Robots with visual sensors have been used in various goods logistics, such as bin picking or uploading. However, there are more and more demands for the automatic blanking and loading, and it is necessary to solve the...
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Robots with visual sensors have been used in various goods logistics, such as bin picking or uploading. However, there are more and more demands for the automatic blanking and loading, and it is necessary to solve the problem of object pose estimation in changing accommodation space. This paper proposes a method for pose estimation in the accommodation space using alpha-shape algorithm and improved fruit fly optimization algorithm (FOA). The alpha-shape volume variety of object and measured space is set to the objective function, and the pose variety of object is set to six variables of improved FOA. The experiments were performed by setting parameters of improved FOA and considering the four space types represented the common accommodation shapes. Compared with previous work using convex hull, the new study using alpha-shape algorithm not only keeps the object in the accommodation space, but also maintains the object pose which is at the bottom of the space and can meet the practical requirement of object placement by robot arms.
To predict strain localization behaviors of granular soils, the modified Cam-Clay (MCC) model is incorporated into the second-order cone programming optimized micropolar continuum finite-element method (mpcFEM-SOCP). ...
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To predict strain localization behaviors of granular soils, the modified Cam-Clay (MCC) model is incorporated into the second-order cone programming optimized micropolar continuum finite-element method (mpcFEM-SOCP). Based on a cylindrical cavity expansion problem, a biaxial compression problem, and a rigid strip footing problem, the numerical analyses reveal that the nonphysical strain localization behaviors including mesh-dependency of shear band, rumpling, or bifurcation can be alleviated or even removed if mpcFEM-SOCP is implemented appropriately. Furthermore, the internal characteristic length in mpcFEM-SOCP is a macroscopic physical parameter that characterizes the microscopic response of soil particles and is utilized to model the shear band width. A comparison between mpcFEM-SOCP and discrete element method (DEM) is performed, and the analysis results disclose that the internal characteristic length is closely related to the median particle size, and the evolution trend of the local void ratio in the specimen predicted by mpcFEM-SOCP agrees well with that by DEM. A larger shear dilatancy, however, is generally simulated by the latter. Finally, in the undrained analysis of the rigid footing problem, the evolution curves of excess pore-water pressure predicted by standard finite-element method and mpcFEM-SOCP may differ to some extent, as they enable the observations on the interesting evolution behaviors of excess pore-water pressure.
The alpha-shape (alpha-shape) concept, which has its origin in computational geometry, is usually applied in building boundary extraction from airborne LiDAR data. However, the results depend on the appropriate choice...
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The alpha-shape (alpha-shape) concept, which has its origin in computational geometry, is usually applied in building boundary extraction from airborne LiDAR data. However, the results depend on the appropriate choice of the parameter alpha. Despite several studies in the literature, the adaptive choice of the parameter alpha persists a challenge in boundary extraction, especially when abrupt density variations occur. To overcome this limitation, this letter proposes a new approach combining five estimation strategies. In the proposed method, these strategies are tested sequentially, prioritizing the one that provides greater level of details. The experiments were conducted considering buildings with different characteristics, which were selected from two LiDAR data sets with the average point densities of 12 points/m(2) and 4 points/m(2). The obtained results, presenting F-score. and PoLiS around 98% and 032 m, respectively, indicate the robustness of the proposed method even when abrupt density variation occurs.
Most conventional leaf area index (LAI) retrieval methods using terrestrial laser scanning (TLS) data are based on Beer's law and are severely affected by the effects of leaf occlusion and aggregation. Moreover, t...
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Most conventional leaf area index (LAI) retrieval methods using terrestrial laser scanning (TLS) data are based on Beer's law and are severely affected by the effects of leaf occlusion and aggregation. Moreover, the correction of LAI using the clumping index (CI) relies on assumptions and is generally not robust. This letter exploits the high spatial resolution and penetration capability of TLS to explore the physical meaning of point cloud data sampling and then model the leaf cluster envelope by the alpha-shape algorithm. Subsequently, the canopy LAI is obtained by counting the surface area of the envelope of each leaf cluster within the canopy and combining it with the projected area of the canopy. The entire process is physically based and introduces a new LAI inversion approach based on the TLS. We tested the approach by simulating the TLS data of 25 synthetic trees with different leaf areas and morphologies to evaluate its robustness. Four strategies were adopted for parameter selection in the envelope modeling step to automate the process of finding the optimal envelope radius and improve the inversion accuracy of LAI. In comparison with the traditional LAI retrieval method based on Beer's law (RMSE% is 47.3%), we found that the method proposed in this letter has a higher inversion accuracy with a minimum RMSE% of 27.7%. Our method is also significantly more robust for high LAI scenes and performs well in scenes with high occlusion and aggregation.
The development of new tools based on remote sensing data in agriculture contributes to cost reduction, increased production, and greater profitability. Airborne LiDAR (Light Detection and Ranging) data show a signifi...
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The development of new tools based on remote sensing data in agriculture contributes to cost reduction, increased production, and greater profitability. Airborne LiDAR (Light Detection and Ranging) data show a significant potential for geometrically characterizing tree plantations. This study aims to develop a methodology to extract walnut (Juglans regia L.) crowns under leafless conditions using airborne LiDAR data. An original approach based on the alpha-shape algorithm, identification of local maxima, and k-means algorithms is developed to extract the crowns of walnut trees in a plot located in Viver (Eastern Spain) with 192 trees. In addition, stem diameter and volume, crown diameter, total height, and crown height were estimated from cloud metrics and other 2D parameters such as crown area, and diameter derived from LiDAR data. A correct identification was made of 178 trees (92.7%). For structure parameters, the most accurate results were obtained for crown diameter, stem diameter, and stem volume with coefficient of determination values (R-2) equal to 0.95, 0.87 and 0.83;and RMSE values of 0.43 m (5.70%), 0.02 m (9.35%) and 0.016 m(3) (21.55%), respectively. The models that gave the lowest R-2 values were 0.69 for total height and 0.70 for crown height, with RMSE values of 0.84 m (12.4%) and 0.83 m (14.5%), respectively. A suitable definition of the central and lower parts of tree canopies was observed. Results of this study generate valuable information, which can be applied for improving the management of walnut plantations.
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