In this study, a closed multi-channel air-blowing plug seedling pick-up device and a combined plug tray were designed to address the issues of complex structure, high seedling damage rates and low pick-up efficiency in fully automated vegetable transplanter systems. The device operates by sealing the plug seedlings in a seedling cup, where compressed air is channeled into the sealed cavity through multiple passages during the seedling pick-up process. The upper surface of the seedling plug is subjected to uniform force, overcoming the friction and adhesion between the plug seedlings and the tray. This process presses the seedlings into the guide tube, completing the pick-up operation. A mechanical model for the plug seedlings was developed, and the kinetics of the pick-up process were analyzed. The multi-channel high-pressure airflow was simulated and evaluated, identifying three key parameters affecting seedling pick-up performance: water content of the seedling plug, air pressure during pick-up, and air-blowing duration. Using these factors as variables, and with seedling pick-up rate and substrate loss rate as evaluation indicators, single-factor experiments and a three-factor, three-level orthogonal experiment were conducted. The experiments’ results showed that the best seedling pick-up performance was achieved when the water content of the plug was 20%, the air pressure was 0.3 MPa, and the air-blowing time was 30 ms. Under these conditions, the seedling pick-up success rate was 97.22%, and the substrate loss rate was 10.46%.

In pigeon farming, manure cleaning is predominantly manual, a method that is both slow and costly, and exposes workers to harsh conditions. Addressing these challenges, this paper introduces a cleaning robot for pigeon farms utilizing magnetic strip navigation combined with RFID signal recognition and derives the magnetic-navigation control model. This method can improve operational stability and accuracy. Given the farm’s unstable environment, a control algorithm based on fuzzy PID with Kalman filtering was developed. This algorithm mitigates input disturbances and measurement noise by integrating Kalman filtering into the fuzzy PID feedback loop, thereby refining signal accuracy. Numerical simulations conducted in Matlab/Simulink demonstrate that the inclusion of Kalman filtering reduces the time of target signal tracking by nearly 1 s compared to fuzzy PID and by almost 2 s relative to standard PID under identical disturbances. Experimental tests confirm that this algorithm significantly improves the robot’s operational stability and reduces magnetic-navigation deviation, underscoring its advancement and practicality over traditional PID and fuzzy PID methods.

Existing LiDAR super-resolution methods primarily aim to increase point cloud density or improve coordinate reconstruction accuracy. However, they tend to introduce blurred edges and distorted planar surfaces during reconstruction, making it difficult to preserve the consistency of local scene geometry. To address this issue, this paper proposes a structural consistency-aware LiDAR super-resolution method that aims to preserve the local geometric relationships of the reconstructed point cloud with respect to the ground-truth point cloud in edge and planar regions. Specifically, complementary observations from adjacent frames are first fused using multi-scale dilated convolutions. An anisotropic Swin Transformer and a Coordinate-Aware Structure Enhancement (CASE) module are then employed to accommodate the horizontally dense and vertically sparse sampling pattern of LiDAR, strengthen long-range geometric modeling, and reduce the loss of critical structural information. During training, a local curvature-based structural consistency loss is designed to separately constrain edge sharpness and planar smoothness. During inference, prediction uncertainty and point cloud height are combined to adaptively remove low-confidence points, further improving the geometric reliability of the reconstructed point cloud. Experiments on the KITTI dataset show that the proposed method achieves an MAE of 0.4916 and an IoU of 0.4633, outperforming the representative comparison methods on both metrics. When the reconstructed point clouds are applied to A-LOAM, the average RTE and RRE values are reduced by 34.6% and 31.2%, respectively. In addition, experiments on the self-collected CSU-SLAM dataset provide preliminary evidence of the applicability of the proposed method to indoor and outdoor scenes under a different LiDAR configuration.