In the process of plug seedling transplantation, the cracking and dropping of seedling substrate or the damage of seedling stems and leaves will affect the survival rate of seedlings after transplantation. Currently, most research focuses on the reduction of substrate loss, while ignoring damage to the hole tray seedling itself. Targeting the problem of high damage rate during transplantation of plug seedlings, we have proposed an adaptive grasp method based on machine vision and deep learning, and designed a lightweight real-time grasp detection network (LRGN). The lightweight network Mobilenet is used as the feature extraction network to reduce the number of parameters of the network. Meanwhile, a dilated refinement module (DRM) is designed to increase the receptive field effectively and capture more contextual information. Further, a pixel-attention-guided fusion module (PAG) and a depth-guided fusion module (DGFM) are proposed to effectively fuse deep and shallow features to extract multi-scale information. Lastly, a mixed attention module (MAM) is proposed to enhance the network’s attention to important grasp features. The experimental results show that the proposed network can reach 98.96% and 98.30% accuracy of grasp detection for the image splitting and object splitting subsets of the Cornell dataset, respectively. The accuracy of grasp detection for the plug seedling grasp dataset is up to 98.83%, and the speed of image detection is up to 113 images/sec, with the number of parameters only 12.67 M. Compared with the comparison network, the proposed network not only has a smaller computational volume and number of parameters, but also significantly improves the accuracy and speed of grasp detection, and the generated grasp results can effectively avoid seedlings, reduce the damage rate in the grasp phase of the plug seedlings, and realize a low-damage grasp, which provides the theoretical basis and method for low-damage transplantation mechanical equipment.

With the increase in the amount of 3D point cloud data and the wide application of point cloud registration in various fields, the question of whether it is possible to quickly extract the key points of registration and perform accurate coarse registration has become a question to be urgently answered. In this paper, we proposed a novel semantic segmentation algorithm that enables the extracted feature point cloud to have a clustering effect for fast registration. First of all, an adaptive technique was proposed to determine the domain radius of a local point. Secondly, the feature intensity of the point is scored through the regional fluctuation coefficient and stationary coefficient calculated by the normal vector, and the high feature region to be registered is preliminarily determined. In the end, FPFH is used to describe the geometric features of the extracted semantic feature point cloud, so as to realize the coarse registration from the local point cloud to the overall point cloud. The results show that the point cloud can be roughly segmented based on the uniqueness of semantic features. The use of a semantic feature point cloud can make the point cloud have a very fast response speed based on the accuracy of coarse registration, almost equal to that of using the original point cloud, which is conducive to the rapid determination of the initial attitude.

As an emerging vegetable cultivation technology, plug seedling cultivation significantly improves seedling production efficiency and reduces costs through standardization. Grading and transplanting, as the final step before the sale of plug seedlings, categorizes seedlings into different grades to ensure consistent quality. However, most current grading methods can only detect seedling emergence but cannot classify the emerged seedlings. Therefore, this study proposes an intelligent grading method for pepper plug seedlings based on RGB and point cloud images, enabling precise grading using both RGB and 3D point cloud data. The proposed method involves the following steps: First, RGB and point cloud images of the seedlings are acquired using 2D and 3D cameras. The point cloud data is then converted into a 2D representation and aligned with the RGB images. Next, a deep learning-based object detection algorithm identifies the positions of individual seedlings in the RGB images. Using these positions, the seedlings are segmented from both the RGB and 2D point cloud images. Subsequently, a deep learning-based leaf recognition algorithm processes the segmented RGB images to determine leaf count, while another deep learning-based algorithm segments the leaves in the 2D point cloud images to extract their spatial information. Their surface area is measured using 3D reconstruction method to calculate leaf area. Additionally, plant height is derived from the point cloud’s height data. Finally, a classification model is trained using these extracted features to establish a grading system. Experimental results demonstrate that this automated grading method achieves a success rate of 97%, and compared with manual methods, this method has higher production efficiency. Meanwhile, it can grade different tray seedlings by training different models and provide reliable technical support for the quality evaluation of seedlings in industrialized transplanting production.

Laser assistance offers a promising pathway for high-efficiency and low-damage ultraprecision grinding for difficult-to-machine hard-brittle semiconductors. This study employs atomistic simulation to investigate the surface removal and subsurface damage mechanisms of C-, M-, and A-plane AlN workpieces during single-grit laser-assisted nanogrinding (LAG). The results indicate that LAG reduces material pileup, thereby decreasing the grit–workpiece contact area and grinding resistance. By leveraging laser-induced thermal effects to enhance atomic plastic flow, LAG evidently achieves a higher material removal rate than conventional grinding (CG). Grinding the C-plane along a orientation yields the lowest surface roughness, although this improvement is not useful for the M- and A-planes. Tangential force increases linearly with grinding depth in both methods, but LAG exhibits a lower rate of increase. LAG consistently produces lower grinding forces and friction coefficients and results in lower dislocation densities in C- and A-plane AlN workpieces at nearly all grinding depths. The C-plane exhibits the thinnest damage layer, followed by the M-plane, with the A-plane the thickest. Increasing the laser power density lowers the grinding force and enhances the removal efficiency. Optimal power density minimizes subsurface damage and improves surface quality; however, excessive power density exacerbates damage. This work provides valuable insights for developing high-efficiency, low-damage LAG techniques for hard-brittle semiconductors.

To solve the issue of inconvenient and dangerous manual operation during the installation and removal of the main pipe plugging plate in the steam generator in nuclear power plants, a ten-degree-of-freedom plugging robot was designed in the present study that includes a collaborative robotic arm coupled with a servo electric cylinder. By establishing a joint coordinate system for the robot model, a D-H parameter model for the plate plugging robot was established, and the forward and inverse kinematics were solved. The volume level approximate convex decomposition algorithm was used to fit the steam generator model with a convex packet, and an experimental simulation platform was constructed. Lastly, path planning was carried out by using the RRT algorithm, with the paths divided into three phases for analysis. The simulation results show that the path in the first stage is relatively smooth, and the parameter changes in each joint are relatively stable. The path in the second stage exhibits zigzagging, with the parameter change curve of joint 9 being particularly evident, and the path in the third stage still exhibits zigzagging—the parameter change curves of joints 3 and 10 are particularly evident. The results of the present study show that, although the paths show a certain degree of zigzagging in complex environments, the plate plugging robot is still able to automatically complete the plate plugging task while avoiding obstacles, which greatly reduces the risk posed to the operator when exposed to a high-radiation environment, in addition to having certain research and application value.

Purpose: In traditional methods, a one-directional distance transformation is employed to eliminate the near-singularity in the radial direction. However, when the projection point of the source point is near the boundary of the integral element, the near-singularity in the circumferential direction still exists, resulting in large errors in the numerical results. The purpose of this paper is to propose a bi-directional distance transformation for the elimination of near-singularities in two directions arising from the irregular integral element.Design/methodology/approach: The sources of the circumferential near-singularity caused by irregular sub-triangular element are analyzed in this paper. A bi-directional distance transformation based on the (a, β) transformation is proposed to eliminate the near-singularities in the two directions. The (a, β) transformation is initially introduced to separate the integral variables and streamline the implementation of subsequent transformations. In the transformed (a, β) coordinate system, a new distance transformation applied in the circumferential direction is constructed.Findings: With the proposed method, the radial and circumferential near-singularities are eliminated using two different distance transformations, respectively. Thus, accurate calculations of the nearly singular integrals can be achieved, irrespective of the shape of the integral element.Originality/value: Numerical examples are presented to calculate the nearly singular integrals of different orders over both the linear integral element and the quadratic integral element. Comparative studies demonstrate that the proposed method significantly improves the accuracy of these calculations.