, the construction of the initial and first iteration, and much freedom in selecting the procedure for obtaining the optimal values of the convergence-control parameters. Our procedure proves to be very efficient, simple, easy to implement, and very accurate to solve a complicated nonlinear dynamical system. To study the stability of equilibrium points, the Routh–Hurwitz criterion is adopted. The Hopf and saddle node bifurcations are studied. Global stability is analyzed by the Lyapunov function, La Salle’s invariance principle, and Pontryagin’s principle with respect to the control variables. Full article

This study investigates the dynamic interdependencies among key sectors of Borsa Istanbul—industrial, services, technology, banking, and electricity—using a novel network-geometric framework. Daily closure prices from 2022 to 2024 are transformed into logarithmic returns and analyzed via a sliding window approach. In each window, mutual information is computed to construct weighted networks that are filtered using Triangulated Maximally Filtered Graphs (TMFG) to isolate the most significant links. Forman–Ricci curvature is then calculated at the node level, and entropy measures over k-neighborhoods ($k=1,2,3$) capture the complexity of both local and global network structures. Cross-correlation, Granger causality, and transfer entropy analyses reveal that sector responses to macroeconomic shocks—such as inflation surges, interest rate hikes, and currency depreciation—vary considerably. The services sector emerges as a critical intermediary, transmitting shocks between the banking and both the industrial and technology sectors, while the electricity sector displays robust, stable interconnections. These findings demonstrate that curvature-based metrics capture nuanced network characteristics beyond traditional measures. Future work could incorporate high-frequency data to capture finer interactions and empirically compare curvature metrics with conventional indicators. Full article

Trajectory prediction is a critical component of autonomous driving, intelligent transportation systems, and human–robot interactions, particularly in complex environments like intersections, where diverse road constraints and multi-agent interactions significantly increase the risk of collisions. To address these challenges, a Heterogeneous Risk-Aware Graph Encoder with Continuous Parameterized Decoder for Trajectory Prediction (HRGC) is proposed. The architecture integrates a heterogeneous risk-aware local graph attention encoder, a low-rank temporal transformer, a fusion lane and global interaction encoder layer, and a continuous parameterized decoder. First, a heterogeneous risk-aware edge-enhanced local attention encoder is proposed, which enhances edge features using risk metrics, constructs graph structures through graph optimization and spectral clustering, maps these enhanced edge features to corresponding graph structure indices, and enriches node features with local agent-to-agent attention. Risk-aware edge attention is aggregated to update node features, capturing spatial and collision-aware representations, embedding crucial risk information into agents’ features. Next, the low-rank temporal transformer is employed to reduce computational complexity while preserving accuracy. By modeling agent-to-lane relationships, it captures critical map context, enhancing the understanding of agent behavior. Global interaction further refines node-to-node interactions via attention mechanisms, integrating risk and spatial information for improved trajectory encoding. Finally, a trajectory decoder utilizes the aforementioned encoder to generate control points for continuous parameterized curves. These control points are multiplied by dynamically adjusted basis functions, which are determined by an adaptive knot vector that adjusts based on velocity and curvature. This mechanism ensures precise local control and the superior handling of sharp turns and speed variations, resulting in more accurate real-time predictions in complex scenarios. The HRGC network achieves superior performance on the Argoverse 1 benchmark, outperforming state-of-the-art methods in complex urban intersections. Full article