The cultivation model for spindle-shaped apple trees is widely used in modern standard apple orchards worldwide and represents the direction of modern apple industry development. However, without an effective obstacle avoidance path, the robotic arm is prone to collision with obstacles such as fruit tree branches during the picking process, which may damage fruits and branches and even affect the healthy growth of fruit trees. To address the above issues, a three-dimensional path -planning algorithm for full-field fruit obstacle avoidance harvesting for spindle-shaped fruit trees, which are widely planted in modern apple orchards, is proposed in this study. Firstly, based on three typical tree structures of spindle-shaped apple trees (free spindle, high spindle, and slender spindle), a three-dimensional spatial model of fruit tree branches was established. Secondly, based on the grid environment representation method, an obstacle map of the apple tree model was established. Then, the initial pheromones were improved by non-uniform distribution on the basis of the original ant colony algorithm. Furthermore, the updating rules of pheromones were improved, and a biomimetic optimization mechanism was integrated with the beetle antenna algorithm to improve the speed and stability of path searching. Finally, the planned path was smoothed using a cubic B-spline curve to make the path smoother and avoid unnecessary pauses or turns during the harvesting process of the robotic arm. Based on the proposed improved ACO algorithm (ant colony optimization algorithm), obstacle avoidance 3D path planning simulation experiments were conducted for three types of spindle-shaped apple trees. The results showed that the success rates of obstacle avoidance path planning were higher than 96%, 86%, and 92% for free-spindle-shaped, high-spindle-shaped, and slender-spindle-shaped trees, respectively. Compared with traditional ant colony algorithms, the average planning time was decreased by 49.38%, 46.33%, and 51.03%, respectively. The proposed improved algorithm can effectively achieve three-dimensional path planning for obstacle avoidance picking, thereby providing technical support for the development of intelligent apple picking robots.

Aiming to accurately identify apple targets and achieve segmentation and the extraction of branch and trunk areas of apple trees, providing visual guidance for a picking robot to actively adjust its posture to avoid branch trunks for obstacle avoidance fruit picking, the spindle-shaped fruit trees, which are widely planted in standard modern apple orchards, were focused on, and an algorithm for apple tree fruit detection and branch segmentation for picking robots was proposed based on an improved YOLOv8s model design. Firstly, image data of spindle-shaped fruit trees in modern apple orchards were collected, and annotations of object detection and pixel-level segmentation were conducted on the data. Training set data were then augmented to improve the generalization performance of the apple detection and branch segmentation algorithm. Secondly, the original YOLOv8s network architecture’s design was improved by embedding the SE module visual attention mechanism after the C2f module of the YOLOv8s Backbone network architecture. Finally, the dynamic snake convolution module was embedded into the Neck structure of the YOLOv8s network architecture to better extract feature information of different apple targets and tree branches. The experimental results showed that the proposed improved algorithm can effectively recognize apple targets in images and segment tree branches and trunks. For apple recognition, the precision was 99.6%, the recall was 96.8%, and the mAP value was 98.3%. The mAP value for branch and trunk segmentation was 81.6%. The proposed improved YOLOv8s algorithm design was compared with the original YOLOv8s, YOLOv8n, and YOLOv5s algorithms for the recognition of apple targets and segmentation of tree branches and trunks on test set images. The experimental results showed that compared with the other three algorithms, the proposed algorithm increased the mAP for apple recognition by 1.5%, 2.3%, and 6%, respectively. The mAP for tree branch and trunk segmentation was increased by 3.7%, 15.4%, and 24.4%, respectively. The proposed detection and segmentation algorithm for apple tree fruits, branches, and trunks is of great significance for ensuring the success rate of robot harvesting, which can provide technical support for the development of an intelligent apple harvesting robot.

Deep learning technology can automatically learn features from large amounts of data, with powerful feature extraction and pattern recognition capabilities, thereby improving the accuracy and efficiency of object detection. [The objective of this study]: In order to improve the accuracy and speed of mask wearing deep learning detection models in the post pandemic era, the [Problem this study aimed to resolve] was based on the fact that no research work has been reported on standardized detection models for mask wearing with detecting nose targets specially. [The topic and method of this study]: A mask wearing normalization detection model (towards the wearing style exposing the nose to outside, which is the most obvious characteristic of non-normalized style) based on improved YOLOv5s (You Only Look Once v5s is an object detection network model) was proposed. [The improved method of the proposed model]: The improvement design work of the detection model mainly includes (1) the BottleneckCSP (abbreviation of Bottleneck Cross Stage Partial) module was improved to a BottleneckCSP-MASK (abbreviation of Bottleneck Cross Stage Partial-MASK) module, which was utilized to replace the BottleneckCSP module in the backbone architecture of the original YOLOv5s model, which reduced the weight parameters’ number of the YOLOv5s model while ensuring the feature extraction effect of the bonding fusion module. (2) An SE module was inserted into the proposed improved model, and the bonding fusion layer in the original YOLOv5s model was improved for better extraction of the features of mask and nose targets. [Results and validation]: The experimental results indicated that, towards different people and complex backgrounds, the proposed mask wearing normalization detection model can effectively detect whether people are wearing masks and whether they are wearing masks in a normalized manner. The overall detection accuracy was 99.3% and the average detection speed was 0.014 s/pic. Contrasted with original YOLOv5s, v5m, and v5l models, the detection results for two types of target objects on the test set indicated that the mAP of the improved model increased by 0.5%, 0.49%, and 0.52%, respectively, and the size of the proposed model compressed by 10% compared to original v5s model. The designed model can achieve precise identification for mask wearing behaviors of people, including not wearing a mask, normalized wearing, and wearing a mask non-normalized.

It’s introduced that the structure and function of automatic vertical drilling tool with independent intellectual property rights. The hydraulic driving experimental bench for automatic vertical drilling tool is a key experimental equipment, which is specially developed in the research and development of automatic vertical drilling tool. The experimental bench uses a water circulation system with centrifugal pump. It simulates the change of drilling fluid flow rate by frequency control. It uses an analog turnplate rotating device to simulate the working condition of the tool rotation. It introduces in detail the overall structure of the hydraulic driving experimental bench for automatic vertical drilling tool and the detail structure of the analog turnplate unit, the adjusting mechanism for analog hole deviation angle, and the testing unit for pushing force. The main technical parameters and design difficulties of the experimental bench are given in this paper. It has been proved in practice that the experimental bench can satisfy the functional test of automatic vertical drilling tool system.

This paper optimizes the design of the clamping device of the carbon fiber sucker rod operating vehicle, aiming to improve the shearing problem when the carbon fiber sucker rod enters the clamping device. The structure of the clamping device is designed; Finite element software was used to simulate the state of the clamping device during actual work, the maximum displacement position of the carbon fiber sucker rod and the stress change curve of the sucker rod in the clamping device are obtained; Reasonably adjust the load distribution of the hydraulic cylinder according to the stress change curve of the sucker rod in the clamping device, so that the sucker rod in the clamping device bears a reasonable stress change, which is beneficial to reduce the shear of the sucker rod just entering the clamping device effect. The analysis shows that in the clamping device, the stress of the sucker rod is the largest when it just enters the clamping device. The load of the three hydraulic cylinders of the moving rail gradually increases from bottom to top, which can make the sucker rod bear the force uniformly and can improve the shear problem when the sucker rod just enters the guide rail.