Despite some rudimentary handling vehicles employed in the labor-intensive harvesting and transportation of greenhouse vegetables, research on intelligent uncrewed transport vehicles remains limited. Herein, an uncrewed transport vehicle was designed for greenhouse solanaceous vegetable harvesting. Its overall structure and path planning were tailored to the greenhouse environment, with specially designed components, including the electric crawler chassis, unloading mechanism, and control system. A SLAM system based on fusion of LiDAR and inertial navigation ensures precise positioning and navigation with the help of an overall path planner using an A* algorithm and a 3D scanning constructed local virtual environment. Multi-sensor fusion localization, path planning, and control enable autonomous operation. Experimental studies demonstrated it can automatically move, pause, steer, and unload along predefined trajectories. The driving capacity and range of electric chassis reach the design specifications, whose walking speeds approach set speeds (<5% error). Under various loads, the vehicle closely follows the target path with very small tracking errors. Initial test points showed high localization accuracy at maximum longitudinal and lateral deviations of 9.5 cm and 6.7 cm, while the average value of the lateral deviation of other points below 5 cm. These findings contribute to the advancement of uncrewed transportation technology and equipment in greenhouse applications.

To address issues in traditional vegetable transplanter planting mechanisms such as poor hole-forming quality and low seedling uprightness, a seven-bar linkage planting mechanism with posture compensation of seedling entering soil was designed. By establishing a mathematical model of the planting mechanism and developing visual auxiliary optimization software, the optimal mechanism parameters for the best planting trajectory were determined. A DEM–MBD (Discrete Element Method and Multibody Dynamics) coupling simulation model of planting mechanism-soil-seedlings was established. The planting frequency, opening width, and opening time of the planter were used as factors, and the soil backflow and seedling uprightness were used as evaluation indicators. A quadratic regression orthogonal rotation combination simulation test was carried out. The regression model was established using Design-Expert 12.0 software to analyze the influence of various factors on the indicators. Response surface methodology was simultaneously applied for comprehensive optimization of the influencing factors. The optimal parameter combination obtained was as follows: planting frequency 57 plants/min, opening width 48.5 mm, opening time 0.76 s, corresponding to a soil backflow of 0.67 and seedling uprightness of 80.35°. Field tests were conducted to verify the following mechanism: the soil backflow was 0.64, and the seedling uprightness was 78.49°, which were 4.47% and 2.31% different from the regression model optimization results, respectively. The error variation was small, indicating that the simulation results were effective and the mechanism design was reasonable. This study provides a reference for the development of high-quality and efficient vegetable transplanters.