Search Results (292)

Traditional algorithms such as Dijkstra and APF rely on complete environmental information for path planning, which results in numerous constraints during modeling. This not only increases the complexity of the algorithms but also reduces the efficiency and reliability of the planning. Swarm intelligence algorithms possess strong data processing and search capabilities, enabling them to efficiently solve path planning problems in different environments and generate approximately optimal paths. However, swarm intelligence algorithms suffer from issues like premature convergence and a tendency to fall into local optima during the search process. Thus, an improved Artificial Bee Colony-Beetle Antennae Search (IABCBAS) algorithm is proposed. Firstly, Tent chaos and non-uniform variation are introduced into the bee algorithm to enhance population diversity and spatial searchability. Secondly, the stochastic reverse learning mechanism and greedy strategy are incorporated into the beetle antennae search algorithm to improve direction-finding ability and the capacity to escape local optima, respectively. Finally, the weights of the two algorithms are adaptively adjusted to balance global search and local refinement. Results of experiments using nine benchmark functions and four comparative algorithms show that the improved algorithm exhibits superior path point search performance and high stability in both high- and low-dimensional environments, as well as in unimodal and multimodal environments. Ablation experiment results indicate that the optimization strategies introduced in the algorithm effectively improve convergence accuracy and speed during path planning. Results of the path planning experiments show that compared with the comparison algorithms, the average path planning distance of the improved algorithm is reduced by 23.83% in the 2D multi-obstacle environment, and the average planning time is shortened by 27.97% in the 3D surface environment. The improvement in path planning efficiency makes this algorithm of certain value in engineering applications. Full article

This study proposes a multi-objective optimization scheduling method for edible oil transportation in smart manufacturing, focusing on centralized control and addressing challenges such as complex pipelines and shared resource constraints. The method employs the A* and Dijkstra pathfinding algorithm to determine the shortest pipeline route for each task, and estimates pipeline resource usage to derive a node cost weight function. Additionally, the transport time is calculated using the Hagen–Poiseuille law by considering the viscosity coefficients of different oil types. To minimize both cost and time, task execution sequences are optimized based on a Pareto front approach. A 3D digital model of the pipeline system was developed using C#, SolidWorks Professional, and the Helix Toolkit V2.24.0 to simulate a realistic production environment. This model is integrated with a 3D visual human–machine interface(HMI) that displays the status of each task before execution and provides real-time scheduling adjustment and decision-making support. Experimental results show that the proposed method improves scheduling efficiency by over 43% across various scenarios, significantly enhancing overall pipeline transport performance. The proposed method is applicable to pipeline scheduling and transportation management in digital factories, contributing to improved operational efficiency and system integration. Full article

Natech events, involving multi-hazard coupling and cascading effects, pose serious threats to coal mine safety. This paper addresses flood-induced Natech scenarios in coal mining and introduces a two-stage cascading analysis framework based on hazard systems theory. A tri-layered network—comprising natural hazards, exposed elements, and secondary hazards—models hazard propagation. In Stage 1, an improved adjacency information entropy algorithm with multi-hazard coupling coefficients identifies critical exposed elements. In Stage 2, Dijkstra’s algorithm extracts key risk transmission paths. A dual-dimensional classification method, based on entropy and transmission risk, is then applied to prioritize emergency responses. This method integrates the criticality of exposed elements with the risk levels associated with secondary disaster propagation paths. Case studies validate the framework, revealing: (1) Hierarchical heterogeneity in the network, with surface facilities and surrounding hydrological systems as central hubs; shaft and tunnel systems and surrounding geological systems are significantly affected by propagation from these core nodes, exhibiting marked instability. (2) Strong risk polarization in secondary hazard propagation, with core-node-originated paths being more efficient and urgent. (3) The entropy-risk classification enables targeted hazard control, improving efficiency. The study proposes chain-breaking strategies for precise, hierarchical, and timely emergency management, enhancing coal mine resilience to flood-induced Natech events. Full article

This study examines the performance characteristics of four commonly used short-path algorithms, including Dijkstra, Bellman–Ford, Floyd–Warshall, and Dantzig, on randomly generated directed graphs. We analyze theoretical computational complexity and empirical execution time using a custom-built testing framework. The experimental results demonstrate significant performance differences across varying graph densities and sizes, with Dijkstra’s algorithm showing superior performance for sparse graphs while Floyd–Warshall and Dantzig provide more consistent performance for dense graphs. Time complexity analysis confirms the theoretical expectations: Dijkstra’s algorithm performs best on sparse graphs with O (E + V log V) complexity, Bellman–Ford shows O (V · E) complexity suitable for graphs with negative edges, while Floyd–Warshall and Dantzig both demonstrate O(V³) complexity that becomes efficient for dense graphs. This research provides practical insights for algorithm selection based on specific graph properties, guiding developers and researchers in choosing the most efficient algorithm for their particular graph structure requirements. Full article

Augmented reality is an educational technology mainly used in disciplines with a strong physical component, such as architecture or engineering. However, its application is much less common in more abstract fields, such as programming and algorithms. Some augmented reality apps for algorithm education exist, but their effect remains insufficiently assessed. In particular, emotions are an important factor for learning, and the emotional impact of augmented reality should be determined. This article inquires about the impact on students’ emotions of an augmented reality tool for learning Dijkstra’s algorithm. This investigation uses an artificial intelligence tool that detects emotions in real time through facial recognition. The data captured with this tool show that students’ positive emotions increased significantly, statistically surpassing negative emotions, and that some negative emotions, such as fear, were considerably reduced. The results show the same trend as those obtained with psychometric questionnaires, but both positive and negative emotions registered with questionnaires were significantly greater than those registered with the artificial intelligence tool. The contribution of this article is twofold. Firstly, it reinforces previous findings on the positive emotional impact of augmented reality on students. Secondly, it shows an alignment of different instruments to measure emotions, but to varying degrees. Full article

StageMix is a video compilation of multiple stage performances of the same song, edited seamlessly together using appropriate editing points. However, generating a StageMix requires specialized editing techniques and is a considerable time-consuming process. To address this challenge, we introduce AutoStageMix, an automated StageMix generation system designed to perform all processes automatically. The system is structured into five principal stages: preprocessing, feature extraction, identifying a transition point, editing path determination, and StageMix generation. The initial stage of the process involves audio analysis to synchronize the sequences across all input videos, followed by frame extraction. After that, the facial features are extracted from each video frame. Next, transition points are identified, which form the basis for face-based transitions, inter-stage cuts, and intra-stage cuts. Subsequently, a cost function is defined to facilitate the creation of cross-edited sequences. The optimal editing path is computed using Dijkstra’s algorithm to minimize the total cost of editing. Finally, the StageMix is generated by applying appropriate editing effects tailored to each transition type, aiming to maximize visual appeal. Experimental results suggest that our method generally achieves lower NME scores than existing StageMix generation approaches across multiple test songs. In a user study with 21 participants, AutoStageMix achieved viewer satisfaction comparable to that of professionally edited StageMixes, with no statistically significant difference between the two. AutoStageMix enables users to produce StageMixes effortlessly and efficiently by eliminating the need for manual editing. Full article

Understanding tortuosity is essential for accurately modeling fluid flow in complex porous media, particularly in the sub-surface reservoir rock; therefore, tortuosity estimation was evaluated using three approaches: Streamline streamline simulations via the Lattice Boltzmann Method (LBM), geometric pathfinding using Dijkstra’s algorithm, and empirical modeling based on pore-structure parameters. The analysis encompassed 1963 micro-Computed Tomography (micro-CT) images of Brazilian pre-salt carbonate and sandstone samples, with the effective porosity extracted from LBM velocity fields, isolating flow-contributing pores, establishing streamline tortuosity as the reference standard. Sandstones exhibited relatively narrow tortuosity ranges (Dijkstra: 1.29–1.75; Streamline: 1.18–2.61; Empirical: 1.18–4.42), whereas carbonates display greater heterogeneity (Dijkstra: 1.00–3.18; Streamline: 1.00–3.68; Empirical: 1.59–4.93). Model performance assessed using the corrected Akaike Information Criterion (AICc) revealed that the best agreement with the data was achieved by the semi-empirical model incorporating coordination number and minimum throat length (AICc = −113.11), followed by the Dijkstra-based geometrical approach (−99.74) and the empirical porosity-based model (202.23). There was a nonlinear inverse correlation between tortuosity and effective porosity across lithologies. This comprehensive comparison underscores the importance of incorporating multiple pore-scale parameters for robust tortuosity prediction, improving the understanding of flow behavior in heterogeneous reservoir rocks. Full article

For the local oscillation phenomenon of the APF algorithm in the face of static U-shaped obstacles, the path cusp phenomenon caused by the vehicle corner and path curvature constraints is not taken into account, as well as the low path safety caused by ignoring the vehicle volume constraints. Therefore, an APF-Dijkstra path planning fusion algorithm based on steering model and volume constraints is proposed to improve it. First, perform an expansion treatment on the obstacles in the map, optimize the search direction of the Dijkstra algorithm and its planned global path, ensuring that the distance between the path and the expanded grid is no less than 1 m, and use the path points as temporary target points for the APF algorithm. Secondly, a Gaussian function is introduced to optimize the potential energy function of the APF algorithm, and the U-shaped obstacle is ellipticized, and a virtual target point is used to provide the gravitational force. Again, the three-point arc method based on the steering model is used to determine the location of the predicted points and to smooth the paths in real time while constraining the steering angle. Finally, a 4.5 m × 2.5 m vehicle rectangle is used instead of the traditional mass points to make the algorithm volumetrically constrained. Meanwhile, a model for detecting vehicle collisions is established to cover the rectangle boundary with 14 envelope circles, and the combined force of the computed mass points is transformed into the combined force of the computed envelope circles to further improve path safety. The algorithm is validated by simulation experiments, and the results show that the fusion algorithm can avoid static U-shaped obstacles and dynamic obstacles well; the curvature change rate of the obstacle avoidance path is 0.248, 0.162, and 0.169, and the curvature standard deviation is 0.16, which verifies the smoothness of the fusion algorithm. Meanwhile, the distances between the obstacles and the center of the rear axle of the vehicle are all higher than 1.60 m, which verifies the safety of the fusion algorithm. Full article

This study examines students’ perceptions of the subjects geography, mathematics, and computer science in integrated science, technology, engineering, and mathematics (STEM) lessons. Although the importance of an integrated approach in STEM education is emphasized, researchers are not clear about whether students perceive connections between the subjects on the one hand and subject-specific working methods and content in integrated lessons on the other. Data was collected in an integrated teaching unit on the sustainability of apples using an open-ended digital questionnaire in to two ninth grade classes in Hamburg, Germany (n = 38); this data was analyzed using qualitative content analysis. The results reveal that students perceive the subjects differently, but similarities can also be identified. While subject-specific content is perceived—such as the use of maps in geography, the calculation of volumes in mathematics, and Dijkstra’s algorithm in computer science—methodological connections, such as calculating, analyzing diagrams, or solving problems, are anchored across disciplines. This suggests that the subject-specific contents are not lost in integrating lessons, and that connections among the subjects are, to a certain extent, promoted. Full article

This study proposes a multi-dimensional urban transportation network optimization framework (MTNO-RQDC) to address structural failure risks from aging infrastructure and regional connectivity bottlenecks. Through dual-dataset validation using both the Baltimore road network and PeMS07 traffic flow data, we first develop a traffic simulation model integrating Dijkstra’s algorithm with capacity-constrained allocation strategies for guiding reconstruction planning for the collapsed Francis Scott Key Bridge. Next, we create a dynamic adaptive public transit optimization model using an entropy weight-TOPSIS decision framework coupled with an improved simulated annealing algorithm (ISA-TS), achieving coordinated suburban–urban network optimization while maintaining 92.3% solution stability under simulated node failure conditions. The framework introduces three key innovations: (1) a dual-layer regional division model combining K-means geographical partitioning with spectral clustering functional zoning; (2) fault-tolerant network topology optimization demonstrated through 1000-epoch Monte Carlo failure simulations; (3) cross-dataset transferability validation showing 15.7% performance variance between Baltimore and PeMS07 environments. Experimental results demonstrate a 28.7% reduction in road network traffic variance (from 42,760 to 32,100), 22.4% improvement in public transit path redundancy, and 30.4–44.6% decrease in regional traffic load variance with minimal costs. Hyperparameter analysis reveals two optimal operational modes: rapid cooling (rate = 0.90) achieves 85% improvement within 50 epochs for emergency response, while slow cooling (rate = 0.99) yields 12.7% superior solutions for long-term planning. The framework establishes a new multi-objective paradigm balancing structural resilience, functional connectivity, and computational robustness for sustainable smart city transportation systems. Full article

Recent advancements in agricultural mobile robots (agribots) have enabled the execution of critical tasks such as crop inspection, precision spraying, and selective harvesting. While agribots show significant potential, conventional path-planning algorithms suffer from three limitations: (1) inadequate dynamic obstacle avoidance, which may compromise operational safety, (2) premature convergence to local optima, and (3) excessive energy consumption due to suboptimal trajectories. To overcome these challenges, this study proposes an enhanced Dynamic Genetic Algorithm—Ant Colony Optimization (DGA-ACO) framework. It integrates a 2D risk-penalty mapping model with dynamic obstacle avoidance mechanisms, improves max–min ant system pheromone allocation through adaptive crossover-mutation operators, and incorporates a hidden Markov model for accurately forecasting obstacle trajectories. A multi-objective fitness function simultaneously optimizes path length, energy efficiency, and safety metrics, while genetic operators prevent algorithmic stagnation. Simulations in different scenarios show that DGA-ACO outperforms Dijkstra, A*, genetic algorithm, ant colony optimization, and other state-of-the-art methods. It achieves shortened path lengths and improved motion smoothness while achieving a certain degree of dynamic obstacle avoidance in the global path-planning process. Full article

Robust indoor robot navigation typically demands either costly sensors or extensive training data. We propose a cost-effective RGB-D navigation pipeline that couples feature-based relative pose estimation with a lightweight multi-layer-perceptron (MLP) policy. RGB-D keyframes extracted from human-driven traversals form nodes of a topological map; edges are added when visual similarity and geometric–kinematic constraints are jointly satisfied. During autonomy, LightGlue features and SVD give six-DoF relative pose to the active keyframe, and the MLP predicts one of four discrete actions. Low visual similarity or detected obstacles trigger graph editing and Dijkstra replanning in real time. Across eight tasks in four Habitat-Sim environments, the agent covered 190.44 m, replanning when required, and consistently stopped within 0.1 m of the goal while running on commodity hardware. An information-theoretic analysis over the Multi-Illumination dataset shows that LightGlue maximizes per-second information gain under lighting changes, motivating its selection. The modular design attains reliable navigation without metric SLAM or large-scale learning, and seamlessly accommodates future perception or policy upgrades. Full article

In the context of coal mine operations, the assurance of work safety relies heavily on efficient autonomous navigation for rescue robots, yet traditional path planning algorithms such as A and RRT exhibit significant deficiencies in a coal mine environment. Traditional path planning algorithms (such as Dijkstra and PRM) have certain deficiencies in dynamic Spaces and narrow environments. For example, the Dijkstra algorithm has A relatively high computational complexity, the PRM algorithm has poor adaptability in real-time obstacle avoidance, and the A* algorithm is prone to generating redundant nodes in complex terrains. In recent years, research on underground mine scenarios has also pointed out that there are many difficulties in the integration of global planning and local planning. This paper proposes an enhanced A* algorithm in conjunction with the Dynamic Window Approach (DWA) to enhance the efficiency, search accuracy, and obstacle avoidance capability of path planning by optimizing the target function and eliminating redundant nodes. This approach enables path smoothing to be performed. In order to ensure that the requirement of multiple target point detection is realized, an RRT algorithm is proposed to reduce the element of randomness and uncertainty in the path planning process, leading to an increase in the convergence rate and overall performance of the algorithm. The solution to the problem of determining the global optimal path is proposed to be simplified by means of the optimal path planning algorithm based on the gradient coordinate rotation method. In this study, we not only focus on the efficiency of mobile robot path planning and real-time dynamic obstacle avoidance capabilities but also pay special attention to the symmetry of the final path. The findings of simulation experiments conducted within the MATLAB environment demonstrate that the proposed algorithm exhibits a substantial enhancement in terms of three key metrics: path planning time, path length, and obstacle avoidance efficiency, when compared with conventional methodologies. This study provides a theoretical foundation for the autonomous navigation of mobile robots in coal mines. Full article

Aiming to address the problems of fewer related studies on autonomous navigation algorithms based on multi-sensor fusion in complex scenarios in pastures, lower degrees of fusion, and insufficient cruising accuracy of the operation path in complex outdoor environments, a multimodal autonomous navigation system is proposed based on a loosely coupled architecture of Cartographer–RTAB-Map (real-time appearance-based mapping). Through laser-vision inertial guidance multi-sensor data fusion, the system achieves high-precision mapping and robust path planning in complex scenes. First, comparing the mainstream laser SLAM algorithms (Hector/Gmapping/Cartographer) through simulation experiments, Cartographer is found to have a significant memory efficiency advantage in large-scale scenarios and is thus chosen as the front-end odometer. Secondly, a two-way position optimization mechanism is innovatively designed: (1) When building the map, Cartographer processes the laser with IMU and odometer data to generate mileage estimations, which provide positioning compensation for RTAB-Map. (2) RTAB-Map fuses the depth camera point cloud and laser data, corrects the global position through visual closed-loop detection, and then uses 2D localization to construct a bimodal environment representation containing a 2D raster map and a 3D point cloud, achieving a complete description of the simulated ranch environment and material morphology and constructing a framework for the navigation algorithm of the pushing robot based on the two types of fused data. During navigation, the combination of RTAB-Map’s global localization and AMCL’s local localization is used to generate a smoother and robust positional attitude by fusing IMU and odometer data through the EKF algorithm. Global path planning is performed using Dijkstra’s algorithm and combined with the TEB (Timed Elastic Band) algorithm for local path planning. Finally, experimental validation is performed in a laboratory-simulated pasture environment. The results indicate that when the RTAB-Map algorithm fuses with the multi-source odometry, its performance is significantly improved in the laboratory-simulated ranch scenario, the maximum absolute value of the error of the map measurement size is narrowed from 24.908 cm to 4.456 cm, the maximum absolute value of the relative error is reduced from 6.227% to 2.025%, and the absolute value of the error at each location is significantly reduced. At the same time, the introduction of multi-source mileage fusion can effectively avoid the phenomenon of large-scale offset or drift in the process of map construction. On this basis, the robot constructs a fusion map containing a simulated pasture environment and material patterns. In the navigation accuracy test experiments, our proposed method reduces the root mean square error (RMSE) coefficient by 1.7% and Std by 2.7% compared with that of RTAB-MAP. The RMSE is reduced by 26.7% and Std by 22.8% compared to that of the AMCL algorithm. On this basis, the robot successfully traverses the six preset points, and the measured X and Y directions and the overall position errors of the six points meet the requirements of the pasture-pushing task. The robot successfully returns to the starting point after completing the task of multi-point navigation, achieving autonomous navigation of the robot. Full article

The growth of air transportation volume and increasing requirements for efficiency require the improvement of algorithms for planning optimal aircraft flight routes. Traditional methods, such as the A*, B*, D* and Dijkstra algorithms, are widely used in navigation systems, but they have a number of limitations when applied in a dynamically changing environment, in particular due to the need to take into account weather conditions, air traffic, economic factors, and aircraft characteristics. This article provides a comprehensive analysis of existing approaches to optimizing airline routes, the advantages and disadvantages of each, and possible areas for their improvement. Particular attention is paid to multi-criteria parameters that affect routing efficiency, such as fuel consumption, safety aspects, forecasting accuracy, and adaptation to changing flight conditions. A methodological solution is proposed to improve route construction algorithms, which involves taking into account variable parameters in real time and integrating them into modern navigation systems. In addition, optimal flight paths were modeled using the improved algorithms, which allow for increasing the efficiency of decision-making in the field of air traffic control. The results of the study can be useful for airline companies, airspace authorities, and navigation software developers. Full article