Search Results (32)

Off-road path planning involves navigating vehicles through areas lacking established road networks, which is critical for emergency response in disaster events, but is limited by the complex geographical environments in natural conditions. How to model the vehicle’s off-road mobility effectively and represent environments is critical for efficient path planning in off-road environments. This paper proposed an improved A* path planning algorithm based on a Delaunay triangular NavMesh model with off-road environment representation. Firstly, a land cover off-road mobility model is constructed to determine the navigable regions by quantifying the mobility of different geographical factors. This model maps passable areas by considering factors such as slope, elevation, and vegetation density and utilizes morphological operations to minimize mapping noise. Secondly, a Delaunay triangular NavMesh model is established to represent off-road environments. This mesh leverages Delaunay triangulation’s empty circle and maximum-minimum angle properties, which accurately represent irregular obstacles without compromising computational efficiency. Finally, an improved A* path planning algorithm is developed to find the optimal off-road mobility path from a start point to an end point, and identify a path triangle chain with which to calculate the shortest path. The improved road-off path planning A* algorithm proposed in this paper, based on the Delaunay triangulation navigation mesh, uses the Euclidean distance between the midpoint of the input edge and the midpoint of the output edge as the cost function $g\left(n\right)$, and the Euclidean distance between the centroids of the current triangle and the goal as the heuristic function $h\left(n\right)$. Considering that the improved road-off path planning A* algorithm could identify a chain of path triangles for calculating the shortest path, the funnel algorithm was then introduced to transform the path planning problem into a dynamic geometric problem, iteratively approximating the optimal path by maintaining an evolving funnel region, obtaining a shortest path closer to the Euclidean shortest path. Research results indicate that the proposed algorithms yield optimal path-planning results in terms of both time and distance. The navigation mesh-based path planning algorithm saves 5~20% of path length than hexagonal and 8-directional grid algorithms used widely in previous research by using only 1~60% of the original data loading. In general, the path planning algorithm is based on a national-level navigation mesh model, validated at the national scale through four cases representing typical natural and social landscapes in China. Although the algorithms are currently constrained by the limited data accessibility reflecting real-time transportation status, these findings highlight the generalizability and efficiency of the proposed off-road path-planning algorithm, which is useful for path-planning solutions for emergency operations, wilderness adventures, and mineral exploration. Full article

In this study, we analyzed regional spatial features and optimized tourism routes based on point cloud data provided by unmanned aerial vehicles (UAVs) with the goal of developing the Xiaosongyuan Red Tourism Scenic Area in Kunming, Yunnan Province, China. We first proposed a novel method for UAV point cloud data coverage based on an irregular regional segmentation technique along with an optimized search path designed to minimize travel time within the specified area. Three DJI Phantom drones were employed to collect data over the designated region, and an improved progressive triangular irregular network densification filtering algorithm was used to extract ground points from the UAV-acquired point cloud data. DJI Terra software was used for image stitching to generate a comprehensive map of spatial features in the target area. Using this three-dimensional map of spatial features, we explored tourist routes in complex environments and applied an improved particle swarm optimization algorithm to identify optimal tourist routes characterized by safety, smoothness, and feasibility. The findings provide valuable technical support for enhancing tourism planning and management in scenic areas while maintaining a balance with conservation efforts. Full article

Erosion poses a significant threat to infrastructure and ecosystems on coastlines worldwide. Public infrastructure such as US 101—a critical conduit linking coastal communities and renowned destinations—can be costly to maintain due to erosion hazards. Erosion is episodic and varies both spatially and temporarily; hence, forecasting erosion patterns to identify vulnerable infrastructure is immensely challenging. This study presents an innovative Geographic Information Systems (GIS) algorithm to forecast sea cliff erosion progression utilizing imagery datasets (hereafter referred to as ‘rasters’). This approach is demonstrated for an approximately 300 m segment of sea cliffs near Spencer Creek Bridge in Beverly Beach State Park, Oregon, USA. First, Digital Elevation Model (DEM) rasters are created from multiple epochs of terrestrial lidar point clouds using two approaches: Triangular Irregular Networks (TINs) and Empirical Bayesian Kriging (EBK). These DEMs were integrated into a multidimensional raster to generate trend rasters. Based on these trend rasters, forecast DEMs were created based on several different combinations of training and forecast epochs. The forecast DEMs were evaluated against the original lidar data, to calculate residuals to determine optimal model parameters. It was revealed that four combinations warrant particular attention: EBK with harmonic and linear regression of trend rasters, and TIN with harmonic and linear regression of trend rasters. These methods demonstrate consistent decreases in residuals as the number of epochs used for interpolation increases. Under these circumstances, it is expected that the forecasting DEMs will exhibit residuals lower than 10 cm. This outcome is contingent on the condition that the time between the epochs used for prediction and the forecasted epochs does not increase. Full article

Extracting building outlines from 3D models poses significant challenges stemming from the intricate diversity of structures and the complexity of urban scenes. Current techniques heavily rely on human expertise and involve repetitive, labor-intensive manual operations. To address these limitations, this paper presents an innovative automatic technique for accurately extracting building footprints, particularly those with gable and hip roofs, directly from 3D data. Our methodology encompasses several key steps: firstly, we construct a triangulated irregular network (TIN) to capture the intricate geometry of the buildings. Subsequently, we employ 2D indexing and counting grids for efficient data processing and utilize a sophisticated connected component labeling algorithm to precisely identify the extents of the roofs. A single seed point is manually specified to initiate the process, from which we select the triangular facets representing the outer walls of the buildings. Utilizing the projection histogram method, these facets are grouped and processed to extract regular building footprints. Extensive experiments conducted on datasets from Nanjing and Wuhan demonstrate the remarkable accuracy of our approach. With mean intersection over union (mIOU) values of 99.2% and 99.4%, respectively, and F1 scores of 94.3% and 96.7%, our method proves to be both effective and robust in mapping building footprints from 3D real-scene data. This work represents a significant advancement in automating the extraction of building footprints from complex 3D scenes, with potential applications in urban planning, disaster response, and environmental monitoring. Full article

Forests are crucial for biodiversity, climate regulation, and hydrological cycles, requiring sustainable management due to threats like deforestation and climate change. Traditional forest monitoring methods are labor-intensive and limited, whereas UAV LiDAR offers detailed three-dimensional data on forest structure and extensive coverage. This study primarily assesses individual tree segmentation algorithms in two forest ecosystems with different levels of complexity using high-density LiDAR data captured by the Zenmuse L1 sensor on a DJI Matrice 300RTK platform. The processing methodology for LiDAR data includes preliminary preprocessing steps to create Digital Elevation Models, Digital Surface Models, and Canopy Height Models. A comprehensive evaluation of the most effective techniques for classifying ground points in the LiDAR point cloud and deriving accurate models was performed, concluding that the Triangular Irregular Network method is a suitable choice. Subsequently, the segmentation step is applied to enable the analysis of forests at the individual tree level. Segmentation is crucial for monitoring forest health, estimating biomass, and understanding species composition and diversity. However, the selection of the most appropriate segmentation technique remains a hot research topic with a lack of consensus on the optimal approach and metrics to be employed. Therefore, after the review of the state of the art, a comparative assessment of four common segmentation algorithms (Dalponte2016, Silva2016, Watershed, and Li2012) was conducted. Results demonstrated that the Li2012 algorithm, applied to the normalized 3D point cloud, achieved the best performance with an F1-score of 91% and an IoU of 83%. Full article

In South China, the large quantity of rainfall in the pre-summer rainy season can easily lead to natural disasters, which emphasizes the importance of improving the accuracy of precipitation forecasting during this period for the social and economic development of the region. In this paper, the back-propagation neural network (BPNN) is used to establish the model for precipitation forecasting. Three schemes are applied to improve the model performance: (1) predictors are selected based on individual meteorological stations within the region rather than the region as a whole; (2) the triangular irregular network (TIN) is proposed to preprocess the observed precipitation data for input of the BPNN model, while simulated/forecast precipitation is the expected output; and (3) a genetic algorithm is used for the hyperparameter optimization of the BPNN. The first scheme reduces the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the simulation by roughly 5% and more than 15 mm; the second reduces the MAPE and RMSE by more than 15% and 15 mm, respectively, while the third improves the simulation inapparently. Obviously, the second scheme raises the upper limit of the model simulation capability greatly by preprocessing the precipitation data. During the training and validation periods, the MAPE of the improved model can be controlled at approximately 35%. For precipitation hindcasting in the test period, the anomaly rate is less than 50% in only one season, and the highest is 64.5%. According to the anomaly correlation coefficient and Ps score of the hindcast precipitation, the improved model performance is slightly better than the FGOALS-f2 model. Although global climate change makes the predictors more variable, the trend of simulation is almost identical to that of the observed values over the whole period, suggesting that the model is able to capture the general characteristics of climate change. Full article

Wind energy resources in complex terrain are abundant. However, the default mesh division of various terrains often needs more specificity, particularly in wind resource analysis. The mesh division method can diminish computational efficiency and quality in intricate topographical conditions. This article presents a combined algorithm for generating Delaunay triangular meshes in mountainous terrains with significant variations in terrain. The algorithm considers the uncertainty of inner nodes and mesh quality, addressing both the advantages and drawbacks of the Delaunay triangular mesh. The proposed method combines the triangular center of gravity insertion algorithm with an incremental inserting algorithm. Its main goal is to enhance the quality and efficiency of mesh generation, specifically tailored for this type of complex terrain. The process involves discretizing boundary edges and contour lines to obtain point sets, screening boundary triangles, and comparing the triangle area to the average boundary triangle area, combining with the incremental inserting algorithm to generate a triangular mesh of complex terrain. After an initial debugging of the mesh, it is determined whether increasing the internal nodes is necessary to insert the triangle centers of gravity. Upon implementing actual mountainous terrain in the simulation software, a comparison of the resulting meshing demonstrates that the proposed method is highly suitable for complex mountainous terrain with significant variations in elevation. Additionally, it effectively improves the quality of the Delaunay triangular mesh and reduces the occurrence of deformed cells during the meshing process. Full article

In recent decades, wireless sensor networks (WSNs) have become a popular ambient sensing and model-based solution for various applications. WSNs are now achievable due to the developments of micro electro mechanical and semiconductors logic circuits with rising computational power and wireless communication technology. The most difficult issues concerning WSNs are related to their energy consumption. Since communication typically requires a significant amount of energy, there are some techniques/ways to reduce energy consumption during the operation of the sensor’s communication systems. The topology control technique is one such effective method for reducing WSNs’ energy usage. A cluster head (CH) is usually selected using a topology control technique known as clustering to control the entire network. A single factor is inadequate for CH selection. Additionally, with the traditional clustering method, each round exhibits a new batch of head nodes. As a result, when using conventional techniques, nodes decay faster and require more energy. Furthermore, the inceptive energy of nodes, the range between sensor nodes and base stations, the size of data packets, voltage and transmission energy measurements, and other factors linked to sensor nodes are also completely unexpected due to irregular or hazardous natural circumstances. Here, unpredictability represented by Triangular Fuzzy Numbers (TFNs). The associated parameters of nodes were converted into crisp ones via the defuzzification of fuzzy numbers. The fuzzy number has been defuzzified using the well-known signed distance approach. Here, we have employed a multi-criteria decision-making (MCDM) approach to choosing the CHs depending on a bunch of characteristics of each node (i) residual energy, (ii) the number of neighbors, (iii) distance from the sink, (iv) average distance of cluster node, (v) distance ratio, and (vi) reliability. This study used the entropy-weighted Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) approach to select the CH in WSNs. For experiments, we have used the NSG2.1 simulator, and based on six characteristics comprising residual energy, number of neighbor nodes, distance from the sink or base station (BS), average distance of cluster nodes, distance ratio, and reliability, optimal CHs have been selected. Finally, experimental results have been presented and compared graphically with the existing literature. A statistical hypothesis test has also been conducted to verify the results that have been provided. Full article

This study presents a unique method of building a surrogate model using a graph convolutional network (GCN) for mesh-based structure-borne noise analysis of a fluid–structure coupled system. Structure-borne noise generated from irregular shape panel vibration and sound pressure was measured in a closed-volume cavity coupled with the panel. The proposed network was trained to predict the sound pressure level with three steps. The first step is predicting the natural frequency of panels and cavities using the graph convolutional network, the second step is to predict the averaged vibration and acoustic response of the panel and cavity, respectively, in a given excitation condition using a triangular wave-type inference function based on the natural frequency predicted from the first step, and the third step is to predict the sound pressure in a cavity using a panel and cavity average response as an input to a 2D convolutional neural network (CNN). This method is an efficient way to build a surrogate model for predicting the response of a system which consisted of several sub-systems, like a full vehicle system model. We predicted the response of each sub-system and then combined this to obtain the response of the whole system. Using this method, an average 0.86 r-square value was achieved to predict the panel-induced structure-borne noise in a cavity from 10 to 500 Hz range in 1/12 octave band. This study is the first step towards creating a surrogate model of an engineering system with various sub-systems by changing it into a heterogeneous graph. Full article

High-precision ground point cloud data has a wide range of applications in various fields, and the separation of ground points from non-ground points is a crucial preprocessing step. Therefore, designing an efficient, accurate, and stable ground extraction algorithm is highly significant for improving the processing efficiency and analysis accuracy of point cloud data. The study area in this article was a park in Guilin, Guangxi, China. The point cloud was obtained by utilizing the UAV platform. In order to improve the stability and accuracy of the filter algorithm, this article proposed a triangular grid filter based on the Slope Filter, found violation points by the spatial position relationship within each point in the triangulation network, improved KD-Tree-Based Euclidean Clustering, and applied it to the non-ground point extraction. This method is accurate, stable, and achieves the separation of ground points from non-ground points. Firstly, the Slope Filter is used to remove some non-ground points and reduce the error of taking ground points as non-ground points. Secondly, a triangular grid based on the triangular relationship between each point is established, and the violation triangle is determined through the grid; thus, the corresponding violation points are found in the violation triangle. Thirdly, according to the three-point collinear method to extract the regular points, these points are used to extract the regular landmarks by the KD-Tree-Based Euclidean Clustering and Convex Hull Algorithm. Finally, the dispersed points and irregular landmarks are removed by the Clustering Algorithm. In order to confirm the superiority of this algorithm, this article compared the filter effects of various algorithms on the study area and filtered the 15 data samples provided by ISPRS, obtaining an average error of 3.46%. The results show that the algorithm presented in this article has high processing efficiency and accuracy, which can significantly improve the processing efficiency of point cloud data in practical applications. Full article

The pore-throat network of rock exerts a vital influence on the permeability and mechanical properties of the rock. Resorting to X-ray micro-CT scanning, the present work investigates the pore-throat structure of marine biogenic carbonate samples from the South China Sea and compares them to terrigenous sedimentary sandstone. With the help of the maximum ball (MB) algorithm, the pore-throat networks inside representative elementary volumes of rock samples are revealed by stick-and-ball diagrams, which enables quantitative analyses afterwards. Higher and more deviant cross sectional porosity was observed for the carbonate samples compared to the sandstone sample, indicating relatively heterogeneous pores in the carbonate. Over 85% of pores in the carbonate samples were classified as mesopores. Irregular triangular cross sections can be inferred for the pores and throats of the carbonate. The type of rock and the porosity seem to have little effect on the shapes of the pores and throats. In the studied carbonate, the average volume of the throat was approximately one order of magnitude smaller than the average volume of a pore. The distribution of throat radius differed significantly between the studied carbonate samples. The average coordination number of the carbonate was measured to be 1. Full article

In this contribution, interpolation methods were assessed to build the bathymetry of 200 km of the Odra River in South Poland. The River Bed Mapping (RBM) was carried out surveying the depth of several reaches of the canalized part of the river using an Global Navigation Satellite System (GNSS) with an echo sounder as well as two navigation schemes. The values from the interpolation were compared with the data from a classical cross-sectional survey as part of the ISOK (Polish acronym for Information System of Country Protection Against Extraordinary Hazards) project. Two statistical errors between the interpolation values and the ISOK information were estimated, namely, the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). Thanks to the presented analysis, it was possible to compare and analyze which interpolation method fits the best for the batymetric surveying of a shallow river. For this specific case study, the TIN (Triangular Irregular Network) and the NN (Natural Neighbor) methods generates the most accurate RBM. Full article

The elevation of airport runways is specified in the operations manuals and in globally accepted design guidelines. Airport runways are constantly exposed to various physical and weather factors. However, these factors can deteriorate the condition of the runway to the point where it becomes unusable. Monitoring and the continuous inspection of runway evenness is an important element of a sustainable airport maintenance system. An important element of a sustainable airport maintenance system is a runway evenness detection and modelling system. The investigation of the use of various available methods for modelling runway evenness was conducted based on measurements of the actual condition of the existing runway at Edvard Rusjan Airport in Maribor, Slovenia. During the measurements of the runway condition, our own measurement equipment was used, which ensures the geodetic accuracy of the measurements. The novelty of the article is a comparison between five different approaches to modelling runway evenness: approximation with regression plane, inverse distance weighted interpolation (IWD) with a weighting factor of 1, 2, and 10, and interpolation based on a triangulated irregular network (TIN)–linear and cubic. In the methodology section, the advantages and disadvantages of the mentioned methods were described. The selected models were evaluated by required processor time, by the file size resulting from the modelling, and by the values of the descriptive statistics of the model deviation at the average uniform slope. It was found that the modelling method using linear triangular irregular network interpolation provided the most useful results. The results of the conducted analysis can be easily used in any runway management models at airport thet allow for professionally based actions aimed at ensuring the safety and efficiency of runway operations, especially at smaller, regional airports. Full article

Computer simulations of vehicle dynamics become more complex when a vehicle movement takes place on the uneven road surface. In such a case two problems must be solved. The first one concerns a way of road surface modeling, and the second one a way of precisely determining a place of interaction of reaction forces of the road on the vehicle wheels. In this paper triangular irregular networks (TIN) surface was used for modeling surface unevenness, and the author’s algorithm based on the efficient kd-tree data structure was developed for determining a place of an application road surface reaction forces. For calculating the reaction forces including rolling resistance force the Pacejka Magic Formula tire model was used. The solution presented in the paper is computing efficient and for this reason it can be used in the in real-time simulations not only for a vehicle dynamics but for any objects moving over an uneven surface road. Full article

Numerical models are being used for the simulation of recent climate conditions as well as future projections. Due to the complexity of the Earth’s climate system and processes occurring at sub-grid scales, model results often diverge from the observed values. Different methods have been developed to minimize such biases. In the present study, the recently introduced “triangular irregular networks (TIN)-Copula” method was used for the bias correction of modelled monthly total and extreme precipitation in Cyprus. The method was applied to a 15-year historical period and two future periods of the same duration. Precipitation time-series were derived from a 12-km resolution EURO-CORDEX regional climate simulation. The results show that the TIN-Copula method significantly reduces the positive biases between the model results and observations during the historical period of 1986–2000, for both total and extreme precipitation (>80%). However, the level of improvement differs temporally and spatially. For future periods, the model tends to project significantly higher total precipitation rates prior to bias correction, while for extremes the differences are smaller. The adjustments slightly affect the overall climate change signal, which tends to be enhanced after bias correction, especially for total precipitation and for the autumn period. Full article