In agricultural field inspection robots, constructing accurate environmental maps and achieving precise localization are essential for effective Light Detection And Ranging (LiDAR) Simultaneous Localization And Mapping (SLAM) navigation. However, navigating in occluded environments, such as mapping distortion and substantial cumulative errors, presents challenges. Although current filter-based algorithms and graph optimization-based algorithms are exceptionally outstanding, they exhibit a high degree of complexity. This paper aims to investigate precise mapping and localization methods for robots, facilitating accurate LiDAR SLAM navigation in agricultural environments characterized by occlusions. Initially, a LiDAR SLAM point cloud mapping scheme is proposed based on the LiDAR Odometry And Mapping (LOAM) framework, tailored to the operational requirements of the robot. Then, the GNU Image Manipulation Program (GIMP) is employed for map optimization. This approach simplifies the map optimization process for autonomous navigation systems and aids in converting the Costmap. Finally, the Adaptive Monte Carlo Localization (AMCL) method is implemented for the robot’s positioning, using sensor data from the robot. Experimental results highlight that during outdoor navigation tests, when the robot operates at a speed of 1.6 m/s, the average error between the mapped values and actual measurements is 0.205 m. The results demonstrate that our method effectively prevents navigation mapping distortion and facilitates reliable robot positioning in experimental settings.

A photovoltaic (PV)-powered electric motor is used for hose-drawn traveler driving instead of a water turbine to achieve high transmission efficiency. PV power generation (PVPG) is affected by different meteorological conditions, resulting in different power generation of PV panels for a hose-drawn traveler. In the above situation, the hose-drawn traveler may experience deficit power generation. The reasonable determination of the PV panel capacity is crucial. Predicting the PVPG is a prerequisite for the reasonable determination of the PV panel capacity. Therefore, it is essential to develop a method for accurately predicting PVPG. Extreme gradient boosting (XGBoost) is currently an outstanding machine learning model for prediction performance, but its hyperparameters are difficult to set. Thus, the XGBoost model based on particle swarm optimization (PSO-XGBoost) is applied for PV power prediction in this study. The PSO algorithm is introduced to optimize hyperparameters in XGBoost model. The meteorological data are segmented into four seasons to develop tailored prediction models, ensuring accurate prediction of PVPG in four seasons for hose-drawn travelers. The input variables of the models include solar irradiance, time, and ambient temperature. The prediction accuracy and stability of the model is then assessed statistically. The predictive accuracy and stability of PV power prediction by the PSO-XGBoost model are higher compared to the XGBoost model. Finally, application of the PSO-XGBoost model is implemented based on meteorological data.